Optimizing the allocation of hydropower resources is essential for aligning high-quality economic growth with China’s carbon neutrality goals. Due to constraints such as market segmentation and government regulation, the resource allocation function of the Chinese market has not been effectively utilized, which leads to hydropower resources being allocated inefficiently. In the digital age, it is valuable to investigate whether digital economic development can rectify the misallocation of hydropower resources and whether the corrective effects exhibit temporal dynamics and spatial heterogeneity. Accordingly, this study employs panel data collected from 30 provincial-level administrative regions in China from 2000 to 2023, employing the production function method combined with a counterfactual analysis framework for quantifying the degree of hydropower resource mismatch. Additionally, panel vector autoregression models and panel threshold regression utilized for discussing spatio-temporal effects of digital economic development on hydropower resource mismatch. Empirical results demonstrate that digital economic development significantly curbs hydropower resource misallocation, albeit with a discernible time lag. When the digital economy experiences a positive impulse shock, its impact on the hydropower resources mismatch emerges in the first lag period, peaks in the second lag period, and then stabilizes. Secondly, the corrective impact of digital economic development on hydropower resources mismatch is contingent upon the level of regional industrialization, which is more pronounced in regions with higher levels of industrialization. In conclusion, this paper offers evidence-based policy recommendations to facilitate the localized implementation of digital economy policies and enhance the efficiency of hydropower resources allocation.

As digital technology and green development become more integrated, exploring how the digital economy affects energy consumption is vital for coordinating economic growth with energy conservation and emission reduction. This study uses panel data from 30 Chinese provinces covering the years 2011 to 2023, constructing a digital economy development index system using the entropy weighting method. Additionally, it employs panel threshold models and spatial Durbin models to empirically examine the nonlinear effects and spatial spillover effects of the digital economy on energy consumption. The research reveals that: First, the digital economy exhibits a pronounced threshold effect on energy consumption. When the level of digital economic development falls below the threshold value of 0.024, it effectively curbs energy consumption. Once this threshold is exceeded, it paradoxically promotes energy consumption exhibiting a nonlinear pattern of “initial suppression followed by promotion”. This confirms the existence of the demand-side green paradox of the digital economy effect—where efficiency gains from technological progress are offset by consumption growth, unexpectedly stimulating higher energy consumption. Second, the digital economy exhibits a complex spatial effect on energy consumption characterized by “local promotion and neighboring suppression”. Although it demonstrates negative spillovers in surrounding areas, the overall regional effect remains positive, revealing the spatial complexity of this “green paradox”. These findings challenge traditional linear assumptions and offer valuable insights into regional digital economic planning and the promotion of green, low-carbon development.

Whether the market-oriented allocation of data factors can simultaneously improve electricity resource allocation efficiency and environmental performance is a key question for the synergy between the digital economy and the green low-carbon transition. Using the entropy weight method to construct a data factor marketization index and a Cobb–Douglas production function to measure electricity resource misallocation, this paper examines the impact of data factor marketization on electricity resource misallocation and its carbon emission consequences, using panel data of 233 prefecture-level cities in China from 2008 to 2023. We find that data factor marketization significantly reduces electricity resource misallocation, and this conclusion remains robust across a series of robustness checks. Mechanism analysis shows that data factor marketization alleviates misallocation through two channels: compressing inappropriate government intervention in electricity allocation and improving capital-output efficiency; the mitigating effect is stronger in cities with higher industrialization intensity and human capital levels. Heterogeneity analysis indicates that the effect is more pronounced in eastern and central-western regions but not significant in the old industrial base of Northeast China. Further environmental consequence analysis shows that electricity resource misallocation significantly raises per capita CO₂ emissions, while data factor marketization not only directly suppresses emissions but also indirectly reduces emissions by lowering misallocation, with the emission-reduction effect being stronger in cities with larger population sizes. It should be emphasized that the data factor marketization index measures the enabling conditions for the market-oriented allocation of data factors (infrastructure foundation, development capacity and overall scale) rather than the realized volume of data transactions; the results therefore speak to the effects of the foundations of data factor marketization. Our findings provide empirical evidence for coordinating data factor market construction, electricity system reform, and carbon reduction policies.