The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is rich in natural and marine resources, and it is scientifically valuable to study the evolution patterns and driving mechanisms of the ecosystem service values (ESVs) of the GBA for the governance and conservation of its ecosystems. Based on the land use changes in the GBA from 2000 to 2020, the ESVs in the GBA were measured at the grid scale, and the Markov model was used to predict the ESVs in 2030; the calculated results were used to analyze the spatial and temporal variation characteristics of the ESVs during the 30-year period, while the driving role of the topographic factors on the ESVs is revealed through the construction of the geographically weighted regression model (GWR). The results show the following: (1) During the 20-year period, the area of arable land and water in the GBA fluctuated greatly, with the area decreasing year by year and shifting mainly into construction land; in terms of shifting the center of gravity of the land, and the center of gravity of the grassland and unused land shifted the greatest distance due to the expansion of construction land, with the center of gravity shifting westward as a whole. (2) The ecosystem services (ESs) in the GBA show obvious aggregation in the spatial distribution, with the total ESVs decreasing year by year. Among them, the areas with an increasing total value are mainly located in the cities of Zhaoqing and Huizhou in the GBA, accounting for 27%, and the areas with a decreasing total value year by year are mainly located in the dense urban areas in the central part of the GBA, accounting for 35%, and the area is increasing, indicating that the habitat quality is deteriorating, and the model prediction shows that the value of ecosystem services in 2030 have a decreasing trend under the development of the natural state. (3) Topographic factors have a significant influence on the ESVs, and in terms of spatial distribution, the areas with the strongest effect are distributed in the northwestern and northeastern parts of the GBA, and the main uses for the land are wood land, arable land, water and the area of the water–land intersection near the sea.

Landscape connectivity refers to the degree of continuity between the spatially structured units of a landscape. Ecological connectivity can characterise the degree to which ecological functional areas are connected in terms of function and ecological processes. In this study, the landscape pattern index and ecosystem service values were used to evaluate the ecological functional resistance of each landscape type, taking the Three Parallel Rivers Natural World Heritage Site as an example and the habitat distribution and population size of the Yunnan snub-nosed monkey as a reference. The minimum cost distance model, combined with the barrier impact index (BEI) and ecological connectivity index (ECI), was used to determine the degree of barrier impact on the study area and the ecological connectivity of the core reserve of the heritage site in both 2000 and 2020. The resistances of the different land types and landscape heterogeneity to the ecological function of species migration between the core protected areas of the heritage site were, in descending order, those of the forest, shrubs and grass, water, unused land, cultivated land, and built-up land. In 2020, the study area had a large BEI, with areas such as built-up areas, major roads, the sides of large rivers, and arable land being significant contributors to the blockage of landscape connectivity. The overall landscape connectivity in the study area was generally low, with clear spatial differentiation and a three-column parallel distribution pattern influenced by the topography and landscape. With the adjustment of the core reserve boundaries of the heritage site, the proportion of areas with high connectivity (ECI = 4–5) increased from 11.31% in 2000 to 34.36% in 2020. This increased landscape connectivity was conducive to the migration and reproduction of large terrestrial animals, such as the Yunnan snub-nosed monkey, with increasing numbers of populations and individuals. This study provides theoretical and methodological insights into the delineation and conservation of natural heritage sites and landscape connectivity.

An emergy analysis is used to assess the sustainability of urban agglomerations’ eco-economic systems, which are generally measured by emergy–value sustainability indicators using a combination of several system indicators. However, this assessment approach is not applicable to economically developed high-density urban agglomerations. The application of the traditional entropy value evaluation method needs to be expanded to further strengthen the sustainability of the complex eco-economic–social relationships in megacity cluster regions. In this study, taking the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) as a case study, we study a new evaluation method for evaluating the sustainable development capacity of cities. This method is based on the entropy power method and is used to construct the evaluation system of all indicators of the social–economic–natural subsystems of the eco-economic system, and it couples the development degree with the coordination degree. (1) This study shows that the new method is applicable for the sustainability assessment of high-density megacity clusters and is more accurate and comprehensive. The sustainability rankings are provided for Zhaoqing, Jiangmen, Huizhou, Guangzhou, Macau, Foshan, Zhongshan, Dongguan, Zhuhai, and Shenzhen. Hong Kong is the most representative, with a high sustainability index, but has the lowest level of coordination and a clear incoherence within the system. (2) The current emergy structure of the GBA city cluster is extremely unreasonable. The GBA city cluster is a resource-consuming city with a common characteristic of a low level of coordinated development. Although urban clusters have some potential in terms of renewable emergy and resources, the recycling rate of waste is low, and the consumption rate of nonrenewable resources is high. The effective use of land resources has become an important factor in the bottlenecking of sustainable development, and all other cities face such problems, except Zhaoqing, Jiangmen, and Huizhou. (3) The GBA city cluster can be divided into three categories according to the new method. Category 1 mainly includes Hong Kong, Shenzhen, Dongguan, and Zhuhai, which have coordinated development degrees ranging between 0.0 and 0.135 and the highest emergy density (ED) values but are extremely dependent on external emergy. They have high levels of emergy use per capita (EUC), high living standards, and high quality of life. The effective use of land resources severely restricts sustainable economic development, resulting in extreme ecological and environmental carrying pressure. Category 2 includes Guangzhou, Macau, Foshan, and Zhongshan, whose coordinated development degrees range from 0.143 to 0.179. The sustainable development capacity of these cities is at the middle level amongst the whole GBA. Their main emergy characteristics are emergy flow and subsystem evaluation indices that are between category 1 and category 3, but each has its own characteristics. The category 3 cities include Zhaoqing, Jiangmen, and Huizhou, whose coordinated development degrees are between 0.192 and 0.369. These cities are characterized by relatively low ED and EUC values, living standards, and quality, but their land resources have certain potential. These cities have a high emergy self-sufficiency rate (ESR) and natural environmental support capacity, but their environmental loading ratio (ELR) is still much higher than the national average. In terms of the economic development and innovation development levels, these cities are ranked as category 1 > category 2 > category 3. In terms of the ecological and environmental conditions and blue–green space protection, these cities are ranked as category 1 < category 2 < category 3. The results of this study can provide cities in the GBA with more scientific and consistent directions for the coordinated development of their ecological–economic–social systems to provide sustainable development decision-making services for megacity cluster systems.