The Impact of Data Element Agglomeration on Inclusive Green Development: Evidence from Threshold and Spatial Spillover Effects

Abstract

As a production factor, data plays an increasingly important role in sustainable development. Using panel data from 31 Chinese provinces (2011–2023) and employing fixed-effects, panel threshold, and spatial Durbin models, this study examines the impact of data element agglomeration on inclusive green development. The results reveal four main findings. First, data element agglomeration significantly improves inclusive green development, though this positive effect stems primarily from economic growth and social inclusion rather than direct environmental gains. Second, industrial structure upgrading and green technology innovation function as underlying mechanisms, but exhibit suppression effects—their indirect contributions are negative, reflecting short-term adjustment costs and institutional frictions. Third, fiscal support intensity exhibits a nonlinear moderating effect with diminishing returns. Fourth, the effect is more pronounced in coastal provinces, regions with stringent environmental regulation, and the pre-2020 period, and generates significant spatial spillovers to neighboring regions. These findings highlight the conditional nature of data-driven green development and offer insights for designing context-sensitive data policies.

1. Introduction

The UN 2030 Agenda for Sustainable Development explicitly calls for integrated approaches to poverty alleviation with climate action. Globally, however, 831 million people still live in extreme poverty (World Bank (2024) Poverty overview: Development news, research, data https://www.worldbank.org/en/topic/poverty/overview, accessed on 22 January 2026) [1], and carbon emissions are projected to exceed Paris Agreement targets by 16% by 2030 (United Nations Environment Programme (2023) Emissions gap report 2023: Broken record—Temperatures hit new highs, yet world fails to cut emissions (again). UNEP) [2]. This inherent tension—between developmental and ecological rights—is particularly acute in developing countries. As the world’s second-most populous country and largest carbon emitter [3], China has achieved rapid growth through an extensive development model—but at the cost of rising social inequality and ecological degradation [4]. Inclusive green development (IGD) has consequently emerged as a critical pathway for addressing these sustainability challenges.

Inclusive green development integrates and extends the concepts of inclusive growth and green development. Inclusive growth, first articulated by the Asian Development Bank (ADB) in 2007 [5], refers to economic growth whose benefits are broadly shared across society. It emphasizes equitable distribution of development gains, particularly to disadvantaged and marginalized groups, thereby reducing inequality and advancing social equity. Green development emerged as a response to resource depletion and environmental degradation accompanying rapid growth. Constrained by ecological carrying capacity and oriented toward low-carbon circularity, it aims to establish a development paradigm that coordinates economic, social, and ecological systems. The concept of inclusive green development emerged from a global reassessment of traditional growth models and the pursuit of sustainable development goals. It reflects the paradigm shift in development thinking—from a narrow focus on GDP growth to inclusive growth, and ultimately to green development, a vision formally articulated at the 2012 United Nations Conference on Sustainable Development (World Bank (2012). Inclusive Green Growth: The Pathway to Sustainable Development. Washington, D.C.: World Bank. https://openknowledge.worldbank.org/handle/10986/6058, accessed on 22 January 2026) [6].

As a core production factor in the digital era, data—with its inherent attributes of non-rivalry, near-zero marginal cost, high penetrability, and shareability—unlocks transformative pathways for inclusive green growth [7]. In 2024, China’s National Development and Reform Commission, jointly with 16 other government agencies, issued the “Data Factor ×” Three-Year Action Plan (2024–2026). The plan emphasizes leveraging China’s vast data resources to foster multi-scenario applications. Notably, it prioritizes the “Data Factor × Green & Low-Carbon” initiative, providing critical policy support for data-driven inclusive green growth (National Data Bureau of China, Cyberspace Administration of China, Ministry of Science and Technology, et al. (2023). Notice by the National Data Bureau and Other Ministries and Commissions of Issuing the Three-Year Action Plan (2024–2026) for “Data Elements ×” (No. 11 [2023])) [8].

Against this backdrop, data element agglomeration—by promoting both green growth and inclusive development—is essential for China’s high-quality economic transformation. It also offers policy insights for structural transformation in other developing economies. Therefore, this study addresses the following questions: whether and how data elements, as a core production factor in the digital economy era, contribute to inclusive green development. These are the central questions of this study.

Prior research on the determinants of inclusive green development can be categorized into four dimensions. The first is economic factors. Industrialization [9], the shadow economy [10], and outward foreign direct investment [11] tend to hinder inclusive green development, whereas economic complexity [12] exhibits a positive effect. The second dimension comprises policy interventions. China’s National Big Data Comprehensive Pilot Zones [13], vertical fiscal imbalances [14], south-south cross-border e-commerce [15], and China’s low-carbon city pilot policy [16] have significant promoting effects. The third dimension encompasses social factors. Rural infrastructure development [17], new-type urbanization [18], and corporate social responsibility fulfillment [19] contribute positively to inclusive green development. The fourth dimension concerns digitalization [20]. Digital infrastructure [21], inclusive digital finance [22], and financial technology [12] exhibit mixed effects—sometimes promoting, sometimes inhibiting inclusive green development. The inhibitory effects of digitalization on inclusive green development manifest in three key aspects. Regarding economic growth, Chen (2026) [23] argues that digitalization boosts service sector productivity but simultaneously reduces manufacturing productivity through labor reallocation, thereby exacerbating structural imbalances between sectors. From a social inclusion perspective, Ji et al. (2025) [24] show that regional IT penetration has multifaceted effects on common prosperity. It widens urban-rural and regional income disparities, despite the potential opportunities created by digital inclusive finance. It also affects dimensions of opportunity equity—education, healthcare, and social security—differently. Concerning the green dimension, ref. [25] contend that digital infrastructure construction—particularly data centers—entails significant ecological costs. As highly energy- and carbon-intensive facilities, data centers consume substantial energy and water and compete for land resources. These pressures may fundamentally undermine a region’s transition to environmental sustainability. Furthermore, using the “Broadband China” policy as a quasi-natural experiment, Yu et al. (2024) [26] find that digital infrastructure construction increases regional welfare output but also significantly raises industrial wastewater discharge, ultimately diminishing cities’ overall ecological welfare performance. This finding reveals a trade-off between digitalization and green development.

However, two limitations in the literature remain. First, most studies measure data element agglomeration using policy dummy variables, overlooking its potential presence in non-pilot areas. Second, research on digitalization’s impact on inclusive green development typically adopts a geospatial perspective, neglecting the virtual mobility of data.

Given the accelerating global digital transformation and the pursuit of Sustainable Development Goals (SDGs), this study examines the impact of data element agglomeration on inclusive green development across China’s 31 provincial-level regions from 2011 to 2023. A three-stage econometric framework is constructed to empirically test this relationship. Following Pan et al. (2025) [27], a comprehensive evaluation system for data element agglomeration is constructed using indicators across three dimensions: data infrastructure, data transformation capacity, and industrial data applications. The entropy weight method is applied for measurement. Similarly, following Sun & Wang (2025) [28], a comprehensive index system for inclusive green development is constructed using indicators of economic growth, social inclusion, and green ecology. The entropy weight method is again applied for measurement. A two-way fixed effects model is employed to estimate the baseline relationship between data element agglomeration and inclusive green development. Its heterogeneous effects across economic, social, and ecological dimensions are then examined. Data, as a production factor, is inherently non-excludable and highly mobile. It therefore generates spillover effects and enables shared utilization across regions. Following Li et al. (2026) [29], a spatial Durbin model is adopted to test whether data element agglomeration generates spatial spillovers on inclusive green development in neighboring regions. Finally, omitting the mediating roles of industrial upgrading or green technological innovation would overestimate the direct effects of data element agglomeration. Similarly, neglecting threshold effects could obscure heterogeneous impacts across different levels of fiscal support. Accordingly, following Dai et al.(2026) [30], mediation and panel threshold models are employed to identify the transmission channels through which data element agglomeration affects inclusive green development.

The results show that data element agglomeration exerts a dual positive effect. It directly enhances inclusive green development locally while generating spatial spillovers that promote it in neighboring provinces. This finding suggests that the mobility and shareability of data enable it to transcend geographical boundaries, creating synergies for cross-regional inclusive green development. Two mechanisms through which data element agglomeration facilitates inclusive green development are identified: industrial structure upgrading and green technology innovation. Through industrial upgrading, data enable the low-carbon transformation of traditional industries. By accelerating green technology innovation, they speed up the iteration and application of green technologies, reducing environmental harm while generating economic returns and productivity gains. Fiscal support intensity exhibits a threshold effect, with its marginal impact diminishing beyond a certain point. At early stages of data element agglomeration, fiscal investment significantly enhances inclusive green development. Beyond a certain threshold, however, its marginal contribution diminishes. This implies that fiscal resources should be precisely allocated and support mechanisms strategically optimized. Moreover, the effect is more pronounced in coastal provinces, regions with stringent environmental regulations, and areas with high data element market maturity.

This paper contributes to the literature in several ways. First, it examines the effects of data element agglomeration on inclusive green development and explores its underlying mechanisms, deepening the understanding of inclusive green development. Second, it tests not only the overall impact but also the heterogeneous effects across the economic, social, and ecological dimensions, offering corresponding policy recommendations. Third, from the perspective of digital infrastructure connectivity, it empirically investigates the spatial spillover effects of data element agglomeration on inclusive green development.

The paper proceeds as follows. Section 2 presents the theoretical analysis. Section 3 describes the model specification, variable selection, and data sources. Section 4 reports the empirical results. Section 5 discusses the findings. Section 6 concludes with policy implications.

