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Ecological quality assessment (EQA) is important for regional socio-economic development and its sustainability. To assess the status of land ecological quality more precisely, an ecological quality assessment system with 11 indicators of ecological stability, ecosystem service function, and habitat stress was established using the analytic hierarchy process for Guangdong Province, a highly urbanized region of China. Remotely sensed data were mainly used to quantify the 11 indicators and acquire regional EQA graphs at high spatial resolution. In addition, we used the spatial autocorrelation measure Moran’s I to capture dynamic signatures of spatial organization of ecological quality in the study area. The results show that the ecological quality of the study area is heterogeneous spatially but relatively consistent in different regions. Significant positive spatial autocorrelation for EQI in Guangdong was revealed by global Moran’s I. Potential ecological hot spot or cold spot were detected based on the spatial clustering patterns that were obtained by local Moran’s I. Lands with low ecological quality is mainly distributed in economically developed areas such as the Pearl River Delta and coastal cities in eastern and western Guangdong, while those with high ecological quality are mostly situated in northern mountainous areas that have lush vegetation. The low assessment scores for Guangdong, especially for the Pearl River Delta, are highly correlated with large populations and degrees of industrial agglomeration; this is mainly because urbanization and economic development jeopardize the environment. The presented case study can facilitate information provision and targeted strategy making for environmental protection. This study provides a helpful approach to assess and to analyze the ecological status in the future research. In contrast with methods that employ a single metric and limited data, the assessment system proposed in this study expands the potential application of the remotely sensed data and enriches the methodological system for EQAs.

Habitat quality is an important indicator for evaluating the biodiversity provided by ecosystem. Estimating and scenario-simulating the dynamic evolution and future development trends of habitat quality under the influence of land-use change is significant in regional biodiversity conservation, formulating land-use planning, and maintaining the ecological environmental sustainability. In this article, we included the Huaihe River Economic Belt as the area of study because of its vital location in China and applied the CA–Markov and InVEST models to analyze the spatio-temporal evolution of habitat quality and to simulate the future development trends of habitat quality under three different land-use scenarios: fast urban growth scenario, farmland conservation-oriented scenario, and ecological conservation-oriented scenario. The results showed that the land-use change in the Huaihe River Economic Belt was mostly represented by the continuous increase of the built-up area, whereas other land types all declined in area from 1995 to 2015. The land-use changes under these three abovementioned alternative future scenarios with different development orientations were considerably different. The built-up area has been shown to expand rapidly to occupy other land types on a large scale under the fast urban growth scenario. Urban land increased slightly and a large area of rural residential land would be converted into farmland under the farmland conservation-oriented scenario. The built-up area and farmland might decrease while woodland, grassland and water would increase in extent of areas under the ecological conservation-oriented scenario. Habitat quality has been shown to be generally poor, continuing to decline from 1995 to 2015, while its spatial distribution was higher in the southwest and northeast areas and lower in the central regions. The future habitat quality would display a downward trend under the fast urban growth and farmland conservation-oriented scenarios with a further deterioration of the ecological environment, while the ecological conservation-oriented scenario predicted the converse trend that the ecological environment would be improved productively. This study may be useful for understanding the impact of land-use dynamics on biodiversity. The research results can provide a scientific basis for the decision-makers to formulate biodiversity conservation and land management policies.

Climate change has emerged as a critical global issue. Land-use/cover change (LUCC) plays a pivotal role in influencing terrestrial ecosystem carbon cycles and further regulates carbon emission intensity by reshaping the spatial characteristics of landscape patterns. Taking 300 Chinese cities as the study area, an analytical framework encompassing carbon emission accounting, regional land-use landscape pattern analysis, spatiotemporal correlation between landscape patterns and carbon emissions, and economic “core-periphery” disparities was presented. The land-use carbon emissions and landscape pattern indices of each city from 2005 to 2020 were calculated, and the geographically weighted regression (GWR) model was employed to examine the impact of land-use landscape pattern changes on carbon emissions from an urban perspective. Furthermore, the cities were categorized into developed and underdeveloped groups based on the median per capita GDP to compare how economic development levels moderate this impact mechanism. The results indicate that the relationship between landscape patterns and carbon emissions exhibits significant spatial heterogeneity, highlighting the complexity of the influence of land-use morphology on carbon emissions. Sustainable land-use strategies must account for regional disparities in economic levels, planning capacity, and administrative characteristics rather than pursuing a uniform urban form. Economic development significantly moderates the carbon mitigation effects of landscape patterns through its influence on spatial governance capacity, leading to pronounced differences between cities at varying development levels. Moving forward, regionally tailored approaches that integrate landscape optimization with industrial transformation and ecological conservation should be prioritized to provide spatial solutions for achieving the carbon peaking and carbon neutrality goals.

