Agricultural carbon emission efficiency is of great significance to accelerate the green and low-carbon transformation, thus promoting high-quality agriculture under the dual-carbon strategy in Liaoning Province, China. In this study, the Super-Efficiency Slack-Based Measure (Super-SBM) model was adopted to accurately measure the agricultural carbon emission efficiency. A systematic analysis was also made for the dynamic spatiotemporal evolution from 2012 to 2022. The key driving factors and their internal action were further explored to combine the Geodetector model and the Geographically and Temporally Weighted Regression (GTWR) model. The results show that: 1) The total agricultural carbon emissions of Liaoning Province presented a trend of first decreasing and then increasing during the period of 2012—2022, while the overall agricultural carbon emission efficiency maintained a steady upward trend with an outstanding stage. The agricultural carbon emission efficiency shared significant spatial agglomeration and regional heterogeneity. Among them, Chaoyang, Dalian, and Yingkou were characterized by high efficiency with stable and excellent performance. Fuxin, Tieling, and Huludao cities were trapped in low-efficiency lagging areas with a slow improvement speed. Shenyang, Panjin, and Benxi cities shared a fluctuating upward state with exciting potential for high efficiency. 2) The dominant driving factors of agricultural carbon emission efficiency shared staged evolution, gradually transforming from the single dominance of resource use in the initial stage to the dual dominance of employment structure and industrial structure in the middle and late stages; Meanwhile, the explanatory power of key influencing factors continued to increase, such as regional economic scale, technical application level, urbanization rate, and mechanization level, indicating the multi-factor driving trend for agricultural carbon emission efficiency. 3) All the influencing factors also presented significant spatial differentiation in the study area. The agricultural carbon emission efficiency was restricted by multiple key factors, including the inter-regional economy, advanced technical promotion, technological investment, allocation structure, and green support. Thus, the high-quality low-carbon transformation can be realized to optimize technical adaptability, coordinated allocation of key production factors, and industrial structure in green agriculture. In conclusion, the spatiotemporal evolution and driving mechanism of agricultural carbon emission efficiency can provide a practical path for the differentiated and precise low-carbon transformation. The finding can also offer a useful reference for green and low-carbon agriculture in the major grain-producing areas of Northeast China.

Studying energy consumption and carbon emissions in the three provinces of northeast China, a key industrial region, is crucial for advancing high-quality development in the region. Based on the fitted carbon emission values of nighttime light data of 35 prefectural-level cities in the three northeastern provinces from 2012 to 2021, the paper analyzes the spatio-temporal heterogeneity of carbon emissions and the influencing factors by using spatial autocorrelation model and the GTWR-STIRPAT combined model. The results indicate that (1) there are marked spatio-temporal variations in carbon emissions across the three northeastern provinces, exhibiting an overall "U"-shaped trend over time, and spatial distribution of high-value areas with a point-like pattern centered around provincial capitals. (2) Carbon emissions as a whole show no obvious spatial agglomeration, and the local correlation characteristics show the spatial aggregation characteristics of "low-low" agglomeration. (3) There is significant spatial and temporal heterogeneity in the carbon emissions across different cities, attributable to a range of influencing factors. Among these, the total population, the proportion of the secondary industry, and the per capita GDP exert a positive driving influence on the carbon emissions within the three northeastern provinces. Conversely, the urbanization rate, the aging rate of the population, and the number of patents granted have a dual impact on the carbon emissions. In response to these findings, it is recommended that policies be developed with scientifically informed planning tailored to local conditions to hasten the achievement of carbon peaking and neutrality goals, and to contribute to the construction of a beautiful China.