Deconstructing the Digital Economy: A New Measurement Framework for Sustainability Research

Le résumé fourni par la source

Empirical research on the impact of the digital economy on sustainable development is hampered by severe methodological challenges. Discrepancies in the theoretical foundations and construction logic of measurement frameworks have led to diverse and often conflicting conclusions, hindering the systematic accumulation of knowledge. This study aims to address this critical gap by proposing a new, logically consistent measurement framework. To overcome the existing limitations, we construct a functional deconstruction framework grounded in General-Purpose Technology (GPT) theory and a “stock–flow” perspective. This framework deconstructs the digital economy into a neutral “digital infrastructure” (stock platform) and two forces reflecting its inherent duality: a “consumption force” (digital industrialization) and an “empowerment force” (industrial digitalization). Based on this, we develop a measurement system adhering to the principle of “logical purity” and apply a “two-step entropy weighting method with annual standardization” to assess 30 provinces in China from 2012 to 2023. Our analysis reveals a multi-scalar evolution. At the micro level, we identified four distinct provincial development models and three evolutionary paths. At the macro level, we found that the overall inter-provincial disparity followed an inverted U-shaped trajectory, with the core contradiction shifting from an “access gap” to a more profound “application gap.” Furthermore, the primary driver of this disparity has transitioned from being “empowerment-led” to a new phase of a “dual-force rebalancing.” The main contribution of this study is the provision of a new analytical tool that enables a paradigm shift from “aggregate assessment” to “structural diagnosis.” By deconstructing the digital economy, our framework allows for the identification of internal structural imbalances and provides a more robust and nuanced foundation for future causal inference studies and evidence-based policymaking in the field of digital sustainability

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