This study investigates the interaction between digital–intelligent integration and carbon productivity in 23 prefecture-level cities across the middle reaches of the Yellow River from 2013 to 2022, focusing on a resource-dependent region transitioning towards low-carbon development. The aim is to examine how digital technologies contribute to improving carbon productivity and reducing environmental pollution. An entropy-weighted index system was used to assess digital–intelligent transformation and carbon productivity. A coupling coordination model was applied to measure their joint performance, with spatial autocorrelation and spillover analyses used to detect regional patterns and intercity linkages. Data were sourced from official yearbooks, environmental bulletins, and urban big-data platforms. The results show a steady improvement in coordination between digital–intelligent integration and carbon productivity, with significant progress in 2018 and 2020 following national policy initiatives. Core cities showed higher coordination and generated positive spillovers, while peripheral cities lagged, resulting in noticeable spatial agglomeration. These findings highlight the growing coupling between digital–intelligent development and carbon productivity, reinforced by policy initiatives but accompanied by regional disparities. This study suggests that policies should focus on enhancing data infrastructure in core cities, improving regional cooperation, and bridging gaps in peripheral areas. It offers insights into the role of digital technologies in achieving low-carbon development in resource-dependent urban regions.

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development.

Smart city policies may affect urban carbon emissions and developed land at the same time, but the two may not change in the same direction. This study examines city-level carbon dioxide (CO₂) emissions per unit of built-up land area, whose natural logarithm is denoted as lnCLCI using panel data for 40 cities in the middle reaches of the Yellow River Basin and surrounding areas from 2011 to 2022. The main policy sample includes 37 cities after removing three cases where the Smart City Pilot designation covered only a sub-prefectural area. Treatment timing is defined using the actual adoption year of each city. Group-time average treatment effects are used as the main estimates, while a two-way fixed-effects model serves as a benchmark. The analysis also separates the responses of carbon emissions and built-up land, tests spatial policy exposure and pre-policy heterogeneity, and evaluates prediction performance in cities excluded from model training. The unadjusted group-time average treatment effect is 0.026 with a p-value of 0.708. After adjustment for pre-treatment covariates, the estimate is −0.040 with a p-value of 0.673. The TWFE benchmark gives an estimate of 0.008 with a p-value of 0.909. These estimates do not provide evidence that the Smart City Pilot policy reduced lnCLCI on average. Estimates for log carbon emissions (lnCO₂) and log built-up land area (lnBUA) are also statistically imprecise, and the pre-policy interaction tests show no consistent pattern of treatment heterogeneity. Estimates of spatial policy exposure are sensitive to the choice of spatial weight matrix. In the 2013–2022 prediction sample, Random Forest achieves a held-out-city R² of 0.568, compared with 0.767 for OLS. Taken together, the results show that changes in carbon emissions per unit of built-up land should not be interpreted as changes in absolute emissions alone. They also show the importance of keeping predictive performance separate from estimates of policy effects.

Urban low-carbon transition is increasingly shaped by the interaction between digital infrastructure, intelligent applications, land-space allocation, and regional governance. Existing studies have mainly examined whether the digital economy or smart-city development can reduce emissions, but less attention has been paid to the coordination between digitalization and intelligentization, the carbon cost of digital infrastructure, and the spatial consequences of local gains. This research defines digital–intelligent integration as the coupling coordination between digitalization and intelligentization subsystems. Using panel data for 39 prefecture-level cities in the Middle Reaches of the Yellow River Urban Agglomeration from 2013 to 2022, it applies Global Moran’s I, a spatial Durbin model, partial-derivative effect decomposition, alternative spatial weight matrices, alternative dependent variable tests, and multidimensional heterogeneity analysis. The own-city coefficient of digital–intelligent integration in the carbon-efficiency model is positive (0.0282, p < 0.05), whereas the spatial-equilibrium direct effect is statistically insignificant. These quantities are not short- and long-run estimates: the former is a conditional model coefficient, while the latter incorporates spatial feedback. The indirect effect on neighboring carbon efficiency is negative and remains negative under contiguity, economic-distance, and geo-economic nested matrices. Under an otherwise identical fixed-effects specification, digital–intelligent integration lowers local construction-land carbon intensity but raises neighboring intensity. The structural estimates further show that local conversion is weaker in industrially and energy-intensive cities. Digital–intelligent integration should therefore be interpreted as a governance capacity rather than a net-carbon technology; its regional effect depends on industrial lock-in, infrastructure-energy demand, and cross-city responsibility sharing.

Regional averages may improve even when individual cities remain within the same coordination state. Using panel data for 40 cities in the middle reaches of the Yellow River from 2011 to 2022, this study distinguishes continuous changes in the corrected coordination degree D from discrete state transitions and further examines spatial clustering, neighborhood-conditioned mobility, socioeconomic heterogeneity, and land-use landscape associations. Under the baseline specification, mean D increased from 0.3074 in 2011 to 0.3790 in 2022. Among 440 adjacent-year transitions, 85.23% remained in the same state and 99.77% occurred within the same or a neighboring state. State persistence, however, did not imply an absence of progress. Among same-state transitions, 62.4% still recorded an increase in D, and the next-year upgrading probability was markedly higher for observations located closer to the next state threshold, reaching 29.9% in the closest distance quartile but only about 1% in each of the two most distant quartiles. Spatial mapping and Getis–Ord Gi* identify localized clusters of high and low coordination values, while spatial Markov results provide no consistent evidence that transition patterns depend on neighborhood conditions across alternative spatial specifications. Geographically and temporally weighted regression (GTWR) reveals substantial spatial and temporal variation in socioeconomic associations, although residual spatial autocorrelation remains in 2013 and 2019. Landscape Shape Index and Patch Density vary much more across cities than within cities over time, and their fixed-effects associations with D are not reproduced under first differencing. Taken together, the results indicate that coordination in the study area is characterized more by gradual within-state adjustment than by frequent movement across state boundaries. They also show that spatial clustering, local socioeconomic associations, and landscape structure capture different dimensions of this process and should not be interpreted as direct evidence of spillover or causal mechanisms.