4.1. Factors Affecting Carbon
The factors affecting carbon storage in the coastal zone are variable and complex, and studies have shown that natural environmental factors and human activities exert varying degrees of influence on coastal carbon storage [
53]. In this study, nine natural environmental factors and four socioeconomic factors were selected for in-depth analysis. These natural environmental factors include elevation (DEM), distance from the coast (Dco), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), mean annual precipitation (Prec), slope (Slp), mean annual temperature (Temp), extreme climate (EC), and temperature range (TR). The socioeconomic factors include gross domestic product (GDP), night light index (NL), population density (PD), and industrial structure (IS). Among these factors, temperature range (TR) indicates the difference between the monthly average temperatures of the hottest and coldest months of the year, while extreme climate (EC) reflects the difference between the highest and lowest temperatures recorded during the year.
GeoDetector and Pearson correlation analysis are both valuable tools for analyzing the factors influencing carbon storage, but they have distinct strengths and applications. GeoDetector excels in detecting spatial heterogeneity and the interactions between factors, making it highly effective for understanding the complex spatial patterns of carbon storage. It quantifies the explanatory power of each factor and their interactions, which is particularly useful for spatially stratified phenomena.
On the other hand, Pearson correlation analysis measures the strength and direction of linear relationships between continuous variables. It provides a straightforward method to assess the correlations between factors influencing carbon storage. Pearson correlation is easy to compute and interpret, making it suitable for initial exploratory analysis to identify potential linear relationships between variables.
The results of this study (
Table 5,
Figure 8 and
Figure 9) show significant differences in the explanatory power of various drivers on carbon storage changes. The
p-values for all detected factors were less than 0.01, confirming their statistical significance. Among them, NDVI exhibited the highest q-statistic value of 0.289, making it the dominant factor in the region. This underscores the crucial role of vegetation cover in carbon sequestration capacity, as vegetation fixes carbon dioxide in biomass through photosynthesis. Higher NDVI values indicate dense vegetation and strong carbon sequestration capacity. Therefore, the higher the NDVI value, the greater the carbon storage. Pearson correlation analysis supports this, showing a strong positive correlation between NDVI and carbon storage (r = 0.453 to 0.564 across different years). Vegetation not only absorbs and stores carbon but also increases the organic carbon content in the soil through roots and litter, maintaining the carbon balance of the ecosystem. Studies have shown that wetlands, such as mangroves, swamps, and marshes, store significantly more carbon compared to other ecosystems due to their dense vegetation and biomass. For instance, Mexican wetlands have been documented to store 13, 7, 6, and 5 times more carbon than terrestrial ecosystems [
54].
Second, MNDWI also had a significant effect on carbon storage, reflecting the importance of moisture conditions on ecosystem carbon storage. The MNDWI indicates the distribution of water in the region. Good water conditions are crucial not only for vegetation growth and increased coverage but also for supporting microbial activity, which accelerates organic matter decomposition and soil carbon formation. Pearson correlation analysis indicates a negative correlation between MNDWI and carbon storage (r = −0.434 to −0.366), suggesting that while good moisture conditions support carbon storage, excessive water might negatively impact it through other means.
Natural factors such as Temp, EC, and TR affect the temperature conditions for plant growth and climate stability, respectively. Suitable temperature conditions not only contribute to the prosperity of vegetation and carbon fixation but also promote carbon cycling in ecosystems. Extreme climates and large temperature ranges reflect climate stability; frequent extreme climates and large temperature differences may lead to ecosystem degradation, thus affecting the carbon sequestration efficiency of vegetation and the stability of carbon storage [
55,
56]. Pearson correlation analysis shows negative correlations for Temp (r = −0.3 to −0.392), EC (r = −0.070 to −0.184), and TR (r = −0.132 to 0.048), indicating that higher temperatures and climate extremes negatively impact carbon storage.
