The local land use carbon budget (LUCB) balance is an important factor in achieving regional carbon neutrality. As the basic unit of China’s economic development and social governance, the county level is an important part of the realization of the “double carbon” goal. This paper focuses on 1069 county units within the Yangtze River Economic Belt (YREB). It utilizes data on land use, nighttime light, energy consumption, and social and economic factors to construct carbon emission models. The spatiotemporal characteristics of LUCB in these county units are analyzed using standard deviational ellipse (SDE) and spatial autocorrelation methods. Additionally, a zoning study is conducted by examining the economic contribution coefficient (ECC) of carbon emissions, the ecological support coefficient (ESC), and their coupling relationship. The results show that (1) the total land use carbon emissions (LUCE) increased significantly during the research period, and the total carbon sink was relatively stable. (2) The LUCB is spatially high in the east and low in the west, with the center of gravity moving to the southwest as a whole. (3) The LUCB shows positive spatial autocorrelation and has significant spatial agglomeration characteristics, which are mainly high–high and low–low regional agglomeration types. (4) The ECC is high in the east and low in the west, the ESC is high in the west and low in the east, and the coordination and coupling degrees of the two are low. (5) According to the ECC and ESC, the county unit is divided into a low-carbon conservation area, an economic development area, a carbon sink development area, and a comprehensive optimization area. This study is helpful in promoting the sustainable development of carbon neutrality and low carbon in the YREB.

Land use carbon emissions (LUCE) contribute significantly to global warming. Recognizing the influence of regional heterogeneity and geographical scale on socioeconomic development, studying LUCE at various scales is crucial for devising more effective emission reduction measures. However, previous studies have predominantly focused on a single scale. This study focuses on the Yangtze River Economic Belt (YREB), utilizing land use, nighttime light, and energy consumption data to compute LUCE at provincial, prefectural, and county scales, employing spatial autocorrelation, geographic detectors, and the Multiscale Geographically Weighted Regression (MGWR) model to analyze the spatiotemporal dynamics and impact factors of LUCE across different scales. Our results show: (1) Throughout the study period, LUCE in the YREB exhibited a steady increase, rising from 28,434.32 × 10⁴ t to 86,581.79 × 10⁴ t. (2) Positive spatial autocorrelation was observed in LUCE at all three scales. Notably, spatial clustering intensified at the provincial and prefectural levels, while a diminishing trend in clustering was noted at the county scale. (3) Predominant clustering patterns at the prefectural and county scales included H–H and L–L types, with the county scale displaying more pronounced clustering characteristics. (4) Economic development emerged as the primary influencing factor on LUCE at both the prefectural and county scales. Nevertheless, the intensity of impact from carbon emission intensity, industrial structure, population size, government intervention, and land use degree differs between the two levels. This research underscores the high sensitivity of LUCE to administrative scales, emphasizing the necessity of considering these scales when formulating emission reduction strategies.

Studying carbon emissions (CE) at different administrative scales will help facilitate crafting tailored emission reduction policies for China’s regions, which is vital for achieving the dual carbon goals. However, previous studies focused on a single administrative scale, lacking multiscale research. This paper combined energy consumption data with nighttime light data and adopted a spatial autocorrelation, variation coefficient (VC), and decoupling model to study the spatiotemporal dynamics and decoupling effect of CE at the three administrative scales of provinces, prefecture-level cities, and counties in China from 2000 to 2020. The results were as follows: (1) The VC of CE showed different trends at different scales, with its coefficient size successively ranked at the county, prefecture, and province levels. (2) CE at different scales showed positive spatial autocorrelation and the significance was strongest at the county level. (3) The decoupling trend between CE and economic growth has generally shown a positive development across different spatial scales. The average elasticity decoupling index at the provincial, prefectural, and county levels has decreased overall, from 0.88, 1.82, and 3.74 to 0.19, 1.36, and 3.05, respectively. However, the characteristics of these changes differ. The CV of the elasticity decoupling index increased at the provincial and prefectural levels, rising from 1.161 to 1.563 to 1.419 and 2.669, respectively, while it decreased slightly at the county level, from 3.862 to 3.765. (4) The dominant type of decoupling at the provincial level had changed from expansive negative decoupling (ED) to strong decoupling (SD). Meanwhile, at the prefectural and county levels, the ED was still dominant, but the number of SD had increased significantly, rising from 75 to 138 at the prefectural level and from 748 to 1160 at the county level. This study demonstrates China’s carbon emissions sensitivity to scale, emphasizing the importance of adapting emission reduction measures to local conditions.