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.

Geopolitical conflicts continue to disrupt global energy supply chains, causing sharp changes in energy prices. These changes reshape the cost structure and efficiency levels of grain circulation. Based on panel data from 30 Chinese provinces covering 2011–2022, this study constructs a proxy for grain circulation efficiency using a resource misallocation framework and counterfactual decomposition. We employ panel threshold regression and double machine learning methods to systematically examine the nonlinear impact of energy price levels on grain circulation efficiency and to reveal regional differences. The findings are as follows: (1) Energy price levels exhibit a significant single-threshold effect. When energy prices remain within a low range, rising prices exert a positive technology push effect; beyond the threshold, a cost-squeeze suppression dominates. (2) The eastern region shows the highest tolerance for price increases, whereas the western region has the lowest tolerance, with the central region falling in between. (3) Double machine learning feature importance analysis reveals that research and experimental development investment and economic development are the dominant factors affecting agricultural product circulation efficiency in the eastern region; water consumption and capital stock are the key factors in the central region; and energy prices, foreign trade dependence, and infrastructure are the most sensitive factors in the western region. This study provides empirical evidence for designing differentiated regional circulation policies and enhancing the resilience of the grain circulation system.