Although the explanatory power of anthropogenic indicators such as GDP, PD, and NL is relatively low, it remains significant. Among the anthropogenic factors, GDP has the highest explanatory power, reflecting the intensity of economic and human activities, including urbanization, industrial development, and infrastructure construction. These factors have complex impacts on the formation and alteration of carbon storage in the coastal zone. Economic development is usually accompanied by land use changes, such as wetland degradation and urban expansion, which are associated with significant carbon exchange and loss. Pearson correlation analysis indicates a negative correlation between GDP and carbon storage (r = −0.205 to −0.147), supporting the notion that increased economic activity may reduce carbon storage. Social activities promoting the sustainable development of a green economy can also contribute to increasing carbon storage. The NL and PD are also indicators of human activities. The difference is that NL reflects the intensity of human activities, while PD reflects their concentration. In urbanized and industrialized areas, higher NL values indicate frequent human activity, often accompanied by reduced vegetation cover and increased soil erosion, which in turn affects carbon fixation and storage. Similarly, high population density is often accompanied by land development and construction activities, leading to reduced vegetation cover and ecosystem destruction, resulting in a decline in carbon storage. Correlation analysis supports these observations with negative correlations for NL (r = −0.22 to −0.239) and PD (r = −0.205 to −0.182). The interaction between natural and socioeconomic factors plays a crucial role in carbon storage. These interactions often result in nonlinear and synergistic effects that are more significant than the sum of individual factors [
54]. Economic activities, indicated by factors such as GDP, population density, and night light, have complex impacts on carbon storage. While economic development can lead to land use changes and reduced vegetation cover, sustainable development practices can help mitigate these effects and enhance carbon storage [
57].
The average explanatory power of natural and socioeconomic factors was 9.46% and 9.38%, respectively, revealing the combined influence of natural and anthropogenic factors on the spatial distribution of carbon storage in the coastal zone of Jiaozhou Bay. In general, vegetation cover and moisture conditions are the main natural factors affecting carbon storage, while the combined influence of socioeconomic factors should not be overlooked despite their lower individual explanatory power. By analyzing these driving factors and the mechanisms behind them in depth, we can better understand the changing patterns of carbon storage and provide a scientific basis for the formulation of effective carbon management and ecological protection policies.
In this study, the selected factors were interactively probed, and all interactions were enhanced, indicating that the interaction between factors affects the spatial differentiation of carbon storage in a nonlinear and bifactorial manner. The complex coupling between different factors influences the spatial differentiation of carbon storage, with 34 nonlinear enhancements and 44 bivariate enhancements identified (
Figure 10b).
The results of this study (
Figure 10a) show that the interaction between NDVI and GDP in the coastal zone of Jiaozhou Bay produced the combination with the strongest explanatory power for carbon storage in the study area, with a q-statistic value of 0.359. This can be understood as changes in NDVI simultaneously enhancing the explanatory power of GDP for regional changes in carbon storage. From 1990 to 2020, driven by economic development, the urbanization rate in the coastal zone of Jiaozhou Bay increased from 29.83% to 64.66%. Land use changes led to a decrease in vegetation cover from 50.35% to 25.97%, which in turn affected the carbon storage in the study area. Additionally, the interactions of NDVI, MNDWI, and GDP with other factors also yielded high q-statistic values.
This result suggests that the ecosystem of the coastal zone of Jiaozhou Bay is influenced by the interaction of multiple factors, not simply by addition or independence, but through nonlinear and bivariate enhancements. For example, the combination of vegetation cover (NDVI) and economic activity (GDP) not only increases land use changes but also further affects carbon storage by altering ecosystem structure and function. Additionally, the interaction of moisture conditions (MNDWI) and vegetation cover also showed significant effects. This interaction may affect carbon fixation and storage by influencing plant growth and water use efficiency.
Such complex interaction mechanisms indicate that the effects of a single factor are often amplified or altered in multifactor interactions, creating new ecological effects. For example, in regions experiencing rapid economic development, even with high vegetation cover, the carbon sequestration capacity of vegetation can be limited if water resources are scarce. Similarly, in the context of climate change, changes in temperature and precipitation, by affecting plant growth and soil moisture, can act in conjunction with socioeconomic factors to alter the distribution and trends in carbon storage.
