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In recent decades, rapid urbanization and intensified global climate change have resulted in a significant difference of environment and resources distribution on space, which would cause trouble for accurate assessment of regional ecological sustainable development, especially in the large urban agglomerations. The parameters used in previous assessment methods have normally ignored spatial heterogeneity, leading to deviations in the evaluation accuracies against the context above. By incorporating remote sensing technology, this study proposed an improved emergy ecological footprint (EEF) method and a novel ecological sustainability index to comprehensively analyze the variability of ecological security states (ESS) from 1994 to 2018 in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to predict its sustainable growth potential based on a combined factorial decomposition and scenario analysis. Results showed that the pixel-based emergy analysis revealed significant heterogeneity over time and space under the impact of climate change and intense land use activities during the study period. The emergy carrying capacity per capita (e c c) and the emergy ecological footprint per capita (e e f) also showed a significant difference between the nine cities in the GBA. In addition, the traditional EEF method, which does not consider the spatiotemporal variation, has indeed overestimated the GBA's e c c by 15% compared with our results. The ESS of the GBA gradually worsened from slight insecurity in the 1990s to moderate insecurity in 2018. If the current trends in socio-economic activities and climate change continue according to the RCP8.5 scenario in the IPCC, the ESS of the GBA will reach the extreme insecurity state in 2050. However, our scenarios show that industrial structure adjustment, energy structure optimization, and especially biological resource conservation can reduce the EFI by approximately 6.52%, 23.4%, and 30.6%, respectively. Consequently, effective implementation of the above measures can limit the increase both in emergy ecological deficit and emergy ecological footprint intensity (EFI) and, together, contribute to a higher security status in the GBA in 2050. [Display omitted] • We improved emergy ecological footprint method (EEF) by incorporating remote sensing technology. • Pixel-based emergy analysis revealed significant heterogeneity over time and space. • We proposed a novel classification method to evaluate the ecological security states (ESS). • The ESS of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) gradually worsened in 1994–2018. • Energy structure optimization and biological resource conservation are effective to improve ESS. [ABSTRACT FROM AUTHOR]