Agricultural land use has a remarkable influence on the stock and distribution of soil organic carbon (SOC). However, both regional soil sampling and process-based ecosystem models for SOC estimation at the regional scale have limitations when applied in areas with a large land use change. In the present study, a framework (CMCS) combining CENTURY modeling (CM) and chronosequences sampling (CS) was established, and a case study was conducted in Cangshan County, where vegetable cultivation conversion from grain production was significant in recent decades. The SOC stock (SOCS) of the non-vegetable area estimated by CM was comparable to that estimated by regional soil sampling in 2008. This result confirmed that CM was reliable in modeling SOC dynamics in a non-vegetable area without land use change. However, when applied to the overall cropland of Cangshan County, the CM, without considering the land use change, underestimated the SOCS by 0.23 Tg (6%), compared with the observed measurements (3.58 and 3.81 Tg, respectively). Using the CMCS framework of our study, the underestimation of CM was offset by the SOC sequestration estimated by CS. The SOCS estimated by the CMCS framework ranged from 3.72 to 4.02 Tg, demonstrating that this framework is reliable for the regional SOC estimation of large-area land use change. In addition, annual SOCS dynamics were obtained by this framework. The CMCS framework provides a low-cost and practicable method for the estimation of the regional SOC dynamic, which can further support the strategy of carbon peaking and carbon neutrality in China.

Monitoring the spatio-temporal dynamics of desertification is critical for desertification control. Using the Urat front flag as the study area, Landsat remote sensing images between 2010 and 2020 were selected as data sources, along with MOD17A3H as auxiliary data. Additionally, RS and GIS theories and methods were used to establish an Albedo–NDVI feature space based on the normalized difference vegetation index (NDVI) and land surface albedo. The desertification difference index (DDI) was developed to investigate the dynamic change and factors contributing to desertification in the Urat front banner. The results show that: ① the Albedo–NDVI feature space method is effective and precise at extracting and classifying desertification information, which is beneficial for quantitative analysis and monitoring of desertification; ② from 2010 to 2020, the spatial distribution of desertification degree in the Urat front banner gradually decreased from south to north; ③ throughout the study period, the area of moderate desertification land increased the most, at an annual rate of 8.2%, while the area of extremely serious desertification land decreased significantly, at an annual rate of 9.2%, indicating that desertification degree improved during the study period; ④ the transformation of desertification types in Urat former banner is mainly from very severe to moderate, from severe to undeserted, and from mild to undeserted, with respective areas of 22.5045 km², 44.0478 km², and 319.2160 km². Over a 10-year period, the desertification restoration areas in the study area ranged from extremely serious desertification to moderate desertification, from serious desertification to non-desertification, and from weak desertification to non-desertification, while the desertification aggravation areas ranged mainly from serious desertification to moderate desertification; ⑤ NPP dynamic changes in vegetation demonstrated a zonal increase in distribution from west to east, and significant progress was made in desertification control. The change in desertification has accelerated significantly over the last decade. Climate change and irresponsible human activities have exacerbated desertification in the eastern part of the study area.

Regional logistic networks and linking urban clusters to boost high quality economic development are both key topics in sustainable development in China. In recent years, China has highlighted the significance of the logistics industry and urban cluster development, attaching practical importance to the connections between these two topics from a spatial perspective. This paper aims to discuss how regional logistic networks linking urban clusters improve economic development. This study constructs a logistic evaluation indicator system based on the multi-indicator data of 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area in 2020. This research employs the entropy-weighted-TOPSIS method to measure and rank the comprehensive logistics quality of each city in the economically linked logistic network of the Guangdong-Hong Kong-Macao Greater Bay Area. This study applies the modified gravity model to explore the logistics linkage and logistic network characteristics of each city in the Guangdong-Hong Kong-Macao Greater Bay Area. Finally, this research analyzes how the agglomeration ability of the central cities in the Guangdong-Hong Kong-Macao Greater Bay Area affects other cities from a spatial perspective. Furthermore, this spatial perspective investigates the agglomeration effect of the economic linkage logistic network through the social network analysis method. The results have the following three implications. (1) The logistic network has a high density, a stable overall structure with a strong agglomeration effect, and there is an increasingly mature logistic network development in the Guangdong-Hong Kong-Macao Greater Bay Area in the Bay Area. (2) Agglomeration is significant in the central cities of the Guangdong-Hong Kong-Macao Greater Bay Area including Hong Kong, Shenzhen, Guangzhou, and Macao. Nonetheless, insufficient peripheral cities have been cultivated. Therefore, the government should focus on strengthening the balance of urban development in the bay area and improving the logistics access among cities to break through the barriers of regional synergistic development. (3) The economic development of cities is highly correlated with the level of logistics links. Additionally, the economy is the cornerstone to promoting the high-quality development of the logistics industry. Moreover, the economy and logistics are inseparable, mutually promoted, and developed together.