Overall, carbon storage in the coastal zone of Jiaozhou Bay is influenced by the complex coupling of natural and anthropogenic factors. This nonlinear enhancement and two-factor enhancement effect emphasizes the importance of considering the combined effects of multiple factors in ecosystem management. Understanding these interaction mechanisms can help formulate more effective carbon management and ecological protection policies, thereby better addressing the challenges of climate change and ecosystem change.
4.2. Analysis of Factors Influencing Carbon Storage
4.2.1. Analysis of the Impact of LUCC on Carbon Storage
LUCC is one of the key factors affecting ecosystem carbon sequestration services [
58]. Converting cropland or wetlands to built-up areas can destroy soil and vegetation, releasing large amounts of CO
2 into the atmosphere, thereby reducing the carbon sequestration capacity of ecosystems. Additionally, the conversion of natural ecosystems to agricultural ecosystems significantly reduces the soil’s carbon storage capacity [
16,
17]. For example, converting forests and grasslands to agricultural land, or wetlands to agricultural land, disturbs the soil and vegetation, resulting in the loss of carbon storage and nutrients [
59].
According to the results of this study (
Figure 4 and
Figure 11), the conversion of land use types in the study area led to significant changes in carbon storage. From 1990 to 2020, land use types in the coastal zone of Jiaozhou Bay changed significantly. The expansion of built-up land and the substantial decrease in the cropland and aquaculture ponds were particularly prominent. The built-up land increased by 410.40 km
2, primarily due to the decrease in the area of cropland and aquaculture ponds. Cropland decreased by 248.21 km
2, especially between 2000 and 2010, and the area of aquaculture ponds decreased by 85.49 km
2. This study showed that the reduction in the area of cropland and woodland led to significant carbon storage loss, accounting for 86.98% and 5.33% of the total carbon storage loss, respectively, while the conversion of aquaculture ponds to built-up land contributed to 60.75% of the increase in carbon storage. The average contributions of different land use types to the total carbon storage in the study area, in descending order, were cropland, built-up land, woodland, mudflat, aquaculture pond, grassland, reservoirs, river, and bare land. Notably, cropland was the largest contributor to carbon storage in the coastal zone of Jiaozhou Bay between 1990 and 2010. However, by 2020, built-up land surpassed cropland as the top contributor to carbon storage. This is mainly due to the continuous expansion of built-up land encroaching on cropland from 1990 to 2020, leading to a continuous decline in cropland carbon storage and a continuous increase in the carbon storage contribution of built-up land. Despite the lower carbon density of built-up land, their carbon storage contribution value rose to the top position because they occupy the highest absolute share in the study area. The combined average total carbon sequestration of cropland and built-up land accounted for 80.49% of the total carbon sequestration in the coastal zone of Jiaozhou Bay.
In addition to cropland and built-up land, which occupy the largest share of the area, woodlands and mudflats, although smaller in area, make significant contributions to the carbon storage in the coastal zone of Jiaozhou Bay due to their higher carbon density values. Despite the small total area of reservoirs, their contribution to carbon storage rose from seventh to fifth, highlighting their increasing importance in the coastal zone ecosystem of Jiaozhou Bay. This increase is due to the creation of many artificial reservoirs between 1990 and 2020, which expanded their area and, thus, significantly increased their contribution to carbon storage. Furthermore, natural ecosystems such as rivers, woodlands, and grasslands also underwent significant changes during these 30 years, with approximately 13.2% of these natural ecosystems (including rivers, reservoirs, mudflats, woodlands, and grasslands) being converted into cropland, leading to a loss of soil carbon (
Figure 11).
Overall, land use changes in the study area have had a profound impact on carbon storage. Cropland and built-up land dominate the carbon storage contribution, while other types such as woodland, mudflats, and reservoirs, although smaller in area, still contribute significantly to the regional carbon storage due to their high carbon density. This study shows that rational planning of land use is crucial for enhancing the carbon sequestration capacity of ecosystems.
4.2.2. Impact of Climate on Carbon Storage
Climate factors significantly influence the spatiotemporal distribution of carbon storage in coastal zones. Climate affects hydrothermal conditions, which in turn influence the decomposition rate of vegetation litter, ultimately impacting soil organic carbon content.