2. Theoretical Hypotheses and Analysis

2.1. The Direct Effect of Data Element Agglomeration on Inclusive Green Development

2.1.1. The Impact of Data Element Agglomeration on the Aggregate Dimension of Inclusive Green Development

The concept of data element agglomeration draws on the classical economic notion of agglomeration but requires clarification of its fundamental differences from traditional factor agglomeration. Classical agglomeration theory, originating from Marshall’s [31] observations of industrial districts and later formalized by Krugman and others [32], established an analytical paradigm centered on geographical concentration. Within this paradigm, firms benefit from three types of externalities—labor pooling, input sharing, and knowledge spillovers—through spatial proximity [33]. These externalities presuppose that traditional factors are rivalrous and subject to transport costs. However, the fundamental characteristics of data—non-rivalry, near-zero marginal replication cost, and network externalities—necessitate a re-examination of the meaning of agglomeration. Non-rivalry implies that the same data can be used simultaneously by multiple agents without value depletion. Combined with near-zero transmission costs, this implies that data element agglomeration need not rely on physical proximity.

Despite these fundamental differences, this study retains the term agglomeration for two reasons. First, from a functional perspective, both concepts focus on how the configuration of production factors generates external economies—whether through geographical proximity or network connectivity. The three dimensions in our framework—data infrastructure, data transformation capacity, and industrial data applications—functionally parallel the classic agglomeration mechanisms of sharing, matching, and learning, but are realized through digital networks rather than physical co-location. Second, from a theoretical dialogue perspective, retaining the term fosters integration between digital economics and established economic geography, positioning data element agglomeration as an extension of, rather than a break from, the classical tradition. Recent research on data element agglomeration supports this terminological choice [34], demonstrating that agglomeration economies can operate through information flows across space.

As a core production factor in the digital economy, data differs fundamentally from traditional factors—land, labor, and capital. These distinctions confer distinct advantages for fostering inclusive green development. Traditional factors are rivalrous and excludable; their allocation is constrained by physical scarcity and spatial limitations. Capital tends to crowd out green investment, given its procyclical bias toward carbon-intensive sectors. Labor faces skill mismatches in transitioning to environmental industries. Land competition exacerbates ecological conflicts, often manifesting as tragedies of the commons. Data’s inherent attributes—non-rivalry, near-zero marginal cost, pervasiveness, and shareability—enable it to transcend the zero-sum game inherent in traditional resource allocation [35]. According to techno-economic paradigm theory, a new factor can drive a paradigm shift when its core features systematically address the bottlenecks of the existing development model. In the context of IGD, data element agglomeration does not simply add more of the same; it reconfigures resource allocation, opening new possibilities for balancing efficiency, equity, and ecological sustainability.

H1. 

Data element agglomeration significantly promotes inclusive green development.

2.1.2. The Effect of Data Element Agglomeration on the Sub-Dimensions of Inclusive Green Development

Inclusive green development aims to optimize resource allocation, enhance regional coordination, and achieve green economic transformation. Data element agglomeration breaks information barriers and optimizes resource allocation. It simultaneously drives economic growth alongside environmental protection while improving equity and inclusiveness [36], thereby enabling sustainable development.

By harnessing increasing returns to scale, data element agglomeration compensates for the diminishing marginal returns inherent in traditional growth models that rely on finite material resources. At the micro level, firms leverage big data and AI to optimize production processes and supply chains. This enables precise demand forecasting and just-in-time production, minimizing overcapacity and reducing material and energy waste [37]. These efficiency gains lower the resource intensity of economic activities. At the meso level, data flows facilitate the integration of digital technologies with traditional industries, fostering green business models such as circular economy platforms. By enabling cross-industry tracking of material flows, data helps transform waste from one sector into inputs for another, supporting industrial upgrading that is both economically productive and environmentally sound. At the macro level, data-driven innovation shifts the primary growth driver from factor accumulation to efficiency gains. This decouples economic expansion from resource depletion, laying the foundation for a growth trajectory consistent with long-term ecological sustainability.

Traditional development models often suffer from “trickle-down” failures, leaving geographically disadvantaged populations with limited access to quality public services. The shareability of data enables education and healthcare resources to be disseminated at near-zero marginal cost to remote areas, weakening the link between geographic location and service access. This promotes a more equitable distribution of developmental opportunities. Digital platforms, powered by data aggregation, create flexible employment opportunities that absorb groups traditionally marginalized in labor markets [38]. These platforms lower entry barriers [39]. By enabling broader participation in public discourse and cultural exchange, data aggregation helps bridge social divides. Digital government platforms lower participatory barriers, enabling citizens—even those in remote areas or with limited mobility—to access public information and engage in decision-making [40].

Environmental governance has long been hampered by information asymmetry, creating regulatory blind spots. Data element agglomeration addresses this challenge by enabling real-time monitoring of atmospheric, aquatic, and soil indicators through IoT sensor networks and satellite imagery. This granular visibility allows regulators to pinpoint pollution sources and implement targeted countermeasures. Industrial production data tracing enhances solid waste recovery rates [41]. Agricultural soil moisture and irrigation monitoring facilitates precise water-fertilizer management, curbing resource waste and non-point pollution at the source. Environmental problems such as river basin pollution and airshed degradation often transcend administrative boundaries, yet governance remains fragmented by jurisdiction. Data element agglomeration overcomes this fragmentation. By integrating environmental data from disparate regions and sectors, centralized governance platforms enable cross-jurisdictional coordination in watershed management and air quality control. Converged ecological and market transaction data monetize natural capital, operationalizing the “green mountains as gold mountains” paradigm [42].

H2. 

Data element agglomeration exerts significant positive effects on economic growth, social inclusion, and green ecology.

2.2. The Mechanism Effect of Data Element Agglomeration on Inclusive Green Development

2.2.1. The Mechanism Effect of Industrial Structure Upgrading

Industrial structure upgrading refers to the reallocation of productive factors from lower- to higher-efficiency sectors, accompanied by a shift toward higher value-added activities [43]. Data—characterized by non-rivalry and low replication costs—play a catalytic role in this process. At the micro level, firms leverage multidimensional data analytics—spanning production, sales, and supply chains—to capture market signals, accelerate innovation cycles, and enhance value-added competitiveness [44]. At the meso level, data element agglomeration fosters cross-industry linkages and refines specialization along value chains. These dynamics enhance systemic resilience to external shocks. Moreover, data element agglomeration gives rise to platform-based business models, representing a structural shift toward technology-intensive sectors and generating new employment opportunities [44]. At the macro level, well-developed data markets perform a dual function: modernizing established industries and accelerating the growth of emerging ones [37]. This two-pronged advancement raises total factor productivity through improved resource allocation, steering the economy toward a knowledge-intensive trajectory. In addition, data element agglomeration contributes to lower energy intensity and reduced pollutant emissions per unit of GDP. This helps decouple economic expansion from environmental pressure, aligning growth with sustainability objectives. At the same time, data-enabled capabilities enhance the international competitiveness of Chinese firms in high-end manufacturing and digital services, supporting their ascent along global value chains. However, the relationship between industrial structure upgrading and inclusive green development may not be linear or uniformly positive, particularly in the short run. The transition entails adjustment costs—phasing out traditional industries, retiring outdated equipment, and substantial digital infrastructure investment [45]—that may temporarily dampen the positive effects of data element agglomeration on inclusive green development. Moreover, whether industrial upgrading yields inclusive outcomes depends on institutional quality, labor market flexibility, and complementary policies [46]. Consequently, the impact of data element agglomeration through this pathway may exhibit complex patterns—including possible short-term suppression effects—consistent with the innovation literature documenting J-curve dynamics in green transitions.

H3. 

Data element agglomeration promotes industrial structure upgrading, but this pathway may exhibit complex effects on inclusive green development due to transitional frictions.

2.2.2. The Mechanism Effect of Green Technology Innovation

Green technology innovation encompasses research and development (R&D), process optimization, and novel business models aimed at reducing resource consumption and mitigating environmental pollution [47]. Its objective is to advance sustainable development by reconciling economic, social, and environmental goals. Data play a foundational role in this process. First, data analytics help research institutions and firms identify technological breakthroughs while reducing trial-and-error costs. The sharing of R&D data via platforms lowers entry barriers, enabling small and medium-sized enterprises (SMEs) and underdeveloped regions to access technical resources and narrow innovation gaps. Second, industrial internet platforms illustrate this mechanism. These platforms collect real-time data on energy consumption and emissions from manufacturing processes, relaying it directly to R&D units. This feedback ensures that fundamental research addresses concrete industrial needs [48]. Data platforms also serve an intermediation function. By aggregating technical patents and business cases, they attract private capital into research ventures. Meanwhile, the analysis of consumer behavior data allows firms to dynamically refine green product designs, enhancing price competitiveness and accelerating market uptake. In resource utilization, green technologies—through clean energy substitution, circular regeneration, and precision control—overcome the constraints of traditional extensive development, substantially improving efficiency. In pollution control, they represent a paradigm shift: from end-of-pipe treatment to source prevention. This transition shifts environmental governance from diminishing returns to increasing returns, aligning economic incentives with ecological outcomes. Nevertheless, the positive effect of green technology innovation on inclusive green development may not materialize immediately. Green innovation requires substantial upfront investment, while its benefits—reduced clean energy costs, new green jobs, and improved environmental quality—accrue over a longer horizon [49]. This temporal mismatch may produce a short-term negative correlation between green innovation and inclusive green development, consistent with the J-curve effect documented in environmental economics [50]. Moreover, in regions with weak institutional quality or narrow industrial bases, green innovation may crowd out more immediately productive uses or fail to integrate with local economic structures, dampening its inclusive effects.

H4. 

