Ecological carrying capacity and sustainability assessment for coastal zones: A novel framework based on spatial scene and three-dimensional ecological footprint model

• Spatial scene is proposed to build a framework to assess coastal ECC/sustainability. • The application in coastal GBA uncovered its unsustainability with more details. • Results of our framework reflect better in economy and energy consumption structure. • Suggestions are proposed to promote the sustainable development of GBA coastal zone. The ecological carrying capacity (ECC) assessment in coastal zones is essential for sustainable coastal management, but there remains a lack of a more effective assessment method to be applied across broad contexts. In this study, we proposed the concept of spatial scene , a geographical unit with a coordinate position, and high unification in social-economic attributes, land cover, ecological function, and externalities, to substitute for the land use/land cover (LULC) in the traditional three-dimensional ecological footprint (EF 3D ) model, thereby establishing a novel framework for coastal ECC (CECC) assessment. The coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was chosen to examine the applicability and reliability of our framework. Results showed that the CECC estimated by spatial scene in the study area reached 0.1877 gha per capita, totaled 3.99 million gha in 2019, and the scenes of marine capture and forest provided the largest CECC. The per capita ecological footprint size (EF size ), ecological footprint depth (EF depth ), and EF 3D reached 0.1684 gha, 14.35, and 2.42 gha, respectively, representing unsustainable development in the GBA coastal zone. The EF 3D mainly distributed in scenes of grassland, forest, industrial, marine capture, coastal intertidal and offshore (CIO) port-shipping, traffic station, dryland, and CIO industrial-urban, while only the scenes of services and CIO tourism-entertainment were within CECC and therefore sustainable. Hong Kong, Huizhou, and Dongguan had the largest per capita EF 3D . Compared to our results, the CECC and EF size estimated by the traditional EF 3D model were respectively 18% and 6% lower, while their EF depth and EF 3D were respectively 21% and 13% higher, which should be attributed to the significant differences in classification standard and scale between spatial scene and LULC. Our results showed higher correlations and more significant relationships with total gross domestic product (GDP), marine GDP, and main energy EF than those based on the traditional LULC, indicating a better reflection of the economic development status, energy consumption structure, and marine economic development modes by our framework. It is recommended to accelerate the industrial transformation and upgrading, and strengthen the conservation of ecological, agricultural, and marine space, in order to promote the sustainable development of GBA coastal zone. Our study revealed that our framework is capable of serving as a more effective and accurate method for assessing CECC and sustainability. Graphical Abstract .