GeoDetector results indicate that climate factors, especially temperature, have a substantial impact on carbon storage in coastal zones (
Figure 8). This finding is corroborated by Pearson correlation analysis. Temperature shows a moderate explanatory power (q-statistic = 0.073 to 0.116), and Pearson correlation analysis reveals a negative correlation between temperature and carbon storage (r = −0.3 to −0.392) (
Figure 9). This suggests that rising temperatures negatively affect carbon storage, likely due to limitations on vegetation growth and increased soil microbial respiration caused by higher temperatures, leading to a reduction in carbon storage.
Furthermore, precipitation and its spatial distribution also influence carbon storage to some extent. Pearson correlation analysis shows that precipitation and carbon storage can be both positively and negatively correlated, indicating that while adequate moisture conditions support vegetation growth and carbon storage, excessive rainfall can lead to waterlogging, negatively impacting soil structure and microbial activity, and thereby reducing carbon storage.
Extreme climate events and temperature range also have significant explanatory power regarding carbon storage. Generally, both EC and TR show a negative correlation with carbon storage, indicating that extreme temperatures and temperature fluctuations can destabilize carbon storage. GeoDetector results confirm these findings, with extreme climate events and temperature range showing moderate explanatory power.
4.2.3. Impact of Economic Factors on Carbon Storage
Based on Pearson correlation analysis, GDP, NL, and PD all show negative correlations with carbon storage. An increase in GDP typically indicates heightened industrial activities, urban expansion, and infrastructure development, all of which involve significant land use changes. The night light index serves as a proxy for human activity intensity and is highly correlated with population density.
From 1990 to 2020, the expansion of built-up areas was a major trend in the region (
Figure 11), leading to substantial vegetation loss and soil carbon depletion. Similarly, areas with high human activity density often exhibit lower carbon storage due to land use changes and habitat destruction, negatively impacting carbon storage.
GeoDetector results confirm the significant impact of economic factors and human activities on carbon storage (
Figure 8). These findings underscore the importance of considering economic growth and human activity in carbon management strategies.
In conclusion, economic factors significantly impact carbon storage in coastal ecosystems. Sustainable economic growth, efficient land use planning, and conservation policies are essential to enhance carbon storage and maintain ecological balance in coastal zones.
4.3. Comparison of InVEST and Improved CASA Models
NPP is the net amount of carbon dioxide fixed by plants through photosynthesis minus the carbon released by respiration [
60]. It is a sensitive indicator of climate and environmental change and is closely related to carbon storage. The magnitude of
NPP directly determines the rate of carbon sequestration by plants, thus affecting the carbon storage capacity of ecosystems. In this study, we calculated the carbon storage and annual
NPP of the coastal zone of Jiaozhou Bay from 1990 to 2020 and from 1990 to 2016, respectively, using the InVEST model and the improved CASA model, and converted the annual
NPP to carbon sequestration.
The results showed that the trends of carbon storage changes and annual carbon sequestration in Jiaozhou Bay were similar, and the trends and spatial distribution of carbon storage and sequestration were highly consistent, indicating a strong correlation between ecosystem productivity and carbon storage in the coastal zone of Jiaozhou Bay. Under the pressure of environmental changes and human activities, the health of the ecosystem continues to deteriorate. From 1990 to 2020, the carbon storage showed a decreasing trend, with the rate of decline gradually decreasing. Although the data on annual carbon sequestration only cover the period from 1990 to 2016, the decreasing trend is consistent with the change in carbon storage. This pattern coincides with the change in cultivated land area in Qingdao, which decreased from 102 km2 per decade to 52 km2 per decade during the same period. This shows that the region has transitioned from rapid urbanization, which led to the encroachment of a large amount of arable land, and to the implementation of the “Protect the Bay and Develop around the Bay” strategy by Qingdao in 2007, aimed at protecting the ecological environment. In recent years, conservation measures in the coastal zone of Jiaozhou Bay have achieved remarkable results, and the trends in carbon storage and annual carbon sequestration indicate that the ecological environment is stabilizing.