Data element agglomeration promotes green technology innovation, but this pathway may exhibit complex effects on inclusive green development due to time lags and adjustment costs.

2.3. Threshold Effects of Data Element Agglomeration on Inclusive Green Development

Fiscal support intensity denotes the extent to which governments allocate resources through instruments such as expenditure, tax incentives, subsidies, and public procurement, often targeting specific sectors [51]. Fiscal support acts as a stabilizer, cushioning economic fluctuations and preventing market deterioration. Through tax incentives and transfer payments, it helps adjust wealth distribution and narrow regional disparities [52]. However, excessive reliance on fiscal spending carries risks. It may crowd out private investment and distort market competition. It can also foster rent-seeking behavior, leading to misallocation of public resources [53]. Fiscal support exhibits a threshold effect in moderating how data element agglomeration affects inclusive green development [54]. At low to moderate levels, fiscal intervention helps correct market failures and reduce transaction costs in data element flows. This facilitates effective data element agglomeration, which in turn enhances green total factor productivity through scale effects. These productivity gains trigger technology diffusion, industrial synergies, and improvements in environmental governance—mechanisms that collectively advance inclusive green development [55]. As fiscal support intensifies, however, the infrastructure and institutional frameworks for data element agglomeration mature. Marginal returns on additional fiscal inputs may diminish, attenuating the positive impact of data element agglomeration. Beyond a certain threshold, further increases in fiscal investment risk crowding out private capital in data-related sectors and inducing rent-seeking behavior. Such outcomes suppress innovation incentives among market actors and distort resource allocation away from actual needs. Consequently, the contribution of fiscal support to inclusive green development may exhibit diminishing marginal returns—or even turn negative.

H5. 

Fiscal support exhibits a threshold effect in moderating the impact of data element agglomeration on inclusive green development.

3. Research Design

3.1. Model Construction

3.1.1. Two-Way Fixed Effects Model

To examine the impact of data element agglomeration on inclusive green development, the following model is specified:

$${\mathrm{IGD}}_{\mathrm{it}} = {\mathsf{\beta }}_{0 }+ {{\mathsf{\beta }}_1\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}} +{ \mathsf{\beta }}_2{\mathrm{control}}_{\mathrm{it}} + {\mathsf{\mu }}_{\mathrm{i}} +{ \mathsf{\lambda }}_{\mathrm{t}} + {\mathsf{\epsilon }}_{\mathrm{it}}$$

(1)

where ${\mathrm{IGD}}_{\mathrm{it}}$ denotes inclusive green development for province i in year t, encompassing economic growth, social inclusion, and ecological sustainability.${ \mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}}$ denotes data element agglomeration. ${\mathrm{control}}_{\mathrm{it}}$ is a series of control variables.${\mathsf{\mu }}_{\mathrm{i}}$ and ${\mathsf{\lambda }}_{\mathrm{t}}$ represent provincial and time fixed effects, respectively.${\mathsf{\epsilon }}_{\mathrm{it}}$ is the random disturbance term. $\mathsf{\beta }$ denotes the coefficients to be estimated.

3.1.2. Mechanism Effect Model

To examine the mediating effects of data element agglomeration on inclusive green development, Models (2) and (3) are specified:

$${\mathrm{IGD}}_{\mathrm{it}} ={ \mathsf{\beta }}_0 + {{\mathsf{\beta }}_1\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}} + {{\mathsf{\beta }}_2\mathrm{control}}_{\mathrm{it} }+ {\mathsf{\mu }}_{\mathrm{i} }+ {\mathsf{\lambda }}_{\mathrm{t} }+ {\mathsf{\epsilon }}_{\mathrm{it}}$$

(2)

$${\mathrm{M}}_{\mathrm{it}}={\mathsf{\alpha }}_{0 }+{{\mathsf{\alpha }}_1\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{control}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{i} }+{\mathsf{\lambda }}_{\mathrm{t} }+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(3)

where ${\mathrm{M}}_{\mathrm{it}}$ denotes the mechanism variables, specifically industrial structure upgrading and green technology innovation. ${\mathsf{\alpha }}_1$ captures the effect of data element agglomeration on the mechanism variables. All other variables are defined as in Model (1).

3.1.3. Panel Threshold Regression Model

To examine the threshold effect of fiscal support intensity, a panel threshold regression model is specified as follows:

$${\mathrm{IGD}}_{\mathrm{it}} ={ \mathsf{\beta }}_0 + {\mathsf{\beta }}_1{\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}} \times \mathrm{I}({\mathrm{Q}}_{\mathrm{it}} \le { \mathsf{\gamma }}_1) +{ \mathsf{\beta }}_2{\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}} \times \mathrm{I}({{\mathsf{\gamma }}_1 < \mathrm{Q}}_{\mathrm{it}} \le { \mathsf{\gamma }}_2) + {\mathsf{\beta }}_3{\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{it}} \times \mathrm{I}({\mathrm{Q}}_{\mathrm{it}} >{ \mathsf{\gamma }}_2) + {\mathsf{\beta }}_4{\mathrm{control}}_{\mathrm{it}} + {\mathsf{\mu }}_{\mathrm{i}} + {\mathsf{\lambda }}_{\mathrm{t}} + {\mathsf{\epsilon }}_{\mathrm{it}}$$

(4)

Here, ${\mathsf{\gamma }}_1 \mathrm{and}{ \mathsf{\gamma }}_2$ are the estimated threshold parameters, $\mathrm{I}(·)$ is the indicator function, and ${\mathrm{Q}}_{\mathrm{it}}$ is the threshold variable—fiscal support intensity. All other variables are defined as in Model (1).

3.1.4. Spatial Durbin Model

To examine the spatial spillover effects of data element agglomeration on inclusive green development, a spatial weight matrix is first required. Given that cross-regional spillovers of data element agglomeration depend primarily on digital infrastructure connectivity, the spatial weight matrix is constructed using the inverse of the absolute difference in inter-provincial long-distance optical cable lengths (LDOCLs). Long-distance optical cables—unlike local cables—form the backbone of cross-regional digital connectivity; their length determines inter-regional data transmission capacity. The spatial weight matrix is constructed as follows:

$${\mathrm{W}}_{{\mathrm{e}}_{\mathrm{i}\mathrm{j}}}=\left\{\begin{array}{ll}{\displaystyle \frac{1}{\left|{\mathrm{l}\mathrm{d}\mathrm{o}\mathrm{c}\mathrm{l}}_{\mathrm{i}}-{\mathrm{l}\mathrm{d}\mathrm{o}\mathrm{c}\mathrm{l}}_{\mathrm{j}}\right|}},& \mathrm{i}\ne \mathrm{j}\\ 0,& \mathrm{i}=\mathrm{j}\end{array}\right.$$

(5)

After model specification tests, a two-way fixed-effects spatial Durbin model (SDM) is employed for estimation. Model (6) is specified as follows:

$${\mathrm{I}\mathrm{G}\mathrm{D}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+\mathsf{\rho }{\sum }_{\mathrm{j}=1,\mathrm{i}\ne \mathrm{j}}^{31}{\mathrm{W}}_{{\mathrm{e}}_{\mathrm{i}\mathrm{j}}}{\mathrm{I}\mathrm{G}\mathrm{D}}_{\mathrm{j}\mathrm{t}}+{\mathsf{\beta }}_1{\mathrm{D}\mathrm{E}\mathrm{A}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{control}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(6)

3.2. Variables Design and Selection

3.2.1. Dependent Variable: Inclusive Green Development

Inclusive green development is defined as a development pathway that achieves synergistic progress in economic growth efficiency and social distribution equity under the rigid constraints of ecological carrying capacity. Specifically, it is a pathway in which economic growth provides the impetus, social inclusion ensures equity, and green ecological sustainability lays the foundation for long-term development [56]. This conceptualization comprises three core dimensions. Economic growth entails a shift from high-pollution, high-energy-consumption models toward higher-quality growth under green ecological constraints [57]. Social inclusion means that the benefits of growth are broadly shared, with particular attention to the equitable participation of vulnerable groups and less-developed regions. Green ecology implies a commitment to low-carbon, circular, and resilient development, with ecological carrying capacity as a rigid constraint. Following Sun and Wang (2025) [28], representative secondary indicators are selected from the three dimensions—economic growth, social inclusion, and green ecology—to measure inclusive green development. Scores are calculated using the entropy weight method and reported in

Table 1. Indicator System for Inclusive Green Development Level.

Secondary Indicators	Variable Description	Indicator Definition	Attribute
Economic Growth	Regional Economic Efficiency	GDP per capita	Positive
	Level of Industrialization	Proportion of secondary industry	Negative
	Development Level of Service Sector	Proportion of tertiary industry	Positive
	Household Consumption Level	Per capita household consumption expenditure	Positive
	Urban Residents’ Purchasing Power	Urban disposable income per capita	Positive
	Rural Residents’ Purchasing Power	Rural disposable income per capita	Positive
Social Inclusion	Medical Resources	Number of urban healthcare beds	Positive
	Transport Capacity	Road area per capita	Positive
	Education Investment	Share of education expenditure	Positive
	Medical Insurance Coverage	Urban basic medical insurance enrollees	Positive
	Pension Insurance Coverage	Basic pension insurance enrollees	Positive
	Unemployment Insurance Coverage	Unemployment insurance enrollees	Positive
Green Ecology	Residential Environmental Governance	Harmless treatment rate of household waste	Positive
	Industrial Emissions Intensity	Sulfur dioxide emissions	Negative
	Industrial Emissions Intensity	Smoke (dust) emissions	Negative

. Results for the stability of the entropy weights are provided in Appendix D.

