Global climate change and coastal urbanization have significantly impacted the health and carbon storage of coastal zone ecosystems. Investigating the spatial and temporal variations in coastal carbon storage is crucial for developing effective strategies for land management and ecological protection. Current methods for evaluating carbon storage are hindered by insufficient accuracy and data acquisition challenges, necessitating solutions to enhance both reliability and precision. This study aims to assess the variations in carbon storage and annual carbon sequestration in the Jiaozhou Bay coastal zone from 1990 to 2020 and to identify the driving factors by integrating the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Carnegie Ames Stanford Approach (CASA) models with remote sensing data and geographic detector methods. The findings suggest that Jiaozhou Bay has experienced a substantial decrease in carbon storage, declining by 17.4% from 1990 to 2020, and annual carbon sequestration, decreasing by 35.5% from 1990 to 2016, but has stabilized recently. Vegetation cover and water bodies play critical roles in regional carbon storage. Furthermore, the dynamics of carbon storage and land use patterns are significantly influenced by socioeconomic factors, including GDP and population density. A comparison of the InVEST and CASA models demonstrates consistency in their carbon storage and annual carbon sequestration assessments. Combining these models in future assessments can enhance the scientific rigor and accuracy of the research, providing more reliable evidence for ecosystem management and policy making.

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R² of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

Controlling the conversion of cultivated land to non-grain uses is of great significance for ensuring global food security. Currently, the research on the conversion of Main Grain Land (MGL) to non-grain uses lacks a theoretical framework that can support differentiated and targeted governance. In this study, a “Resource Cost-Negative Effect-Remediation Potential” (RC-NE-RP) evaluation framework for the conversion of cultivated MGL to non-grain uses was established based on the definition of “non-grain conversion of MGL” and the theory of the production function in economics, the negative effects of non-grain conversion of MGL and the remediation potential of non-grain land converted from MGL in the Bohai Rim (BR) region, China, during the period from 1990 to 2020 were quantitatively evaluated using an improved SBM model, and the non-grain land to be remediated in the BR region was zoned through cluster analysis. The results show that: (1) The process of non-grain conversion of MGL in the Bohai Rim region shows a trend of fast conversion followed by slow conversion, with increasingly significant characteristics of spatial differentiation. (2) For the period from 1990 to 2020, the negative effects of non-grain conversion generally exhibit an upward trend, and the negative effects of non-grain conversion in coastal economic zones are generally stronger than those in hinterlands; the remediation potential of non-grain land converted from MGL shows a downward trend followed by an upward trend, and the remediation potential of non-grain land in coastal economic zones is lower compared to hinterland areas. (3) The areas represented by Beijing and the Bohai Economic Rim (BER) are classified as priority remediation zones, and the other areas are classified as low-priority remediation zones. The BR region is divided into three types of zones for remediation, namely, Quantity–Quality Priority Zones, Quantity–Landscape Priority Zones, and Quality–Landscape Priority Zones. This study provides a scientific basis for the management and control of non-grain conversion of cultivated land and the protection of cultivated land.