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As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA. [ABSTRACT FROM AUTHOR]

Vegetation variation is one of important indicators to evaluate the quality of ecological environment and measure the status of surface vegetation. Exploring the spatio-temporal dynamic variation of vegetation and its influencing factors is of great significance for the protection of ecological environment and the restoration of vegetation. Based on MODIS LAI satellite remote sensing products, meteorological data and socio-economic data, the spatial-temporal variation of LAI and its influencing factors in Guangdong province from 2003 to 2020 were explored through Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index analysis and geographic detector. The results show that the variation of LAI annual mean exhibits a fluctuating upward trend(0.219/10a)from 2003 to 2020, and the LAI seasonal average in the descending order are winter(0.405/10a), spring(0.251/10a), autumn(0.139/10a)and summer(0.111/10a); spatially, high values of LAI are distributed in the northwest, while low values are distributed in the southeast, mainly in the Pearl River Delta and coastal economic zone. LAI showes a weakly sustainable and weakly anti-sustainable trend in the future. Moreover, the area with sustainability and improvement is larger than the area showing stability and degradation, indicating the strong possibility of improvement for vegetation in Guangdong province in the future. The main influencing factors of LAI in Guangdong province are elevation, population density and GDP, of which elevation has the most impact with an explanatory power of 16.9% for LAI spatial distribution. Moreover, the interaction of two factors could enhance the explanatory power of LAI spatial distribution in Guangdong province. [ABSTRACT FROM AUTHOR]

Resilience evaluation is an important foundation for sustainable rural development. Taking the 57 counties in Guangdong province as examples, this study used the CRITIC method to construct a comprehensive evaluation index system for rural resilience and identified the main influencing factors and their spatial heterogeneity on the basis of the geographical detector method and multiscale geographically weighted regression. The results showed that: (1) Most of the counties in Guangdong province had medium or higher values of comprehensive resilience, and the high-value areas were mainly located in the Pearl River Delta region. (2) The comprehensive resilience and each dimensional resilience measure exhibited significant positive spatial correlations. (3) The geographic detector results showed that the per capita gross regional product and the number of industries above the scale were the main influencing factors for rural resilience, and each influencing factor had an enhanced effect after interaction. (4) The effect of each factor on rural resilience demonstrated spatial heterogeneity. Specifically, the proportion of secondary and tertiary industries showed negative effects in some counties in eastern and northern Guangdong and positive effects in other counties. [ABSTRACT FROM AUTHOR]

This study aims to clarify the grain production efficiency in western Guangdong and its key influencing factors and to optimize the efficiency of resource allocation, promoting the transformation and enhancement of grain production. Based on the grain production data of three cities in western Guangdong, from 1999 to 2018, a cross efficiency DEA model was employed to calculate the regional grain production efficiency and reveal almost two decades of temporal and spatial evolution characteristics of the grain production efficiency. The key influencing factors of the grain production efficiency were compared and analyzed by using the Tobit model and geographical detector model. Results showed that the average grain production efficiency in western Guangdong was 0.77; Maoming City exhibited the highest efficiency（0.88）, followed by Yangjiang City（0.78）and Zhanjiang City（0.65）. The most important input factor（contribution）of the Tobit and geographical detector models was the total power of agricultural machinery（- 1.082 and 0.942, respectively）. However, the Tobit model demonstrated that the seven input factors affecting the grain production efficiency were positively correlated only with the amount of chemical fertilizer applied（0.015）; that the effective irrigated area, grain sown area, and pesticide use amount were not correlated with the grain production efficiency; and that the remaining factors were negatively correlated. All factors measured by the geographical detector had a significant influence on grain production efficiency. The contribution of input factors in Yangjiang was similar to that in western Guangdong Province, but the contribution of the amount of chemical fertilizer was 0.008 higher than that of the effective irrigation area. The contribution of chemical fertilizer and pesticide input in Zhanjiang and Maoming was second only to the total power of agricultural machinery and the contribution of effective irrigation area was the lowest. However, the contribution of the area in which grain was sown in Maoming was higher than that of the employed population and agricultural plastic film, whereas Zhanjiang showed the opposite trend. In general, the grain production efficiency of western Guangdong during the past 20 years has not been high, and has remained at a medium level. The data demonstrate that grain production efficiency decreases first and then increases significantly. There are no insignificant factors in the measurement results of the geographical detector and the influence of detection factors is more stable and less biased than in the Tobit model. The total power of agricultural machinery is the leading factor affecting the grain production efficiency of the three cities in western Guangdong, but the contribution of the factors in different cities is quite different，Zhanjiang and Maoming are more dependent on the chemical fertilizer and pesticide pollution [ABSTRACT FROM AUTHOR]

The ecological and environmental quality of Dongjiang river watershed has great influence on Guangdong, Hong Kong and Macao. The landscape ecological risk assessment model could effectively monitor and assess environmental quality. In this study, spatial autocorrelation and geographic detector methods were used to explore the spatial characteristics of landscape ecological risk and their driving factors in the Dongjiang river watershed for four decades. The results showed that the ecological risks of Dongjiang River Source Watershed are mainly classified as low and intermediate, which are distributed in the hilly regions and the marginal mountainous regions at the junction of the Xunwu and Dingnan counties. From 1980 to 2018, the area of regions with the low ecological risk increased by 587.01 km 2. The size of regions with moderate, high and severe ecological risk decreased by 165.6 km 2, 258.82 km2 and 162.58 km2, respectively. Moreover, landscape ecological risk values exhibited an apparent spatial dependency, and high-risk areas cluster together. Among influencing factors, population density has the most significant impact on the change of landscape ecological risk in the Dongjiang river watershed, followed by elevation (DEM), human interface, vegetation index (NDVI), and urbanization level. However, the interaction of driving factors has a greater impact on the ecological risk of the Dongjiang river watershed than a single driving factor. The research provides good knowledge for environmental quality management, and the proposed methods can be used for other regions. [Display omitted] • The ecological risks of Dongjiang River source watershed were investigated for 40 years. • Ecological risk model was constructed. • Driving factors affecting distributions of ecological risk were determined. • Population density and elevation are the most influencing factors. • Ecological risk pattern showed obvious spatial dependency. [ABSTRACT FROM AUTHOR]