3.2.2. Independent Variable: Data Element Agglomeration

Data element agglomeration is conceptualized as the process by which raw data are mobilized, integrated, and valorized through specific carriers or platforms, ultimately achieving optimal economic value via market-based allocation [58]. Data element agglomeration is characterized along three dimensions. Data infrastructure support captures the physical prerequisites and foundational infrastructure for data element possession and utilization within a region—the “hard conditions” for agglomeration [59]. Data transformation capability measures the capacity to convert raw data into economic and social value—the “soft capability” of data element agglomeration. Industry-specific data applications denote the depth of data element integration and application within specific economic and social domains—the “ultimate manifestation” of data element agglomeration [60]. Following Pan et al. (2025) [27], the level of data element agglomeration is measured across three dimensions: data infrastructure support, data transformation capability, and industry-specific data applications. Scores are calculated using the entropy weight method and reported in

Table 2. Indicator System for Data Element Agglomeration Level.

Secondary Indicators	Variable Description	Indicator Definition	Attribute
Data Infrastructure Support	Network Connectivity	Number of Broadband Internet Subscribers	Positive
	Network Connectivity	Number of Registered Domain Names	Positive
	Internet Usage	Number of Web Pages	Positive
	Network Foundation	Number of IPv4 Addresses	Positive
	Enterprise IT Infrastructure	Number of Websites per 100 Enterprises	Positive
Data Transformation Capability	Data Management Capacity	Revenue from Software Products	Positive
	Data Application Capacity	Revenue from IT Services	Positive
	Data Innovation Capacity	Transaction Volume in Technology Markets	Positive
Industry-Specific Data Applications	Commercial Applications	E-commerce Procurement Volume	Positive
	Commercial Applications	E-commerce Sales Volume	Positive
	Enterprise Applications	Proportion of Enterprises Engaging in E-commerce	Positive
	Consumer Applications	Digital Inclusive Finance: Digital Payments	Positive
	Financial Applications	Digital Inclusive Finance: Digital Insurance	Positive
	Social & Livelihood Applications	Digital Inclusive Finance: Digitalization Level	Positive

. Results for the stability of the entropy weights are provided in Appendix D.

3.2.3. Control Variables

To mitigate omitted variable bias, the following control variables are included in the empirical model: urbanization rate, trade openness, human capital, social consumption level, and financial development level. Variable definitions are provided in

Table 3. Descriptive Statistical Results.

Variable Type	Variable Name	Symbol	Measurement	Obs.	Mean	Std. Dev.
Dependent Variable	Inclusive green development	IGD	Calculated by entropy weight method	403	0.32	0.13
Independent Variable	Data element agglomeration	DEA	Calculated by entropy weight method	403	0.11	0.11
Control Variables	Urbanization rate	Urban	Urban population/Total population	403	59.16	12.96
	Trade openness	Trade	(Total import & export × USD-CNY rate)/GDP	403	0.26	0.27
	Human capital	HC	Retail sales of consumer goods/GDP	403	0.02	0.00
	Social consumption level	Consump	Retail sales of consumer goods/GDP	403	0.37	0.06
	Financial development	FinDev	(Total deposits + loans)/GDP	403	3.54	1.13
Mediating Variables	Industrial structure upgrading	IndStr	Value-added of tertiary sector/secondary sector	403	1.38	0.75
	Green technology innovation	GreenTech	Green patent applications per 10,000 population	403	1.48	2.03
Threshold Variable	Fiscal support intensity	FisSup	General budget expenditure/GDP	403	0.28	0.20

.

3.2.4. Mechanism Variables

Following Du et al. (2019) [61], the mechanism effects of data element agglomeration on inclusive green development are examined from two perspectives: industrial structure upgrading and green technology innovation. Variable definitions are provided in Table 3. A detailed explanation of the economic meaning of the green technology progress variable is provided in Appendix C.1.

3.2.5. Threshold Variable

Following Nyasha & Odhiambo (2019) [62], fiscal support intensity is adopted as the threshold variable. Variable definitions are provided in Table 3.

3.3. Data Sources

Based on data availability and consistency, the study sample consists of China’s 31 provinces, municipalities, and autonomous regions (excluding Hong Kong, Macao, and Taiwan) over the period 2011–2023. The Digital Inclusive Finance Index are obtained from the Peking University Digital Inclusive Finance Index (2011–2023). Green technology innovation data are obtained from the China National Research Data Service (CNRDS). All other variables are obtained from the China Statistical Yearbook, provincial and municipal statistical yearbooks, and the EPS database.

4. Empirical Results

To check for multicollinearity, the variance inflation factor (VIF) is calculated for all explanatory and control variables. The mean VIF is 2.340, well below the conventional threshold of 10, indicating no severe multicollinearity and thus confirming the reliability of the regression estimates.

4.1. Baseline Regression Analysis

Table 4. Baseline Regression Results.

Variable	(1)	(2)	(3)	(4)	(5)
	IGD	IGD	Economic Growth	Social Inclusion	Green Ecology
DEA	0.225 ***	0.452 ***	0.242 ***	0.600 ***	0.077
	(8.12)	(14.68)	(9.52)	(13.79)	(0.97)
Urban		0.004 ***	−0.008 ***	0.011 ***	0.011 ***
		(4.83)	(−10.12)	(8.59)	(4.54)
Trade		0.082 ***	0.095 ***	0.071 **	0.128 **
		(3.78)	(5.33)	(2.32)	(2.29)
HC		5.256 ***	0.443	8.113 ***	4.834 **
		(5.81)	(0.59)	(6.35)	(2.07)
Consump		0.030	0.085 ***	0.027	−0.430 ***
		(0.88)	(3.03)	(0.56)	(−4.86)
FinDev		0.005	0.002	0.003	0.064 ***
		(1.13)	(0.47)	(0.45)	(6.16)
Constant	0.203 ***	−0.182 ***	0.500 ***	−0.591 ***	−0.065
	(42.91)	(−3.90)	(13.00)	(−8.96)	(−0.54)
Time Fixed	Yes	Yes	Yes	Yes	Yes
Province Fixed	Yes	Yes	Yes	Yes	Yes
Observation	403	403	403	403	403
R2	0.887	0.920	0.964	0.811	0.818
adj. R2	0.873	0.909	0.959	0.786	0.793

Notes: Table in parentheses report t statistics. *** and ** indicate significance at the 1% and 5% levels respectively.

reports the regression results for the impact of data element agglomeration on inclusive green development. Columns (1) and (2) report the results for the aggregate index. Data element agglomeration significantly promotes inclusive green development, with or without control variables. The results are consistent with H1. Columns (3)–(5) report the results for each sub-dimension. Data element agglomeration significantly promotes economic growth and social inclusion, but its effect on green ecology is insignificant. This partially supports H2. Industries with public goods characteristics—such as environmental governance and ecological conservation—lack corresponding market incentives. Firms therefore allocate data resources to areas that generate direct economic returns. This suggests that, at the current stage, development has yet to meet the green rigid constraint, and the green dividend of data element agglomeration remains to be realized.

4.2. Endogeneity Analysis

A mutually reinforcing relationship may exist between inclusive green development and data element agglomeration. Data element agglomeration drives inclusive green development by optimizing resource allocation, enhancing productivity, fostering technological innovation, and strengthening environmental governance. Conversely, inclusive green development stimulates further data element agglomeration.

Following Z. Dong et al. (2025) [21] and Zhai et al. (2026) [63], terrain ruggedness and the number of fixed telephone lines in 1984 are selected as instrumental variables (IV1 and IV2) for cross-validation. As both instrumental variables are cross-sectional, they cannot be directly used in panel data analysis. Following Nunn and Qian (2014) [64], a time trend is interacted with each instrument to construct panel-valid instrumental variables. Specifically, terrain ruggedness and 1984 landline telephone numbers are interacted with time trends to form the panel IVs, thereby testing the potential reverse causality between data element agglomeration and inclusive green development.

Terrain ruggedness index is selected as an instrumental variable for two reasons. First, as a natural geographic feature shaped by tectonic movements and long-term geomorphic evolution, it is exogenous to contemporary inclusive green development. Second, rugged terrain increases the cost of deploying data infrastructure such as fiber optics and degrades signal coverage, thereby directly impeding data collection and transmission, ensuring strong relevance. The number of landline telephones in 1984 is selected as an instrumental variable for two reasons. First, the deployment of landline telephones in 1984 was primarily determined by historical policy and urban planning, rendering it plausibly exogenous to current inclusive green development. Second, the historical distribution of landline infrastructure influenced the diffusion of information and communication technologies and the location of data centers, ensuring strong relevance.

As shown in

Table 5. Results of Endogeneity Test.

