Abstract
The marine ecological economy has emerged as one of the most dynamic and promising sectors for economic expansion in coastal nations and regions. Its development is vital for fostering sustained, balanced and resilient economic growth. To enhance the precision of predictions and assessments of the marine eco-economic system, thereby supporting sustainable marine economic development, this study proposes a corrected discrete grey model (CDGM(1,1)) incorporating dynamic adjustment mechanisms. The model is further integrated with the driving force-pressure-state-impact-response (DPSIR) framework to establish a comprehensive forecasting and evaluation methodology. By introducing the concept of “grey effective information” and refining the probability accumulating generation operator (P-AGO), the approach effectively extracts critical information from data sequences. For datasets containing periodic patterns, a discrete GM(1,1) variant based on dynamic local accumulation is employed, allowing for combined forecasting. Finally, with the DPSIR framework and using the TOPSIS method, China’s marine economic data from 2010 to 2026 are analyzed. The prediction accuracy improved by 84% compared with traditional GM(1,1). The results demonstrate not only the effectiveness of marine ecological protection and economic development strategies but also elucidate the synergistic interplay of industrial growth, ecological feedback and policy regulation within the system.