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The negative impacts of urbanization-triggered land use changes on ecosystem services have attracted worldwide attention. However, the interactive relationships between land use structure and ecosystem services remain unclear, which is not conducive to sustainable regional ecological management. This study employed the information entropy and ecosystem services value assessment method to measure the land use structure and ecosystem services at the county level in China between 2000 and 2020, respectively. Then the spatial correlation analysis and decoupling model were used to explore the spatial and temporal interactive relationships between them, respectively. The results indicated that the average information entropy of land use structure (IELUS) and total ecosystem services value (ESV) in China both showed a continuous growth trend during the study period. ESV and IELUS were found to have a significant positive spatial correlation and were dominated by low-value agglomeration. The dominant types of the interactive relationship between IELUS and ESV were strong decoupling and expansion negative decoupling, and the number of counties with expansion negative decoupling decreased rapidly during the study period. These results suggest that differentiated land-use structure management policies ought to be adopted to protect regional ecosystem services.

Developing high-value crop planting industries according to local conditions is an important strategy for targeted poverty alleviation, and it is also a momentous solution for agricultural structure adjustment. However, previous studies mainly have focused on their spatiotemporal evolution and economic benefits instead of their environmental impact. An in-depth analysis of high-value crop planting industries is urgently needed. Taking Linquan County as the research area, this study applied emergy and economic analyses to explore the differences among different high-value crop planting systems and wheat planting system from the perspectives of economic benefit, environmental impact and overall sustainability. The results showed that high-value crop planting systems provide more competitive economic profit than that of wheat planting. Despite the high economic benefits, the three highvalue crop planting systems intensify the purchased nonrenewable resource inputs and lead to higher environmental stress, especially the ginger planting system. Under the trade-off of economic benefit and environmental performance, peony planting is considered a promising pattern for further sustainable agriculture considering its synergistic development of photovoltaic power generation. Several policy incentives are suggested to increase the financial attractiveness of environmentally sound patterns and promote a virtuous cycle for future sustainable agriculture.

Under the interaction between natural ecosystems and human interferences, farmland extends to multi-functions such as production, ecological, social, and cultural functions. Despite the exponential increase in research on the multi-functional evaluation of farmland in recent years, little study has been conducted at fine spatial and long-time scales. Furthermore, the existing quantitative analyses of multifunctional synergies and trade-offs in farmland mainly consider static spatial patterns and neglect dynamic information. Selecting the Chinese coastal province of Zhejiang as the study area, this study thus evaluated the spatio-temporal patterns of farmland functions from 2000 to 2020 at the county scale and introduced the trade-off/synergy degree (TSD) model to quantify the intensity of the relationships among functions. The results showed that farmland functional values and their relationships were significantly heterogeneous in spatial and temporal distribution. In addition to social function, the other functions all exhibited an increasing trend. Furthermore, strong correlations were mainly observed between production, ecological and cultural functions. Ultimately, five farmland zones were determined by the k-means clustering algorithm and considering both functional values and their relationships, and targeted suggestions applicable to each zone were put forward in this study. This study contributes to the utilization and planning of farmland and its surrounding land, especially to the improvement of the policy of returning farmland to forests.

Facility agriculture is an important initiative to adopt an all-encompassing approach to food and build a diversified food supply system. Understanding the evolution of facility agricultural land and the factors that drive it can contribute to the development of scientifically strategic agricultural planning and agricultural modernization. Therefore, this paper constructs a “situation-structure-behavior-value” theoretical framework; quantifies the relevant driving factors (physical, proximal, and socioeconomic) and their impacts on the development and layout of facility agriculture land by using a multivariate logistic regression model; and provides a strategy for optimizing land use. The results showed that the area of facility agriculture in Huzhou is rapidly expanding. Regarding drivers, facility agricultural land tends to be located in areas with higher slopes according to plot selection. Facility agriculture is more likely to develop in plots with convenient transportation and closer proximity to markets. At the economic level, economic efficiency, agricultural resource superiority, and policies significantly impact facility agriculture expansion. Finally, we propose three land use policy options to facilitate the sustainable development of facility agriculture. This study elucidates the underlying factors driving different types of facility agricultural land and offers methodological guidance for policy support, planning, control, and optimization strategies for facility agriculture.

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, and information flow information among sub-cities. Then, spatial visualization analysis, social network analysis, and geographical detector were applied to analyze the differences in spatial structure of multiple urban networks and influencing factors in Hangzhou metropolitan circle, respectively. The results showed that (1) the network connections of population, traffic, goods, and capital flows transcended geographical proximity except that of information flow, and population and traffic flow networks were found to be more flattened in Hangzhou metropolitan circle than in other urban networks; (2) the comprehensive urban network of Hangzhou metropolitan circle was imbalanced across sub-cities, presenting hierarchical and unipolar characteristics; and (3) the influence of traffic distance on the network spatial structure of Hangzhou metropolitan was stronger than the geographical distance, and the interactions between traffic distance and socioeconomic factors would further enhance the regional differentiation of the network spatial structure. This study could provide scientific reference for constructing a coordinated and integrated development pattern in a metropolitan circle.

Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.

Digital inclusive finance not only reduces the threshold of rural financial services but also plays an important role in promoting the rural economy. Under the background of the digital economy in China, we studied how digital inclusive finance affects the high-quality development of the rural economy. For this purpose, we assembled the provincial panel data from 2009 to 2018 and constructed an index system of rural high-quality development and then calculated its index using the entropy weight method. FE regression model and RE regression model were employed for empirical analysis, and we constructed a nonlinear regression model to investigate the nonlinear relationship as well. Furthermore, systematic GMM and iterative GMM were used to solve the endogenous problem. We found that digital inclusive finance plays a significant and positive role in promoting rural high-quality development, mainly through the channel of economic efficiency, urban and rural structure, green ecological development, harmony of people’s livelihood, and innovative development potential. Each subdimension of digital inclusive finance has a positive impact on rural high-quality development to different degrees. There is also a direct nonlinear relationship between the core variables, which act as a disincentive in the initial stage and boost after reaching a certain inflection point. Therefore, it is recommended to increase the construction of rural digital infrastructure, improve the digitalization of inclusive finance with the help of digital technologies such as cloud computing, and expand the breadth and depth of digital inclusive finance services in rural areas, especially in economically underdeveloped provinces and cities, to effectively promote the comprehensive and high-quality development of rural areas.

Rapid rural social-economic transitions in China have given rise to increasingly serious agricultural eco-environmental (AEE) problems. The deterioration of AEE poses great challenges to the sustainable agriculture and results in an incoordination among rural economy, society and ecology. Coordinating the relationship between rural transition development and AEE has great significance in agricultural sustainability and rural revitalization strategy. However, few studies focus on the interactions between social-economic transitions and AEE in rural areas. In this study, comprehensive index systems were built to assess the levels of rural transition development and AEE in Zhejiang Province, China. Coupling coordination model and Kuznets curve analysis were used to explore the interactive relationship between these two subsystems. The results showed that the level of rural transition development in Zhejiang Province showed an overall uptrend during 2000 and 2018, while the AEE evolved from a rapid decline to a stable trend. Both rural transition development and AEE level exhibited obvious spatial variability. The coupling coordination degree between rural transition development and AEE in the whole region fluctuated upward, and changed from the incoordination state to the basic coordination state. The spatial distribution of coupling coordination degree was characterized by high in the northwestern areas and low in the central and southeastern areas of Zhejiang Province, the majority of coupling coordination type transformed from lagging AEE to lagging rural transition development. The dynamic evolution of the relationship between rural transition development and AEE displayed a U-shaped curve and presented different characteristics at different aspects of rural transition process. These results will provide effective references for promoting the coordinated development between rural social-economic transitions and AEE, and formulating targeted rural revitalization policies to achieve rural sustainability.

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.

The metropolitan circle is the basic unit of regional competition. Enhancing the connection between cities in the metropolitan circle and optimizing the spatial layout of the metropolitan circle is one of the goals of regional high-quality development in the new era. Therefore, it is of great significance to analyze the spatial network structure of the metropolitan circle. Taking Hangzhou metropolitan circle as an example, this study used web crawler technology to obtain data in multiple Internet big data platforms; used centrality analysis, flow data model, and social network analysis to construct the network connection matrix of human flow, goods flow, capital flow, information flow, and traffic flow; and explored the spatial network structure of the metropolitan circle. The results showed that the node intensity of the metropolitan circle presented a distribution pattern of strong in the east and weak in the west. The network connections of each county under the action of different element flows were different, and the skeleton of the integrated flow network connections showed a starfish-shaped feature. Hangzhou, Jiaxing, Huzhou, and Shaoxing cities had strong group effects in goods flow and traffic flow, while Quzhou and Huangshan cities had relatively independent cohesive subgroups in human flow and information flow. This study can provide useful references for regional development and spatial planning implementation.

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 scientific evaluation of landscape fragmentation and connectivity is important for habitat conservation. It is strongly influenced by the spatial resolution of source maps, particularly in urban environments. However, there is limited comprehensive investigation of the spatial grain effect on urban habitat and few in-depth analysis across different urban gradients. In this paper, we scrutinize the spatial grain effects of urban green space (UGS) cover maps (derived from remote sensing imagery and survey data) with respect to evaluating habitat fragmentation and connectivity, comparing among different urban gradient scenarios (downtown, urban periphery, and suburban area) in Hangzhou, a megacity in China. The fragmentation was detected from three indices, including Entropy, Contagion, and Hypsometry. Then morphological spatial pattern analysis (MSPA) was applied for the landscape element identification. The possibility of connectivity (PC) and patch importance (dPC) were proposed for measuring the landscape connectivity based on Cores and Bridges from MSPA results. The results indicate that the farther the location is from downtown, the less sensitive the landscape element proportion to the spatial resolution. Among the three fragmentation indices, the overall hypsometry index has the lowest sensitivity to the spatial resolution, which implies this index’s broader application value. Considering connectivity, high spatial resolution maps are appropriate for analyzing highly heterogeneous urban areas, while medium spatial resolution maps are more applicable to urban periphery and suburban area with larger UGS patches and less fragmentation. This study suggests that the spatial resolution of UGS maps substantially influence habitat fragmentation and connectivity, which is critical for decision making in urban planning and management.