Variable	IV1		IV2		Exclusion Restriction
	First Stage	Second Stage	First Stage	Second Stage	First Stage	Second Stage
Topographic relief index × Time Trend	−0.002 ***				−0.001 ***
	(−5.31)				(−2.65)
The number of landline telephones in 1984 × Time Trend			0.000 ***		0.000 ***
			(7.92)		(6.91)
DEA		0.982 ***		1.039 ***		1.028 ***
		(7.59)		(9.13)		(9.74)
Urban	−0.002	0.008 ***	−0.004 *	0.009 ***	−0.002	0.008 ***
	(−0.96)	(3.72)	(−1.86)	(3.70)	(−1.04)	(3.75)
Trade	−0.212 ***	0.182 ***	−0.211 ***	0.192 ***	−0.219 ***	0.190 ***
	(−3.13)	(3.02)	(−3.12)	(3.01)	(−3.26)	(3.05)
HC	−11.193 ***	9.853 ***	−8.186 ***	10.344 ***	−9.496 ***	10.246 ***
	(−6.47)	(4.55)	(−4.38)	(5.02)	(−5.54)	(5.02)
Consump	0.022	−0.003	0.059	−0.006	0.039	−0.005
	(0.41)	(−0.06)	(1.07)	(−0.13)	(0.74)	(−0.11)
FinDev	0.013 *	−0.005	0.010	−0.006	0.008	−0.006
	(1.79)	(−0.86)	(1.41)	(−1.04)	(1.24)	(−1.02)
Constant	0.508 ***		0.510 ***		0.452 ***
	(3.23)		(3.19)		(2.84)
Time Fixed	Yes	Yes	Yes	Yes
Province Fixed	Yes	Yes	Yes	Yes
Kleibergen-Paap rk LM statistic	14.69 [0.00]		20.03 [0.00]		23.21 [0.00]
Kleibergen-Paap rk Wald F statistic	22.63 [16.38]		62.69 [16.38]		38.54 [16.38]
Hansen J						0.63
Observation	403	403	403	403	403	403
R2	0.917	−0.092	0.925	−0.204	0.926	−0.181
adj. R2	0.906	−0.243	0.915	−0.371	0.916	−0.344

Notes: Table in parentheses t statistics. *** and * indicate significance at the 1% and 10% levels respectively. The R-squared in 2SLS estimation can be negative and is not meaningful for assessing model fit (Sribney et al., 2022 “For Two-Stage Least-Squares (2SLS/IV/Ivregress) Estimates, Why Is the R² Statistic Not Printed in Some Cases?” Stata FAQ, StataCorp, 2022, 17.0, https://www.stata.com/support/faqs/statistics/two-stage-least-squares/, accessed on 26 February 2026) [65]. Model validity is assessed using the first-stage F-statistic, which exceeds conventional thresholds.

, the Kleibergen-Paap rk LM statistics for both IVs are significant at the 1% level. And the Kleibergen-Paap rk Wald F statistics exceed the 10% critical value of 16.38, confirming that IVs pass both the under-identification and weak identification tests. The first-stage regression indicates a significantly negative coefficient for the interaction between topographic relief index and time trend, whereas the interaction term for 1984 landline penetration and time trend is significantly positive. The second-stage estimates confirm that data element agglomeration promotes inclusive green development. The coefficient magnitudes remain within tenfold of the baseline estimates, ruling out severe reverse causality. The Hansen J test yields a p-value of 0.63, well above conventional significance levels, indicating that the instruments are exogenous and satisfy the exclusion restriction. The baseline results are therefore robust.

4.3. Robustness Tests

4.3.1. Outlier Handling

To mitigate the influence of outliers, quantile trimming is applied at the 1st/99th and 5th/95th percentiles. As shown in columns (1) and (2) of

Table 6. Outlier Handling Results.

Variable	(1)	(2)
	1% Winsorization	5% Winsorization
	IGD	IGD
DEA	0.527 ***	0.606 ***
	(15.88)	(14.50)
Constant	−0.142 ***	−0.025
	(−3.17)	(−0.67)
Controls	Yes	Yes
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	403	403
R2	0.926	0.914
adj. R2	0.916	0.903

Notes: Table in parentheses report t statistics. *** indicate significance at the 1% levels respectively. See Appendix E, Table A11 for detailed results on the control variables.

, data element agglomeration remains statistically significant at the 1% level in promoting inclusive green development.

4.3.2. Using Lagged Explanatory Variables

To address potential autocorrelation bias, data element agglomeration is lagged by one period. As shown in column (1) of

Table 7. Extend the Time Window and Drop Centrally Administered Municipalities Results.

Variable	(1)	(2)
	Using Lagged Explanatory Variables	Exclusion of Centrally Administered Municipalities
	IGD	IGD
DEA		0.763 ***
		(14.59)
L. DEA	0.440 ***
	(13.50)
Constant	−0.159 ***	−0.069
	(−3.36)	(−1.26)
Controls	Yes	Yes
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	372	351
R2	0.920	0.985
adj. R2	0.909	0.983

Notes: Table in parentheses report t statistics. *** indicate significance at the 1% levels respectively. L. DEA represents the data element agglomeration lagged by one period. See Appendix E, Table A12 for detailed results on the control variables.

, the one-period lagged data element agglomeration remains positive and significant at the 1% level for inclusive green development. The test results for the second and third lags of the explanatory variables are presented in Appendix E.2.

4.3.3. Exclusion of Centrally Administered Municipalities

Given the significant differences in administrative status and economic structure between centrally administered municipalities and provinces, these municipalities are excluded from the analysis. As shown in column (2) of Table 7, the baseline results remain robust after excluding these municipalities.

4.3.4. Add Control Variables

Although a set of control variables is included, potential confounding effects cannot be theoretically excluded. Following Madia et al. (2025) [66], two additional variables—tax burden and labor force size—are sequentially incorporated to assess robustness. Tax burden is measured as the ratio of tax revenue to regional GDP, and labor force size is defined as the natural logarithm of employment. As shown in columns (1) and (2) of the positive effect of data element agglomeration on inclusive green development remains significant at the 1% level after controlling for these additional variables, confirming the baseline findings.

Table 8. Add Control Variables Results.

Variable	(1)	(2)
	Add Tax Burden	Add Labor Force Size
	IGD	IGD
DEA	0.452 ***	0.449 ***
	(14.93)	(14.45)
Tax burden	0.538 ***
	(3.73)
Labor force size		0.024
		(1.00)
Constant	−0.192 ***	−0.362 *
	(−4.17)	(−1.94)
Controls	Yes	Yes
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	403	403
R2	0.923	0.920
adj. R2	0.913	0.909

Notes: Table in parentheses report t statistics. *** and * indicate significance at the 1% and 10% levels respectively. See Appendix E, Table A13 for detailed results on the control variables.

, the positive effect of data element agglomeration on inclusive green development remains significant at the 1% level after controlling for these additional variables, confirming the baseline findings.

4.4. Mechanism Analysis

To further examine the mediating roles of industrial structure upgrading and green technology innovation, empirical tests are conducted based on the theoretical analysis. The results are presented in

Table 9. Results of Suppression Effect.

Variable	(1)	(2)	(3)
	IGD	IndStr	GreenTech
DEA	0.452 ***	0.930 ***	6.717 ***
	(7.33)	(2.99)	(3.67)
Urban	0.004 ***	−0.056 ***	−0.100 ***
	(4.89)	(−7.53)	(−3.28)
Trade	0.082 **	0.265	−2.608 ***
	(2.42)	(1.59)	(−3.23)
HC	5.256 ***	26.233 ***	−39.249 *
	(6.00)	(3.93)	(−1.94)
Consump	0.030	0.552 **	−39.249 *
	(0.90)	(2.41)	(0.68)
FinDev	0.005	0.079 *	−0.424 ***
	(1.03)	(1.91)	(−4.57)
Constant	−0.152 ***	3.471 ***	9.449 ***
	(−2.69)	(7.32)	(5.47)
Time Fixed	Yes	Yes	Yes
Province Fixed	Yes	Yes	Yes
Observation	403	403	403
R2	0.976	0.970	0.948
adj. R2	0.973	0.966	0.940

Notes: Table in parentheses report t statistics. ***, **, and * indicate significance at the 1%, 5%, and 10% levels respectively.

.

Column (2) reports the estimated effects for industrial structure upgrading as a mechanism. The estimates show that data element agglomeration significantly promotes industrial upgrading at the 1% level. This suggests that agglomeration improves real-time matching of market demand and supply, reduces resource misallocation, and drives industrial transformation toward digitalization and service-orientation, forming higher value-added chains. The upgraded industrial structure enables more precise monitoring and control of energy use, reducing waste and pollution. Consequently, it raises production efficiency, lowers employment barriers for low-income groups, and shifts production toward greater environmental sustainability, ultimately promoting inclusive green development. These results confirm the first stage of the proposed pathway: data agglomeration positively drives industrial upgrading.

Column (3) reports the estimated effects for green technology innovation as a mechanism. The estimates show that data element agglomeration significantly promotes green technology innovation at the 1% level. Specifically, satellite remote sensing and Internet of Things (IoT) sensors generate real-time data on environmental pollution and energy consumption, creating new research opportunities for green technology development. Data-driven simulation further accelerates green technology upgrades by shortening R&D cycles. This process generates new green jobs—providing skill-matched employment—and reduces clean energy costs, benefiting small and medium-sized enterprises and households. It also enables precise ecological governance in remote regions, reducing interregional disparities in green development. These results confirm the first stage of the proposed pathway: data agglomeration positively drives green technology innovation.

Table 10. The Bootstrap Mechanism Effect Results.

Path	Effect Type	Coefficient	Standard Error	95% Bootstrap Cl
DEA → IndStr → IGD	Direct Effect	1.003	0.09	[0.86, 1.20]
	Indirect Effect	−0.099	0.04	[−0.18, −0.03]
DEA → GreenTech → IGD	Direct Effect	1.192	0.02	[0.96, 1.45]
	Indirect Effect	−0.288	0.00	[−0.52, −0.14]

reports the mechanism test results using a bias-corrected bootstrap method (500 replications). Both mediation models reveal a consistent pattern: the direct effects of data element agglomeration on inclusive green development are significantly positive (coefficients of 1.003 and 1.192, respectively, with confidence intervals excluding zero). In contrast, the indirect effects through industrial structure upgrading and green technology innovation are significantly negative (coefficients of −0.099 and −0.288, respectively, with confidence intervals excluding zero). This pattern is known as a suppression effect [67], indicating that the mediators act as suppressors rather than conduits for the positive effect—meaning that while data agglomeration directly promotes inclusive green development, the pathways through industrial upgrading and green innovation currently weaken rather than strengthen this positive impact.

