The rapid expansion of the digital economy is reshaping urban systems, yet the pathways through which digitalization drives urban green land use transition remain insufficiently understood. Using panel data from 279 Chinese cities, we measure Urban Green Land Use Efficiency (UGLUE) via a super-efficiency Slack-Based Measure (SBM) model and estimate digital economy effects through double machine learning (DML). We find that digital technology, digital industry, and digital infrastructure all positively influence UGLUE, with digital technology exerting the strongest effect, followed by digital industry, while digital infrastructure exerts the weakest direct effect. Urban spatial compactness (USC) mediates this relationship, functioning as a dominant transmission channel for both digital industry and digital technology, and a supplementary yet significant pathway for digital infrastructure, indicating that digitalization enhances UGLUE in part by promoting more compact urban forms. Effects are heterogeneous, as resource-based and old industrial cities benefit more from technological upgrades, while cities with higher administrative status gain more from broader digital development. These findings identify USC as a key transition pathway linking digitalization to sustainable land use outcomes, and provide evidence-based support for governance-differentiated digital economy policies that steer urban land use transition toward green and compact development trajectories.

Clarifying the complex interaction between climate risks and the new energy industry chain is of key significance to advancing the energy transition and strengthening industrial chain robustness. This research pairwise-matches the climate physical risk and the climate transition risk with the entire range of the new energy industry chain segments, comprehensively examining the pairwise interactive relationships. By applying the MF-ADCCA series of methods, it was revealed that there are prevalent asymmetric cross-correlated multifractal characteristics between climate risks and the new energy industry. The long-term memory under the upward trend of the market is distinctly stronger than that under the downward trend. Given that this correlation can indirectly reflect market efficiency differences, this paper constructs the Hurst Volatility Sensitivity Index (HVI) and the Hurst Asymmetry Index (HAI) and further proposes the Unified Market Efficiency Index (UMEI). Its innovative advantage resides in the balanced integration of volatility efficiency and structural symmetry, in turn enabling a comprehensive assessment of the new energy market efficiency under climate risk perturbations. Static analysis reveals that the overall market efficiency of the new energy industry under the climate transition risk is generally higher than that under the climate physical risk, and the market efficiency of mature upstream and midstream new energy segments is significantly superior to that of the downstream. Dynamic evolution characteristics indicate that market efficiency has typical time-varying traits, the evolution of which is often driven by significant policies or extreme events. The climate transition risk tends to trigger aperiodic structural adjustments, while the climate physical risk mostly induces periodic efficiency fluctuations. This study furnishes solid evidence for the new energy market in coping with climate risks.