The research question addresses whether data element agglomeration can significantly boost green innovation vitality (GIV) in China, particularly within the context of the country's dual-carbon goals and innovation-driven development strategy. The study highlights the importance of enhancing GIV as a crucial component of fostering new drivers of innovation-driven green development and strengthening productive forces. China's impressive growth in green patents contrasts with a relatively low industrialization rate, suggesting a need for further stimulation of GIV. Data elements, with their unique characteristics of non-exclusivity, replicability, and value-added sharing, are posited as a potential "main engine" for this enhancement. The study aims to empirically test this hypothesis, acknowledging regional heterogeneity across China and exploring the mediating and threshold effects of government support and public environmental concern on the DATA-GIV relationship. The successful implementation of such research would contribute significantly to both theoretical understanding and practical policy recommendations in China's pursuit of ecological civilization and sustainable development.

The literature review explores existing research on data element agglomeration (DATA) and green innovation vitality (GIV). Regarding DATA, studies emphasize its liquidity, permeability, and spillover effects, influencing interregional cooperation and economic development. Research on platform economies highlights the role of data as a crucial asset, impacting the value creation of platform enterprises and potentially leading to data monopolies. Additionally, DATA's capacity to enhance innovation efficiency by improving understanding of customer needs and market trends is acknowledged. Existing research on GIV is categorized into pre-effects (analyzing mechanisms of policy impact) and after-effects (examining GIV's impact on environmental governance and structural optimization). The review identifies gaps in research, including the lack of a universally accepted DATA measurement standard, limited research on the interplay of DATA and GIV during the strategic convergence of digital and sustainable development, and insufficient exploration of the complex, nonlinear relationship between DATA and GIV, particularly considering regional heterogeneity. This study aims to bridge these gaps.

The study employs a quantitative approach using panel data from 30 Chinese provinces from 2011 to 2021. Three models are used: a baseline regression model to assess the direct effect of DATA on GIV; a mediating effect model to examine the indirect effects through government support (GS) and public environmental concern (PEC); and a dynamic threshold regression model to analyze the nonlinear relationship considering GS and PEC as threshold variables. The dependent variable, GIV, is measured using the logarithm of the number of green invention patent applications. The core independent variable, DATA, is constructed using a projection pursuit method based on internet broadband access, the number of enterprise websites, and the number of e-commerce trading enterprises. GS is measured by government financial expenditure, and PEC by the frequency of public environmental reports. Control variables include science and technology human capital, industrial structure, marketization degree, and unemployment degree. The study uses fixed-effects models and system GMM estimation to address potential endogeneity issues. Robustness checks are conducted using different control variables and by lagging the dependent variable. Data sources include the China Statistical Yearbook and other public materials from the National Bureau of Statistics of China.

The empirical results reveal a significant positive direct effect of DATA on GIV, confirming the primary hypothesis. This effect exhibits a spatial gradient of "central > western > eastern," suggesting that regions with lower initial levels of technological resources and innovation networks may benefit more from DATA. The mediating effect analysis demonstrates that DATA indirectly enhances GIV through both GS and PEC. Government support plays a partial mediating role, with DATA boosting government spending, which in turn positively influences GIV. Public environmental concern also plays a partial mediating role, where improved environmental information transparency increases public engagement, positively influencing GIV. The dynamic threshold regression analysis confirms a nonlinear relationship, showing a stronger positive effect of DATA on GIV when both GS and PEC exceed their respective thresholds. The robustness tests confirm the consistency and reliability of these findings across different model specifications and variable selections.

The findings highlight the importance of data element agglomeration as a driver of green innovation vitality in China. The spatial heterogeneity underscores the need for regionally tailored policies that leverage DATA's potential effectively, especially in central and western regions where the positive effects are potentially greater. The mediating roles of government support and public environmental concern suggest the significance of policy interventions aimed at enhancing these factors alongside the fostering of data infrastructure. The nonlinear relationship emphasizes the threshold effects, highlighting the necessity of sufficient government support and strong public engagement to fully realize the potential of DATA in driving green innovation. The research contributes to the understanding of the intricate relationship between data, innovation, and sustainable development, providing valuable insights for policy-makers and practitioners.

This study demonstrates a significant positive relationship between data element agglomeration and green innovation vitality in China, revealing direct, indirect, and nonlinear effects. The findings highlight the importance of regionally differentiated policies aimed at fostering data infrastructure, strengthening government support, and enhancing public environmental awareness to maximize the contribution of data to green innovation. Future research could delve into more granular data, explore additional mediating or moderating variables, and incorporate the evolving role of digital technologies like AI and machine learning in shaping the data-innovation nexus.

The study's limitations include the use of provincial-level data, which might mask variations at finer geographic levels. The analysis focuses primarily on patent applications as a proxy for green innovation, potentially overlooking other aspects of green innovation. Further research could explore additional mediating variables and consider the potential influence of unobserved factors. The data used is subject to restrictions due to an ongoing project.