There are three potential explanations for this finding. First, short-term adjustment costs and temporal mismatches play a key role. Industrial upgrading and green technology innovation entail substantial upfront investments—such as phasing out traditional industries, retiring outdated equipment, and funding long-cycle R&D—while their benefits (e.g., cleaner production, green jobs, and reduced energy costs) tend to materialize only gradually. Within the sample period, these upfront costs may outweigh the realized benefits, potentially generating a negative statistical association with inclusive green development. This temporal mismatch is consistent with the innovation literature, which has documented J-curve effects in the context of green transitions. Second, institutional heterogeneity helps explain the suppression effect. Given China’s substantial regional disparities in institutional quality —a conditionality also emphasized in recent studies of digital development [68]—industrial upgrading and green innovation may not automatically yield inclusive outcomes in less developed areas, precisely those receiving high fiscal transfers. Instead, these processes can generate exclusionary side effects: workers may lack skills for new green jobs; green R&D may crowd out basic public services; and digital infrastructure may be deployed without ensuring affordability or accessibility. These institutional frictions can temporarily suppress the inclusive benefits of green transitions. Third, resource misallocation in fiscally dependent regions offers a complementary explanation, directly linking to the fiscal support threshold effect. Provinces with high fiscal support intensity—such as Tibet, Qinghai, and Gansu—tend to exhibit lower institutional quality and greater reliance on traditional industries. In these regions, industrial upgrading and green innovation, often driven by top-down policy mandates rather than market demand, may lead to resource misallocation. Fiscal funds intended for green innovation may be diverted to infrastructure with limited local applicability, and data element agglomeration may concentrate in low-productivity sectors rather than genuinely green activities. Together, these explanations suggest that the suppression effect does not contradict H3 and H4 but rather refines them. Data element agglomeration promotes industrial upgrading and green innovation (Table 9, first-stage results). However, whether these transformations translate into inclusive green development depends on institutional context, regional absorptive capacity, and the time horizon considered. The negative indirect effects capture the transitional frictions inherent in China’s current digital and green transformation—frictions that policy interventions could potentially alleviate. Detailed results of the robustness checks for green technology innovation are presented in Appendix C.2.

4.5. Threshold Effect Analysis

To examine whether the impact of data element agglomeration on inclusive green development varies with fiscal support intensity, a panel threshold regression model is estimated. The presence and number of thresholds are tested using a nonparametric percentile bootstrap method with 300 replications. As shown in

Table 11. Threshold Estimates.

Threshold	Estimator	F-Value	p-Value	Number of Bootstrap Replications	Critical Value
					10%	5%	1%
Single Threshold	0.174	43.38	0.01	300	25.81	31.60	45.23
Double Threshold	0.304	30.82	0.02	300	22.92	27.38	32.46
Triple Threshold	0.185	21.24	0.57	300	41.72	48.77	63.66

, the single and double threshold effects are significant at the 5% level, while the triple threshold effect is insignificant. The estimated threshold values are 0.174 and 0.304. Accordingly, the double-threshold model is adopted for the analysis.

Based on the threshold values, fiscal support intensity is categorized into three regimes: low (≤0.174), medium (0.174 < support ≤ 0.304), and high (>0.304) levels.

Table 12. Regression Results of Threshold Effect Test.

Variable	IGD
FisSup ≤ 0.174	0.531 ***
	(15.59)
0.174 < FisSup ≤ 0.304	0.368 ***
	(12.58)
FisSup > 0.304	−0.257 **
	(−2.36)
Urban	0.008 ***
	(13.74)
Trade	0.012
	(0.61)
HC	3.315 ***
	(4.46)
Consump	0.127 ***
	(4.33)
FinDev	0.015 ***
	(3.90)
Constant	−0.346 ***
	(−16.15)
Observation	403
R2	0.916
adj. R2	0.908

Notes: Table in parentheses report t statistics. *** and ** indicate significance at the 1% and 5% levels respectively.

shows that under low fiscal support, data element agglomeration has a significant positive effect on inclusive green development, with a coefficient of 0.531 at the 1% level. In the early development stage of the data element market, government investment in foundational infrastructure reduces data circulation costs, stimulates market vitality, and compensates for underinvestment by the private sector. Under moderate fiscal support, the coefficient declines to 0.368, remaining significantly positive. The marginal effect weakens considerably, indicating diminishing returns as the data element market matures. This suggests that sustained policy dependence may dampen private sector innovation incentives. Under high fiscal support, the coefficient becomes negative (−0.257) and is significant at the 5% level. Further evidence is needed to fully interpret this result. Provinces with high fiscal support—such as Tibet, Qinghai, and Gansu—differ systematically from those with low fiscal support—such as Guangdong and Jiangsu—in economic development, industrial structure, and institutional quality. The observed nonlinear pattern may therefore partly reflect heterogeneity in development stages, rather than solely the nonlinear effect of fiscal support intensity. Based on the analytical framework of the “political resource curse”, this study argues that in less developed regions with weaker institutional quality, excessive fiscal transfers may foster fiscal dependence and weaken local governments’ incentives to cultivate market mechanisms and improve the business environment [69]. In such contexts, agglomerated data, talent, and capital may become locked into inefficient traditional industries or infrastructure, rather than flowing toward green innovation or inclusive finance. This resource curse-like mechanism—where excessive reliance on external transfers stifles endogenous innovation—may prevent data element agglomeration from realizing its positive spillovers and may even adversely affect inclusive green development. The threshold effect observed in this study should therefore be understood as the joint outcome of fiscal support intensity, regional development stage, and institutional quality. H5 is therefore supported. The detailed results of the robustness checks for the threshold effects are presented in Appendix A.

This mechanism is corroborated by the typical cases of high fiscal support provinces. Provinces exceeding the high-level fiscal support threshold include Gansu, Guizhou, Hainan, Heilongjiang, Jilin, Inner Mongolia, Ningxia, Qinghai, Tibet, Xinjiang, and Yunnan. These provinces are predominantly located in western and northern border regions of China, most of which are economically underdeveloped. Fiscal transfers to these regions are primarily allocated to maintaining social stability and narrowing regional development gaps. Over the sample period, Tibet exhibits the highest fiscal support intensity, with a mean value of 1.283—well above the threshold of 0.304. It peaks at 1.379 in 2016, then gradually declines to 0.974 by 2021. Over the same period, Tibet’s data element agglomeration level averages 0.0493, ranking 24th among all provinces. This reveals a structural mismatch in Tibet: high fiscal support intensity coexists with low data element agglomeration. There are potential explanations. First, as an ethnic border region, Tibet prioritizes fiscal resources for basic public services and infrastructure over data element market development, resulting in limited data element accumulation. According to China’s National Bureau of Statistics, Tibet’s fiscal spending on science and technology accounts for 0.31% of its total budget, significantly lower than the 12.25% allocated to general public services. Second, Tibet’s economy relies primarily on agriculture and tourism—sectors that inherently lack digital application scenarios. Its vast territory, sparse population, and harsh high-altitude climate increase operational costs for digital infrastructure, hindering economies of scale and discouraging market participation.

4.6. Heterogeneity Analyses

Data element agglomeration’s impact on inclusive green development is inherently embedded within a three-dimensional framework comprising institutional environment, market development level, and temporal dynamics. Here, geographic location serves as a spatial proxy for the combined effect of institutional environment and market development: the coastal-inland dichotomy encapsulates the long-term coupling of marketization, openness, and institutional constraints. Environmental regulation intensity, a core component of the institutional framework, has an effectiveness that is contingent on its alignment with market development. Temporal dynamics are captured by a policy threshold—the official designation of data as a factor of production in April 2020—which demarcates distinct phases in the market-oriented allocation of data elements. Accordingly, this paper develops a three-dimensional analytical framework—integrating institutional environment, market development, and temporal dynamics—to separately examine the heterogeneous effects of data element agglomeration on inclusive green development across three dimensions: geographic location, environmental regulation intensity, and policy timing.

4.6.1. Geographical Heterogeneity

Given substantial disparities in economic development, resource endowments, and industrial structure, provinces are classified as coastal or inland following National Bureau of Statistics criteria. As shown in columns (1) and (2) of

Table 13. Results of Geographical Heterogeneity.

Variable	(1)	(2)
	Coastal Provinces	Inland Provinces
	IGD	IGD
DEA	0.508 ***	0.471 ***
	(7.65)	(7.59)
Urban	0.007 ***	0.008 ***
	(5.66)	(5.37)
Trade	0.033	0.135 ***
	(0.94)	(3.68)
HC	4.426 ***	6.218 ***
	(3.35)	(6.69)
Consump	−0.032	−0.005
	(−0.48)	(−0.14)
FinDev	0.003	0.002
	(0.47)	(0.46)
Constant	−0.253 ***	−0.350 ***
	(−3.26)	(−4.13)
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	143	260
R2	0.988	0.970
adj. R2	0.985	0.966
Chow test	4.54 [0.00]

Notes: Table in parentheses report t statistics. *** indicate significance at the 1% levels respectively. [0.00] indicates the p-value of the Chow test.

, data element agglomeration has a significant positive effect on inclusive green development in both regions, with larger coefficients in coastal areas. A Chow test is conducted to examine structural differences between the two groups. The p-value of 0.00 for the coefficient difference confirms that the effect is significantly stronger in coastal regions. This may be explained by coastal areas’ superior digital infrastructure, which lowers the marginal costs of data acquisition, storage, and computing, facilitating the transformation of raw data into productive uses. In contrast, inland regions lag in traditional industry digitalization, attenuating the effect.

