Land-use change substantially contributes to carbon emissions, yet systematic research on complex human–environment interactions in old industrial bases remains scarce. Here, we integrated multi-temporal land-use data and socio-economic statistics from 1990 to 2023 to analyze the spatiotemporal dynamics and drivers of land-use carbon emissions in Northeast China. The land-use transfer matrix, the carbon emission coefficient, exploratory spatiotemporal data analysis (ESTDA), standard deviational ellipses, and modified Kaya–LMDI models were applied. Construction land area expanded by 120%, with its share of total emissions increasing from 87% to 95%. Meanwhile, forest and grassland shrank, reducing their carbon sink capacity and increasing their net carbon emissions 1.9-fold. Spatially, emissions showed a weak global correlation but strong local lock-in (i.e., persistent stability of local spatial patterns over time), with the emission center of gravity shifting southwestward. Economic development was the dominant positive driver (provincial contribution rates: 275–529%), whereas energy intensity was the main mitigating factor (up to −409%). Population loss exerted a slight negative contribution, while energy structure showed only a weak inhibitory effect (−9.1%), reflecting the region’s path-dependent lock-in to fossil fuels. This study provides a scientific basis for differentiated carbon management strategies in Northeast China and analogous regions.

Urban spatial patterns regulate microclimates, yet those nonlinear interactive actions of 2D landscape patterns and 3D building morphology on land surface temperature (LST) among diverse urban functional zones (UFZs) remain insufficiently quantified. Using Shenyang as the case study, this study coupled Bayesian-optimized LightGBM and GeoShapley to disentangle these driving mechanisms. The models demonstrated high predictive accuracy (R² > 0.84 across all UFZs). Results show high-temperature zone proportions ranking as: commercial (17.93%) > public service (11.40%) > residential (8.70%) > industrial (3.97%). Further factor analysis reveals that while geographic location is the predominant overall driver, after removing this baseline effect, residential, public service, and commercial zones are mainly regulated by MNDWI and NDVI, while industrial zones are dominated by Spatial Congestion Degree (SCD) and Floor Area Ratio (FAR). Morphological metrics exhibit clear ecological thresholds: residential zones show turning points at FAR 1.19 and NDVI 0.13; commercial zones see SCD’s cooling effect reverse to warming beyond 0.27; industrial zones experience sharp deterioration when SCD and FAR exceed 0.09 and 0.51, respectively. These findings demonstrate the necessity of UFZ-based differentiated spatial regulation and provide actionable thresholds for climate-adaptive urban planning.