Context: The intensification of land use changes in coastal cities has been a result of the ongoing development of the social economy. A decrease in the ecosystem service value (ESV) and terrestrial carbon storage (TCS) of coastal cities has been observed as a result of the intensification of urbanization and climate change. However, it is unclear whether the influence of land use change on ESV and TCS in coastal towns would be facilitated or hampered under various growth scenarios. Aim: This study simulated the impact of land use change on the ESV and TCS of coastal cities under various future development scenarios and provided scientific policy references for the preservation of their ecological functions. Approaches: The InVEST model and PLUS model were employed to predict the land use changes in coastal cities in Liaoning Province from 2030 to 2060 under various development scenarios, based on the land use change data of three periods from 2000 to 2010 to 2020. The changes in ESV and TCS in coastal cities were also calculated. Results: The distribution pattern of ESV and TCS and future development scenarios are significantly influenced by the area changes and chief influencing factors of various land types in coastal cities of Liaoning Province. The dynamic changes in construction land, cultivated land, grassland, and unused land play a significant role in various development scenarios, given the variations in development patterns across different cities. Two of the primary factors that influence the variations in various land types are GDP, NDVI, DEM, rainfall, and population distribution. Three provisioning services, regulating services, supporting services, and cultural services, also experienced a gradual decline in the ESV variations of coastal cities, while the ESV of cultivated land, forest land, rivers, and grasslands exhibited a downward development trend. The spatial distribution of carbon storage in coastal cities exhibited the characteristics of “low coastal, high eastern, western, and inland forest distribution areas, and medium carbon storage in the central grassland distribution area.” Four coastal cities can effectively mitigate the impact of urbanization development on ecosystem services under the ecological protection scenario. Conclusions: The present study demonstrates the spatiotemporal variations and propelling forces of ecosystem services in coastal communities during land use change under various simulation scenarios. Important references for sustainable development and land use control in coastal cities are provided through recommendations for non-construction land management that enhance ESV and TCS.

Amid the rapid evolution of the digital economy reshaping global competitiveness, China has advanced regional coordination through the Digital China initiative and the “Data Elements ×” Three-Year Action Plan (2024–2026). To further integrate digital transformation with high-quality growth in the urban agglomerations of the middle and lower Yellow River, this study aims to strengthen regional competitiveness, expand digital industries, foster new productivity, refine the development pathway, and safeguard balanced economic, social, and ecological progress. Taking the Yellow River urban clusters as the research object, a comprehensive assessment framework encompassing seven subsystems is established. By employing a mixed-weighting approach, entropy-based TOPSIS, hotspot analysis, coupling coordination models, spatial gravity shift techniques, and grey relational methods, this study investigates the spatiotemporal dynamics between the digital economy and high-quality development. The findings reveal that: (1) temporally, the coupling–coordination process evolves through three distinct phases—initial fluctuation and divergence (1990–2005), synergy consolidation (2005–2015), and high-level stabilization (2015–2022)—with the average coordination index rising from 0.21 to 0.41; (2) spatially, a persistent “core–periphery” structure emerges, while subsystem coupling consistently surpasses coordination levels, reflecting a pattern of “high coupling but insufficient coordination”; (3) hot–cold spot analysis identifies sharp east–west contrasts, with the gravity center shift and ellipse trajectory showing weaker directional stability but greater dispersion; and (4) grey correlation results indicate that key drivers have transitioned from economic scale and infrastructure inputs to green innovation performance and data resource allocation. Overall, this study interprets the empirical results in both temporal and spatial dimensions, offering insights for policymakers seeking to narrow the digital divide and advance sustainable, high-quality development in the Yellow River region.