4.6.2. Environmental Regulation Intensity Heterogeneity

The Porter Hypothesis posits that well-designed environmental regulations stimulate innovation, enhancing productivity and long-term competitiveness while achieving win-win outcomes for environmental and economic performance [70]. Following Wang et al. (2025) [71], environmental regulation intensity (ERI) is measured as the ratio of industrial pollution control investment to secondary industry value-added. Regions are classified into high-ERI and low-ERI groups based on a median split. The results are presented in columns (3) and (4) of

Table 14. Results of Environmental Regulation Heterogeneity.

Variable	(1)	(2)
	High ERI	Weak ERI
	IGD	IGD
DEA	0.633 ***	0.220 ***
	(5.29)	(3.30)
Urban	0.007 ***	0.008 ***
	(5.47)	(5.06)
Trade	0.016	0.008
	(0.39)	(0.17)
HC	5.176 ***	0.081
	(3.79)	(0.06)
Consump	−0.097 ***	0.007
	(−2.82)	(0.09)
FinDev	0.010 **	0.019 **
	(2.03)	(2.27)
Constant	−0.272 ***	−0.256 **
	(−3.79)	(−2.45)
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	201	202
R2	0.980	0.985
adj. R2	0.975	0.980
Chow test	7.16 [0.00]

Notes: Table in parentheses report t statistics. *** and ** indicate significance at the 1% and 5% levels respectively. High ERI refers to a high degree of environmental regulation intensity. Weak ERI refers to a low degree of environmental regulation intensity. [0.00] indicates the p-value of the Chow test.

. Data element agglomeration significantly promotes inclusive green development at the 1% level in both groups. Notably, the coefficient is larger in the high-ERI group. A Chow test is conducted to test for structural differences between the two groups. The p-value of 0.00 for the coefficient difference confirms that the effect is significantly stronger in the high-ERI group. This disparity may reflect the capacity of high-ERI regions to drive production optimization, enhance resource efficiency, and reallocate capital toward green sectors, collectively promoting sustainable economic restructuring [72]. However, firms in high-ERI regions may also circumvent environmental penalties by relocating pollution-intensive production to low-ERI jurisdictions or adopting greenwashing strategies as substitutes for genuine green transformation [73].

4.6.3. Temporal Heterogeneity

In April 2020, China’s Central Committee and the State Council issued the Opinions on Building a More Effective Institutional Mechanism for Market-based Allocation of Production Factors, which explicitly classified data as the fifth key production factor, alongside land, labor, capital, and technology. This policy marked the official elevation of data from an economic resource to a fundamental national strategic resource, establishing an institutional foundation for the development of China’s data element market. Accordingly, the sample period is divided into two phases—pre−2020 and post−2020—to examine the temporal heterogeneity in the effect of data element agglomeration on inclusive green development. As shown in

Table 15. Temporal Heterogeneity.

Variable	(1)	(2)
	Pre-2020	Post-2020
	IGD	IGD
DEA	0.496 ***	0.119 **
	(5.01)	(2.40)
Urban	0.006 ***	0.001
	(4.82)	(0.22)
Trade	0.054	0.013
	(1.59)	(0.42)
HC	4.569 ***	−0.234
	(2.95)	(−0.21)
Consump	−0.103 ***	0.259 ***
	(−3.39)	(5.32)
FinDev	−0.010 **	0.015 **
	(−2.01)	(2.48)
Constant	−0.144 *	0.195
	(−1.91)	(1.17)
Time Fixed	Yes	Yes
Province Fixed	Yes	Yes
Observation	279	124
R2	0.977	0.999
adj. R2	0.972	0.998
Chow test	7.58 [0.00]

Notes: Table in parentheses report t statistics. ***, **, and * indicate significance at the 1%, 5%, and 10% levels respectively. Pre-2020 refers to the period from 2011 to 2019, and Post-2020 refers to the period from 2020 to 2023. [0.00] indicates the p-value of the Chow test.

, the effect of data element agglomeration on inclusive green development is positive and significant in both periods, but it is larger in the pre-2020 phase. A Chow test is conducted to test for a structural break between the two periods. The p-value of 0.00 for the coefficient difference confirms that the effect is significantly larger in the pre-2020 period. Data-driven improvements—such as process optimization or enhanced information transparency—could generate significant efficiency gains and modest environmental benefits under China’s traditional extensive development model. However, to achieve higher-standard inclusive green targets, a series of regulatory policies—including the Data Security Law and the “Data Twenty Measures”—were introduced in the post-2020 period. While these policies standardized the market, they may have increased short-term institutional costs for data allocation, temporarily dampening the net positive effect of data element agglomeration.

4.7. Further Analysis

4.7.1. Model Selection

Lagrange multiplier (LM), likelihood ratio (LR), and Wald tests are conducted to guide spatial econometric model selection. The results are presented in

Table 16. Applicability Test of Spatial Durbin Model.

Test Methods	Test Type	Statistic	p-Value
Lagrange Multiplier Test	LM-spatial lag	108.27	0.00
	Robust LM-spatial lag	41.83	0.00
	LM-spatial error	70.07	0.00
	Robust LM-spatial error	3.64	0.05
Likelihood Ratio Test	LR-spatial lag	54.81	0.00
	LR-spatial error	56.31	0.00
Wald Test	Wald-spatial lag	46.55	0.00
	Wald-spatial error	74.84	0.00
Hausman Test	FE or RE	80.36	0.00

. All test statistics are significant at conventional levels, suggesting potential spatial spillover effects of data element agglomeration on inclusive green development. This supports the use of a spatial Durbin model. The Hausman test rejects the random effects specification at the 1% level. Accordingly, a spatial Durbin model with two-way fixed effects is employed for the empirical analysis.

4.7.2. Spatial Spillover Analysis

Using a digital infrastructure connectivity matrix (See Appendix F for the rationale for digital infrastructure connectivity matrix.), an inverse distance matrix, and an adjacency matrix, the spatial spillover effects of data element agglomeration on inclusive green development are examined. As shown in

Table 17. Regression Results of Spatial Spillover Effects.

Variable	(1)	(2)	(3)
	Digital Infrastructure Connectivity Weight Matrix	Inverse Distance Weight Matrix	Adjacency Weight Matrix
	IGD	IGD	IGD
DEA	0.504 ***	0.466 ***	0.419 ***
	(0.03)	(0.03)	(0.03)
WxDEA	0.659 ***	0.548 ***	0.209 ***
	(0.12)	(0.19)	(0.06)
Direct	0.483 ***	0.482 ***	0.434 ***
	(0.03)	(0.03)	(0.03)
Indirect	0.339 ***	1.009 **	0.350 ***
	(0.08)	(0.40)	(0.06)
Total	0.822 ***	1.491 ***	0.784 ***
	(0.09)	(0.42)	(0.08)
rho	−0.416 ***	0.302 **	0.199 ***
	(0.11)	(0.15)	(0.07)
sigma2_e	0.000 ***	0.000 ***	0.000 ***
	(0.00)	(0.00)	(0.00)
Controls	Yes	Yes	Yes
Time Fixed	Yes	Yes	Yes
Province Fixed	Yes	Yes	Yes
Observation	403	403	403
R2	0.210	0.484	0.393

Notes: Table in parentheses report standard error. *** and ** indicate significance at the 1% and 5% levels respectively. WxDEA refers to the spatial spillover effect of data element agglomeration on inclusive green development, rho represents the spatial autocorrelation coefficient, and sigma2_e denotes the variance of the random disturbance term, where a smaller value indicates a better model fit.

. Regression Results of Spatial Spillover Effects, the spatial autoregressive coefficients are all statistically significant, confirming that data element agglomeration exerts significant spatial spillovers on inclusive green development.

For local effects, data element agglomeration has a positive and significant impact on local inclusive green development at the 1% level. This suggests that data element agglomeration enhances resource allocation efficiency, drives intelligent transformation in traditional industries, and reduces the share of high-pollution and energy-intensive sectors. For spatial spillover effects, data element agglomeration in neighboring regions has a significant positive effect on local inclusive green development. Data element agglomeration in neighboring regions enhances local green productivity and social equity through technological spillovers and scale effects from industrial clustering, reflecting a pronounced diffusion effect.

Table 17 also shows that the estimated coefficients for both local and spillover effects are larger under the digital infrastructure connectivity matrix than under the inverse distance or adjacency matrices. This suggests that data-driven green development depends more on digital connectivity than on geographic proximity. Strong green synergies can persist even across geographically distant regions when supported by interregional digital infrastructure. The digital infrastructure connectivity matrix also yields a significantly negative spatial autoregressive coefficient. This suggests that, unlike in geographic space, digital infrastructure connectivity—while facilitating information flow—may also intensify interregional competition through siphoning effects. Despite this, data element agglomeration—characterized by non-rivalry and strong spillovers—generates knowledge diffusion, technology spillovers, and market sharing, yielding net positive effects on neighboring regions.

Spatial decomposition reveals significantly positive direct, indirect, and total effects. The total effect (0.822) is substantially larger than the direct effect (0.483), indicating that digital infrastructure investment generates considerable spatial spillovers. When provinces decide independently, they capture only 0.483 of the local benefits. With coordinated regional planning, however, spatial externalities are fully realized, raising the average provincial benefit to 0.822. This finding provides strong empirical support for the coordinated planning and integrated development of cross-regional digital infrastructure.

5. Discussion

5.1. Comparison with Existing Studies

This study provides evidence that data element agglomeration contributes to inclusive green development. Using China, a major developing economy, as an empirical context, this analysis examines the relationship and underlying mechanisms linking data element agglomeration to inclusive green development. In doing so, it complements and extends recent studies by Z. Dong et al. (2025) [62], Fang (2024) [22], and Khan et al. (2025) [12] on the role of digitalization in inclusive green development.