Rail transit is a significant measure for the comprehensive development of large cities, which influences the land use patterns and the spatial patterns of residential prices around the rail. This study considered Beijing Metro Line 10 and Line 13 as examples, based on a semi-logarithmic hedonic price model, combined with facility point-of-interest (POI) data and residential unit transaction data, to study how rail transit affects the spatial differentiation of urban residential prices. Within the 2 km study area along the line, factors such as community grade (property fee), living environment (park), and living convenience (shopping mall) significantly affected the residential prices. Factors influencing residential prices in different rail locations also differed. The residential prices within the fourth ring (Line 10) were correlated significantly with population density (plot ratio) and station distance, while residential prices outside the fourth ring (Line 13) were correlated with community environment (greening rate), community-built time (age of residence), and public transportation conditions. The conclusions of this study are as follows: (1) Within the urban area of a single central city, the average residential price on the inner side of the rail transit line adjacent to the city center is higher than on the outer side. (2) Neighborhood characteristics significantly affect residential prices along rail transit lines in urban areas, while the architectural and neighborhood characteristics have equally important effects on residential prices along suburban rail transit lines. (3) Urban residential patterns affect residential prices along rail transit lines, with rail transit in urban areas having lesser value-added effect on areas with higher residential prices and suburban rail transit having higher value-added effect on areas with lower residential prices. The innovation of this study is to analyze the spatial differentiation from two location perspectives: the residential price pattern of the city and the city’s own ring division, and to add new location characteristic variables at a unit distance of 200 m. This study confirms that the spatial effect of rail transit on residential prices in different locations of the same city is not the same, and it also provides policy suggestions for strengthening the combination of Transit-Oriented-Development (TOD) model and the layout of residential land.

The Han River Basin, a critical water conservation and ecological barrier in Hubei Province, is intricately associated with the United Nations Sustainable Development Goals (SDGs). Research results show that vegetation cover changes are affected by multiple factors, and understanding the influences of climate change and human activities on vegetation is imperative for achieving sustainable development in the basin. Through quantitative assessment of vegetation changes in diverse landform regions, implementing adaptive ecological construction and environmental protection will foster the sustainable development of ecological civilization in the Han River Basin. This study utilizes MODIS13Q1 data and employs diverse analytical methods to investigate the characteristics of vegetation change and the interrelationships between climate change, meteorological factors, and vegetation cover in various geomorphological areas of the Han River Basin from 2000 to 2020. The results showed that (1) throughout the entire study period, the NDVI of the six types of geomorphological divisions in the Han River Basin exhibited a fluctuating upward trend, with the changes in the low-altitude hilly geomorphic regions being particularly noteworthy. (2) Within the study area, approximately 92.67% of vegetation coverage displayed an increasing trend, while 7.33% showed degradation, predominantly in plains and platforms. Notably, the area of continuous improvement (31.16%) outweighed the area of continuous degradation (3.05%), with low and middle-relief mountain areas demonstrating the most robust growth and sustainability. (3) Human agriculture activities and urbanization processes have emerged as the primary driving force behind vegetation changes in the Han River Basin. The responses of vegetation to climate change and human activities exhibited significant variations across diverse geomorphological regions. In areas characterized by vegetation improvement, the contribution rate of human activities to NDVI changes in different vegetation types surpassed 70%, with plain areas displaying the highest contribution rate at a remarkable 90%. In contrast, the plain and platform regions of the vegetation degradation area were significantly influenced by climate change. In future watershed ecological environment management, it is essential to not only recognize the dominant role of human activities in promoting the growth of mountain vegetation NDVI but also address the impact of climate change on the degradation of vegetation NDVI in plains and platforms. A comprehensive understanding of these factors is crucial for devising effective strategies to ensure sustainable development and ecological balance in the Han River Basin.