In the context of ensuring national food security, high-intensity agricultural production and construction activities have aggravated the conflicts between agricultural and ecological spaces in ecologically fragile areas, which have become one of the most important factors hindering regional sustainable development. This study took Lin’an District, a typical hilly region of southeastern China, as an example. By constructing a landscape ecological risk evaluation model, land-use conflicts between agricultural and ecological spaces were identified, spatial autocorrelation and topographic gradient characteristics were analyzed, and land-use conflict trade-off mechanisms were proposed. During 2008 and 2018, the degree of land-use conflict in Lin’an District displayed an increasing trend, and the proportion of severe conflicts increased obviously. Slope is the main factor affecting land-use conflicts in a hilly region and shows a negative correlation, mainly because areas with flat terrain are more conducive to human activities. Based on the characteristics of land-use conflicts in Lin’an District, conflict trade-off mechanisms were proposed to provide a theoretical basis and practical support for land-use conflict management. Our study provides scientific evidence for sustainable land-use planning and ecological management in ecologically fragile areas.

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Exploring impacts of urban expansion on ecosystem services has become a hot topic for regional sustainable development, while analyzing the ecological effects of urban expansion forms under different expansion intensities and city sizes is relatively rare. Therefore, taking a typical urban agglomeration, Shanghai-Hangzhou Bay Urban Agglomeration, as a case study, this study first analyzed the dynamics of urban expansion forms (leapfrogging, edge-expansion, and infilling) and four critical ecosystem services (carbon sequestration, food supply, habitat quality, and soil retention) in three periods from 1990 to 2019. The multiple linear regression model and zonal statistics analysis model were used to quantitatively identify the impacts of urban expansion forms on ecosystem services, taking into account different expansion intensities and city sizes. The results showed that the urban expansion trend in the study area experienced a morphological change from integration to diffusion and then to integration in 1990–2019; edge-expansion was the dominant expansion form. Food supply decreased continuously while other ecosystem services had fluctuating changes, and they all had spatial heterogeneity. The leapfrogging, edge-expansion, and infilling all had negative impacts on ecosystem services, and among them, the edge-expansion intensity had the highest influence degree in the early expansion, and the leapfrogging intensity occupied the dominant position in all influences with the expansion of urban scales. For different city sizes, the impact of edge-expansion in large-scale cities was greater than in small-scale cities in the early expansion, and the impact of leapfrogging in large-scale cities exceeded the edge-expansion in the subsequent expansion. These findings will help further understand the influential mechanisms between urban expansion and ecosystem services and provide a scientific basis for formulating reasonable urban planning.

Understanding the spatiotemporal dynamics of bamboo forests is of critical importance as it characterizes the interaction between forest and agricultural ecosystems and provides essential information for sustainable ecosystem management and decision-making. Thus far, the specific dynamics of moso bamboo (Phyllostachys edulis) and other bamboo are still unknown. In this study, we used temporal information extracted from Landsat time series stacks with Google Earth Engine (GEE) to characterize the spatiotemporal dynamics of bamboo forests, including moso bamboo and other bamboo, in Lin’an County, China, from 2000 to 2019. The bamboo forests were mapped in four periods: the early 2000s (2000–2004), the late 2000s (2005–2009), the early 2010s (2010–2014), and the late 2010s (2015–2019). The overall accuracy of these maps ranged from 97% to 99%. We then analyzed the spatiotemporal dynamics of the bamboo forests at the county and subdistrict/township scales, and probed the bamboo forest gain and loss with respect to the terrain features. Our findings show that bamboo forests increased by 4% from 2000 to 2014, followed by a sharp decrease of 13% in the late 2010s. The decrease was mainly caused by the loss of other bamboo. Approximately 69% of the bamboo forest gain occurred in non-bamboo forest areas, and the rest occupied non-forest areas. Bamboo forest loss was mainly due to conversion into orchard (59%) and forest plantation (22%). Compared to bamboo forest gain, bamboo forest loss was typically observed in areas with lower elevations and steeper slopes. Our study offers spatially explicit and timely insight into bamboo forest changes at the regional scale. The derived maps can be applied to study the drivers, consequences, and future trends of bamboo forest dynamics, which will contribute to sustainable ecosystem management.