Although both the InVEST model and the improved CASA model are based on LUCC data, there are significant differences in modeling principles and applications. The InVEST model excels at comprehensively assessing ecosystem carbon sequestration capacity by quantifying the carbon in various carbon pools. However, its accuracy depends on exhaustive carbon pool data, and it has a limited ability to reflect the complexity of ecosystem carbon sequestration mechanisms. In contrast, the CASA model, as a process-based remote sensing model, has advantages in data availability and parameter inputs, but its ability to accurately estimate carbon sequestration in small areas is weaker, particularly due to its high requirements for accuracy in land use classification and parameters [
61,
62]. Therefore, for estimating carbon storage and annual carbon sequestration in small areas, more mature ecosystem service valuation models like the InVEST model may provide more accurate results than the CASA model, better reflecting the region’s carbon storage and productivity.
Comparing
Figure 5 and
Figure 7, it can be seen that both the distribution of carbon storage changes calculated using the InVEST model and the distribution of carbon sequestration changes calculated using the CASA model show that the vast majority of the area remained relatively stable, with a small portion experiencing significant decreases in carbon storage and sequestration, and only 1% showing an increasing trend in carbon storage. This spatial pattern indicates that the carbon sequestration capacity of the coastal zone ecosystem in Jiaozhou Bay is experiencing a rapid decline. Further analysis revealed that areas with declining carbon storage and sequestration were mainly concentrated in zones with drastic urbanization and land use changes. Especially in areas with frequent urban expansion, industrial development, and agricultural activities, these human activities have led to significant vegetation destruction and soil carbon loss, thereby markedly reducing the carbon storage capacity and ecosystem service function of the region. Overall, the decline in carbon storage and sequestration capacity in the coastal zone of Jiaozhou Bay reflects serious challenges facing the regional ecosystem. To reverse this trend, there is an urgent need to strengthen the ecological protection and restoration efforts in the Jiaozhou Bay coastal zone.
4.4. Limitations of This Study and Future Directions
Within the context of this study, the following limitations should be pointed out and could be further explored in future studies. First, there may be some uncertainties in the remote sensing data and other datasets used in this study due to data availability and resolution limitations. For example, the calculation of annual carbon sequestration was limited to 1990–2016 due to the lack of solar radiation data from 2017–2020. Additionally, errors might be present in the calculation of annual carbon sequestration because the monthly NDVI data were obtained from three different sources. Furthermore, although the random forest and CASA models showed relatively good classification and estimation in this study, the accuracy and generalization ability of the models were constrained by data quality. It should also be noted that, although GeoDetector can reveal the relationships and interactions between carbon storage and the influencing factors, it is limited in the ability to explore complex nonlinear relationships and spatiotemporal distributions.
Another significant limitation of this study arises from the dynamic nature of the coastline in the Jiaozhou Bay area. Over the years, natural processes such as erosion, sediment deposition, and sea-level rise, along with anthropogenic activities like land reclamation and infrastructure development, have led to changes in the coastline. Consequently, the shape and area of the study region have experienced slight variations over time. For instance, the study area was initially determined based on the 1990 coastline and extended 10 km inland. The study areas for 2000, 2010, and 2020 were determined by combining the land boundary of the 1990 study area with the coastline of each respective year. These adjustments in the study area’s boundaries lead to minor variations in the total area covered each year. This results in the carbon storage calculations not being strictly based on the same area, introducing an element of uncertainty in the total carbon storage estimates.
Future research will focus on the following aspects: First, with the continuous development and advancement of remote sensing and geographic information, datasets with higher spatial and temporal resolution should be used to improve the accuracy and reliability of the research. Second, more advanced machine learning algorithms and ecological assessment models should be explored to enhance the accuracy of land use classification and carbon storage estimation. Additionally, a deeper investigation into the mechanisms affecting ecological changes and carbon storage dynamics, especially the combined effects of socioeconomic development and climate change on coastal carbon storage, will better aid in assessing and predicting the trends of spatiotemporal changes in carbon storage. Finally, emphasis should be placed on researching ecosystem restoration and management strategies, evaluating the impacts of various conservation measures on ecosystem health and carbon storage, thereby providing a basis for the formulation of more effective ecological protection policies.