Rapid urbanization and drastic land-use change have led to landscape fragmentation and ecological environment deterioration in the regions along the Grand Canal. Building an ecological network is an important means to improve the connectivity of habitat patches and carry out ecological protection and restoration of territorial space, which is of great significance to ensure regional biodiversity and ecological security. In this article, we took the Huaiyang Section of the Grand Canal (Huaiyang Canal) as the study area, used the ecosystem service assessment model, morphological spatial pattern analysis (MSPA), and the landscape connectivity evaluation method to identify ecological sources, then used the minimum cumulative resistance (MCR) model and the gravity model to extract and grade ecological corridors. Based on these, the ecological network was constructed by combining the identification method of ecological nodes and ecological breakpoints. The aim of this was to provide a reference for the ecological space optimization of Huaiyang Canal and even the entire Grand Canal, the formulation of an ecological protection plan, and the implementation of territorial space ecological restoration. The results showed that the spatial distribution of the water conservation service, soil conservation service, carbon sequestration service, and biodiversity conservation service were significantly different, and the level of ecosystem services showed a trend of continuous degradation from 1990 to 2018. There were 12 ecological source patches comprehensively identified by multiple methods, with a total area of 2007.06 km2. In terms of spatial distribution, large ecological source patches were mainly distributed in the central and western areas adjacent to the Grand Canal, while small ecological source patches were scattered in the eastern and southern border regions of the study area. The total length of ecological corridors was 373.84 km, of which the number of the primary ecological corridor, secondary ecological corridor, and tertiary ecological corridor were 9, 7, and 7, respectively, and the suitable width of the ecological corridor was 200–400 m. After optimization, the proposed ecological network was composed of 3 key ecological source patches, 9 important ecological source patches, 23 terrestrial corridors, 10 aquatic corridors, and 18 ecological nodes. Twenty-nine ecological breakpoints were key areas requiring ecological restoration. The overlap rate of the integrated ecosystem service change area and land-use change area was 99%, indicating that land-use change has a significant impact on regional ecosystem services. This study is of great significance for carrying out the ecological protection and restoration of the Huaiyang Canal and adjusting local land-use policies. It also provides a typical case demonstration for identifying an ecological network and formulating ecological restoration planning for other sections of the Grand Canal and cities along the canal.

Regional agricultural carbon balance studies are crucial for promoting coordinated development and achieving carbon neutrality. This research quantifies agricultural carbon emissions and sinks across 190 counties in the Beijing–Tianjin–Hebei (BTH) region from 2013 to 2022. The methodology involved the carbon emission factor approach and crop productivity models to assess the agricultural carbon balance. Furthermore, this study employed the carbon load model and the carbon output technical elasticity model to analyze the per-unit contribution of agricultural products and the carbon implications of augmented agricultural output. The findings reveal the following: (1) Total agricultural carbon emissions followed a fluctuating, inverted “U”-shaped trajectory, peaking in 2015. Emission reductions were primarily driven by decreases in agricultural energy consumption and land utilization, followed by crop cultivation. Conversely, agricultural net carbon sequestration and the carbon offset ratio show a fluctuating upward trend. (2) The agricultural carbon balance exhibits a distinct north–south differentiation. There has been a year-on-year reduction in carbon deficit counties, while the spatial aggregation of the carbon balance has become increasingly pronounced over time. (3) The marginal contribution of the agricultural carbon balance across the five primary agricultural zones positively correlates with the scale of agriculture and the prevailing crop cultivation regimes. Specifically, the Central Hebei Plain agricultural zone demonstrated the highest contribution, while the Daming agricultural zone exhibited the lowest. (4) Driven by the synergistic effect of internal and external factors, the BTH region has optimized its production elements. This has led to an elevated agricultural carbon balance and reduced inter-regional disparities. The region’s agricultural carbon balance demonstrates a favorable trajectory, suggesting sustainability under a low-carbon development paradigm. This study offers sustainability recommendations based on four pillars: establishing rigid production systems, enhancing compensation and trading mechanisms, optimizing industrial structures and integration strategies, and reinforcing regional coordination and incentive frameworks.

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

Due to limited land resources, it is necessary to balance urban economic development and efficient land use. Clarifying the relationship between the two is crucial to improving both economic efficiency and land use efficiency. Considering the undesirable output of urban land use, this paper adopts a super efficiency SBM model to quantify the urban land use efficiency (ULUE) of the Beijing–Tianjin–Hebei (BTH) region from 1999 to 2019, and analyzes the relationship between ULUE and economic development level (EDL) by combining the Tapio model and the environmental Kuznets curve (EKC) model. The results show the following: (1) During the study period, the ULUE showed a fluctuating upward trend on the temporal scale, with the lowest and highest inflection points occurring in 2002 and 2018, respectively, and a distribution pattern of “high in the southeast and low in the northwest” on the spatial scale. (2) The decoupling relationship between ULUE and EDL showed repeated fluctuations between decoupling and coupling states on the temporal scale, but the overall showed a transition trend from decoupling state to coupling state. On the spatial scale, from north to south, there were a strong decoupling state (SDS), weak decoupling state (WDS), strong decoupling state (SDS), and weak decoupling state (WDS) in order, showing a regular interval repetition distribution pattern. (3) The relationship between ULUE and EDL showed an EKC “U-shaped” curve, that is, ULUE decreases first and then increases with the increases in EDL. The results of this study can provide a reference for the coordinated and sustainable development of the BTH region.