Mechanism analysis identifies two channels through which data element agglomeration affects inclusive green development: industrial structure upgrading and green technology innovation. The first channel—industrial upgrading—is consistent with Hao et al. (2023) [74], who emphasize the importance of structural transformation for green growth. This study extends this line of inquiry by examining how data element mobility enables industrial upgrading to foster inclusive green outcomes, subjecting this mechanism to rigorous econometric testing. The second channel is green technology innovation. The results indicate that data element agglomeration accelerates green technological progress. This finding provides empirical support for the mechanism through which digitalization enables green innovation—a channel that, as Du et al.(2019) [61] demonstrate, subsequently contributes to emission reduction. While prior studies focus on the environmental outcomes of green innovation, this analysis examines its digital antecedents, thereby extending the literature.

The analysis also reveals that fiscal support intensity moderates the relationship between data element agglomeration and inclusive green development in a nonlinear fashion. This finding aligns with Divino et al. (2020) [54], who show that fiscal subsidies do not follow a “more-is-better” logic: beyond an optimal threshold, additional subsidies reduce social welfare. This study extends this insight by demonstrating a threshold effect in the context of data element agglomeration. As fiscal support increases, the positive effect of data element agglomeration on inclusive green development gradually weakens. Once fiscal support exceeds a critical threshold, further increases cause data element agglomeration to significantly hinder inclusive green development. This nonlinear moderating effect may also be subject to alternative interpretations. For instance, the observed diminishing returns could reflect not only inefficiency from excessive subsidies but also crowding-out effects, where government support displaces private investment in digital infrastructure or green innovation. Alternatively, regions with high fiscal support may face diminishing marginal returns due to absorptive capacity constraints—that is, the local economy’s ability to effectively utilize additional data resources may be constrained by human capital or institutional quality. Disentangling these competing explanations warrants further investigation.

The enabling effect of data element agglomeration varies substantially across regions. Specifically, the effect is more pronounced in coastal provinces and in provinces with stringent environmental regulations. For coastal regions, this effect likely stems from superior digital infrastructure, which facilitates efficient data utilization. This finding is consistent with Hao et al. (2023) [74] and Yang and Hu (2026) [48], who argue that digital–real economy integration in coastal cities enhances carbon efficiency and supports green economic recovery. For regions with strict environmental regulations, the stronger effect corroborates [75], who find that stringent regulations—whether market-based or government-led—reduce emissions and accelerate corporate green transitions. The stronger effect observed in coastal provinces and regions with stringent environmental regulations may also reflect alternative underlying dynamics. For coastal regions, the enabling effect could be partially attributable to agglomeration economies and knowledge spillovers that are correlated with—but not caused by—digital infrastructure. For regions with strict environmental regulations, the stronger effect might result from sample selection: provinces that voluntarily adopt stringent regulations may differ systematically in unobserved ways—such as greater administrative capacity or stronger environmental awareness—from those that do not. Future research employing quasi-experimental designs could help isolate the causal contribution of regulatory stringency from these confounding factors.

This study further documents spatial spillovers: data element agglomeration fosters inclusive green development not only locally but also in neighboring regions. This result echoes Huang et al. (2025) [13], who demonstrate that China’s National Big Data Pilot Zones promote inclusive green growth both locally and in remote regions, while inhibiting it in adjacent areas.

5.2. Limitations and Future Research

This study has several limitations that merit acknowledgment.

First, regarding measurement, the entropy method provides objective weighting for constructing the data element agglomeration and inclusive green development indices, and cross-period stability tests confirm that the indices are insensitive to sample period selection. However, any composite index involves subjective choices in indicator selection, normalization, and aggregation. In particular, as a multidimensional concept, the weights assigned to the three dimensions of inclusive green development reflect the relative dispersion of indicators during the sample period, not their theoretical importance. Future research could employ alternative weighting methods—such as principal component analysis or equal weighting—for sensitivity analysis and cross-validation, or use expert survey-based analytic hierarchy process to enhance the robustness and validity of index construction.

Second, concerning institutional factors, although the provincial-level data allow for controlling a comprehensive set of socioeconomic variables—including social consumption level, trade openness, and urbanization rate—omitted variable bias cannot be entirely ruled out. In particular, certain institutional factors that vary across provinces and over time—such as the stringency of digital policy implementation, the enforcement effectiveness of environmental regulations, and informal constraints like administrative culture or policy coordination mechanisms—are difficult to quantify and are therefore not directly incorporated into the models. These latent institutional characteristics may simultaneously influence both data element agglomeration and inclusive green development, potentially confounding the estimated relationships. Future research could capture these institutional dimensions using policy text analysis to construct quantitative indicators of digital policy support, or employ instrumental variable methods to better address endogeneity concerns.

Third, regarding spatial econometric specifications, this study employs a spatial weight matrix based on digital infrastructure connectivity to examine spatial spillover effects. This approach better captures the flow characteristics of data element agglomeration than traditional geographical distance matrices. However, the choice of spatial weight matrix is inherently subjective, and different specifications may affect the estimation of spillover effects. Although robustness checks using alternative specifications—including inverse distance and adjacency matrices—yield results largely consistent with the baseline findings, cross-regional data flows may still be affected by policy barriers, digital divides, and industrial linkages. These complex mechanisms are difficult to capture fully with a single weight matrix. Future research could explore more flexible spatial econometric specifications—such as nested matrices or dynamic spatial panel models—to delineate the spatial spillover boundaries of data element agglomeration more precisely.

Fourth, regarding causal identification, the analysis combines [76] two-step framework with bootstrap mediation. While this approach establishes the existence of mechanism channels through coefficient significance and provides robust inference for indirect effects, it does not address potential issues such as simultaneity, reverse causality, or feedback loops. The results should therefore be interpreted with caution. Future research could employ more rigorous causal identification strategies—such as instrumental variable approaches, difference-in-differences designs, or quasi-natural experiments—to further validate these mechanisms and establish causality more convincingly.

6. Conclusions and Recommendations

This study analyzes panel data from 31 Chinese provinces (excluding Hong Kong, Macao, and Taiwan) over 2011–2023. Data element agglomeration and inclusive green development—including their sub-dimensions—are measured using the entropy weight method. The empirical analysis employs a two-way fixed-effects model, a threshold regression model, and a spatial Durbin model to examine the impact of data element agglomeration on inclusive green development. The baseline results show that data element agglomeration significantly enhances inclusive green development. Across sub-dimensions, it significantly improves economic growth and social inclusion, but has no statistically significant effect on ecological performance. These findings are robust to a series of sensitivity tests. Mechanism analysis reveals that industrial structure upgrading and green technology innovation act as channels linking data element agglomeration to inclusive green development, yet exhibit a suppression effect: their indirect contributions are negative while the direct effect remains positive. Threshold regression reveals a nonlinear relationship between fiscal support intensity and the effect of data element agglomeration on inclusive green development. The positive effect of data element agglomeration on inclusive green development is more pronounced in coastal provinces, regions with stringent environmental regulations, and the pre−2020 period. The spatial Durbin model results show that data element agglomeration significantly enhances local inclusive green development and exerts positive spillover effects on neighboring regions. Based on these findings, the following policy insights emerge.

(1)

Based on the baseline regression results, government agencies should facilitate the integration of data elements into advanced manufacturing and modern services, using data to enhance supply chain efficiency and develop new productive capacities. Data should also be used to optimize public services—such as agriculture, gig employment, and telemedicine—by improving information access. Given the insignificant effect on the green ecology dimension, policymakers should establish and improve foundational institutions for data elements—including rights confirmation, circulation and trading, and security safeguards.

(2)

Given the mechanism results, policies should promote industrial structure upgrading and green technology innovation—for instance, by establishing a cloud-based carbon emissions monitoring platform to facilitate digital energy-saving retrofits and by supporting a multi-sector data-sharing repository to lower innovation barriers. However, given the suppression effect observed, these initiatives should be complemented by transition assistance—such as worker retraining programs and extended evaluation horizons—to cushion short-term adjustment costs and allow long-term benefits to materialize.

(3)

Policies should emphasize the role of fiscal support intensity. First, fiscal support has an optimal scale. Policy design should account for the threshold effects of fiscal expenditure intensity to avoid diminishing or even negative returns from excessive scale. In regions with already high support levels, a prudent assessment of existing expenditure efficiency and timely adjustments in intervention modes are warranted. Second, expenditure composition matters more than scale. Thus, while maintaining moderate scale, fiscal resources should be directed toward areas that can directly cultivate data element markets.

(4)

Given the observed regional heterogeneity, policymakers should adopt a differentiated approach tailored to local conditions. Coastal provinces should pioneer “data-for-green” demonstration zones, where replicable models of data-driven green transition could be institutionalized and later scaled to inland regions. In regions with high environmental regulations, data elements should be prioritized for precision pollution control, fostering innovative environmental governance through data element agglomeration. Weakly regulated regions should first digitize environmental compliance systems before leveraging data element agglomeration for green outcomes. In the short run, policy should prioritize transitional support—such as compliance assistance and pilot flexibility—to cushion the immediate institutional costs of reform. Over the medium to long term, the emphasis should gradually pivot toward building robust institutional capacity.

(5)

Given the positive spatial spillover effects, policymakers should prioritize cross-regional data sharing and industrial linkages. Joint data industrial parks should be established across administrative boundaries through collaboration between developed and less-developed regions. Concurrently, digital infrastructure integration requires coordinated upgrades across regions. Data flow and sharing mechanisms require institutional optimization, particularly to ensure equitable distribution of digital dividends.