The changes in cultivated soil organic carbon (SOC) have significant effects on soil fertility and atmospheric carbon dioxide (CO₂) concentration. Shandong Province is an important agricultural and grain production area in China. Dry farmland accounts for 74.15% of the province’s area, so studies on dynamic SOC changes would be helpful to understand its contribution to the Chinese national carbon (C) inventory. Using the spatial overlay analysis of the soil layer (1:10,000,000) and the land use layer (1:10,000,000), 2329 dry farmland soil polygons were obtained to drive the CENTURY model to simulate SOC dynamics in Shandong Province from the period 1980 to 2016. The results showed that the CENTURY model can be used to simulate the dry farmland SOC in Shandong Province. From the period 1980 to 2016, the soil organic carbon storage (SOCS) and soil organic carbon density (SOCD) showed an initial increase and then decreased, especially after reaching a maximum in 2009. In 2016, the SOCS was 290.58 × 10⁶ t, an increase of 26.99 × 10⁶ t compared with 1980. SOCD in the dry farmland increased from 23.69 t C ha⁻¹ in 1980 to 25.94 t C ha⁻¹ in 2016. The dry farmland of Shandong Province was a C sink from 1980 to 2016. Among the four soil orders, inceptisols SOCD dominated, and accounted for 47.81% of the dry farmland, followed by >entisols > vertisols > alfisols. Entisols SOCD growth rate was the highest (0.23 t C ha⁻¹year⁻¹). Compared to 1980, SOCD in 2016 showed an increasing trend in the northeast, northwest and southeast regions, while it followed a downward trend in the southwest.

The spatial coupling of population and economy is an important indicator that reflects regional differences and measures the coordination degree of industrial layouts and environments. To explore the spatial coupling of population and economic densities and the effect of topography in Anhui Province at a grid scale, a land-use impact model was used to spatialize socio-economic indicators in Anhui Province using 2020 county-level data. Geographical concentration and coupling index were used to quantify the spatial relationship between population and economic densities. Then the effects of topography were assessed. The results show the following: (1) the accuracy of the regression models for the individual counties is generally better than that of the models for the whole region. The population and economic densities predicted by the proposed model reflect actual conditions. (2) Topography has a stronger effect on population density and primary industry density than on secondary and tertiary industry density. Slope has the strongest effect on population and economic densities, followed by topographic relief and elevation. (3) A spatial correlation exists between topographic factors and population and economic densities. Although the spatial relationship between population and economic densities is predominantly of the balanced development type in regions with complex topography, population and economic densities are significantly lower in regions with complex rather than flat topography. In addition, economic development in the northern Anhui region, a flat region, lags behind population aggregation. Efforts should be made to improve the economic level of the southern and northern Anhui regions and the Dabie Mountain region in western Anhui.

In coastal saline–alkali regions, the intrusion of saline water exacerbates the nutrient depletion in the plow layer, posing a significant challenge to agricultural productivity. Given the limited understanding of soil fertility in these areas and the inconsistent results among different assessment methods, this study aims to develop a more accurate and reliable soil fertility evaluation system. To achieve this objective, 108 topsoil samples were systematically collected from saline–alkali lands in Jiangsu Province. Several key soil fertility indicators, including soil pH, total salinity (TS), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK), were comprehensively evaluated. Four advanced methods, namely principal component analysis indexing–linear scoring (SQI_PCAL), principal component analysis indexing–nonlinear scoring (SQI_PCANL), modified Nemerow–linear scoring (SQI_NemeroL), and modified Nemerow indexing–nonlinear scoring (SQI_NemeroNL), were employed to conduct a multi-dimensional examination of soil fertility. Additionally, principal component analysis (PCA) was utilized to establish a minimum data set (MDS), which was then compared with the total data set (TDS) for a more precise assessment of soil fertility. Linear scoring methods (SQI_PCAL and SQI_NemeroL) had higher semi-variogram R² values compared to nonlinear methods. Moreover, under the SQI_PCAL and SQI_NemeroL evaluation methods, a strong correlation was observed between the TDS and MDS, with R² values reaching 0.63 and 0.65, respectively. Based on these findings, the SQI_NemeroL method, integrated with MDS, is recommended as an effective approach for soil fertility assessments in coastal saline–alkali regions in Jiangsu Province. This research not only enriches the theoretical understanding of soil fertility in such regions but also provides practical insights for sustainable agricultural management.