This paper attempts to reveal the impact and mechanisms of digital inclusive finance (DIF) on agricultural carbon emission performance (ACEP). Specifically, based on the provincial panel data in China from 2011 to 2020, a super slacks-based measure (Super SBM) model is applied to measure ACEP. The panel regression model and spatial regression model are used to empirically analyze the impact of DIF on ACEP and its mechanism. The results show that: (1) during the study period, China’s ACEP exhibited a continuous growth trend, and began to accelerate after 2017. The high-value agglomeration areas of ACEP shifted from the Huang-Huai-Hai plain and the Pearl River Delta to the coastal regions and the Yellow River basin, the provincial differences displayed an increasing trend from 2011 to 2020. (2) DIF was found to have a significant positive impact on ACEP. The main manifestation is that the development of the coverage breadth and depth of use of DIF helps to improve the ACEP. (3) The positive impact of DIF on ACEP had a significant spatial spillover effect, that is, it had a positive effect on the improvement of ACEP in the surrounding provinces. These empirical results can help policymakers better understand the contribution of DIF to low-carbon agriculture, and provide them with valuable information for the formulation of supportive policies.

Urban fringe is an active expanding belt, indicating urban-rural interaction processes. Previous studies have attempted to define urban fringe as the transitional area between urban and rural areas, but there is a lack of quantitative analysis of the periphery boundaries. We developed a novel, the Spatial Segmentation Model (SSM), to detect the extent of urban fringe via calculating the share of the built-up land. Within the urban fringe, we statistically compared the number of built-up patches in each direction and described four urban expanding patterns (stable, sprawling, leaping, and mixing patterns) indicated by the empirical analysis. The results show that this model can reliably detect the urban fringe and could reveal urban growth characteristics. We find the spatial territory changes are highly relative with transport infrastructures in Harbin. Meanwhile, the roads density in the urban core are higher than in the urban fringe. Especially for city roads, roads density in the urban core is more than 4 times higher than in the urban fringe. The growth of the urban fringe is closely related to the development of social economies as well as the space policies and development plans designed by governments. Similar to the post-industry cities worldwide, Harbin should take action to address population decline. Effective land-use and suitable urban growth strategies play an important role in alleviating urban shrinkage. Thus, understanding the dynamics, urban expanding patterns, and driving factors in the urban fringe can help us form a basis for future urban development.

Identifying urban production–living–ecological spaces and their interactive relationships is conducive to better understanding and optimizing urban space development. This paper took the main urban area of Hangzhou city as an example, and a two-level scoring evaluation model was constructed to accurately identify urban production–living–ecological spaces using point of interest (POI) data. Then, kernel density analysis, a spatial transfer matrix, and a bivariate spatial autocorrelation model were used to reveal the spatial patterns of urban production–living–ecological spaces and their interactive relationships during 2010 and 2019. The results showed that the proposed two-level scoring evaluation model combining both the physical area and density of POIs was effective in accurately identifying urban production–living–ecological spaces using POI data, with an identification accuracy of 88.9%. Urban production space was concentrated on the south bank of the Qiantang River and around the north of Hangzhou. Urban living space had the highest proportion, mainly distributed within the ring highway of Hangzhou in a contiguous distribution pattern, and urban ecological space was concentrated around West Lake and Xiang Lake. During 2010 and 2019, the expansion of urban production–living–ecological spaces had obvious spatial differences. Additionally, the mutual transformation between production and living spaces was more frequent during the study period and was mainly distributed within the ring highway of Hangzhou. There were significant positive spatial correlations between production and living and between living and ecological spaces, while a significant negative spatial correlation occurred between production and ecological spaces. The spatial correlations of urban production–living–ecological spaces revealed obvious spatial heterogeneity. This study proposed a two-level scoring evaluation model to accurately identify the spatial patterns of urban production–living–ecological spaces and their interactive relationships using POI data, which can provide detailed information and scientific references for urban spatial planning and management in rapidly urbanizing cities.

The farmland loss caused by urban–rural land development has exacerbated China’s challenges of using limited farmland to feed more than 1.4 billion people. Earlier studies shed light on the impacts of urban sprawl and rural settlement expansion, separately. However, there is little quantitative understanding of which one has more severe impacts on farmland and its net primary productivity (NPP). Thus, this study used spatially explicit satellite data including land-use maps and estimated NPP data, as well as spatiotemporal analysis methods to conduct a comparative analysis of farmland loss due to urban sprawl and rural settlement expansion at different scales from 2000 to 2020 in China. The results show that during the study period, urban sprawl resulted in a loss of 49,086.6 km² of farmland area and 8.34 TgC of farmland NPP, while the loss of farmland area and farmland NPP due to rural settlement expansion reached 18,006.8 km² and 3.88 TgC. The largest gap between the total area of farmland loss due to urban sprawl and the total loss area due to rural settlement expansion was 12,983.3 km² in Eastern China, while the smallest gap was 1291.1 km² in Northeastern China. The largest gap between the loss of farmland NPP due to urban sprawl and the total loss due to rural settlement expansion occurred in Eastern China at 1.97 TgC. Spatially, the total loss of farmland and its NPP due to urban sprawl and rural settlement expansion occurred mainly in the eastern and central regions of China; the areas of farmland loss by urban sprawl were more concentrated than that by rural settlement expansion. The negative impacts of urban sprawl on farmland area and its NPP were greater in southern China than that of rural settlement expansion. Noticeably, the loss of NPP per unit of farmland due to rural settlement expansion was higher than that by urban sprawl, especially in the Yangtze River Delta and Beijing–Tianjin–Hebei region. The results highlight the non-negligible impacts of rural settlement expansion on farmland in China. It is necessary to improve farmland protection policies by optimizing the spatial allocation of urban and rural construction land.