The Yellow River Delta (YRD) stands as a globally significant wetland, playing a pivotal role in sustaining regional ecosystem stability and offering crucial ecosystem services to humanity. However, anthropogenic activities, particularly resource development, unavoidably disrupt the ecosystem, leading to the degradation of these vital services. Utilizing satellite remote sensing data, the InVEST model, and energy analysis, this study introduces the concept of ‘emergy’ as an ‘intermediate variable’ to investigate the spatiotemporal changes in the ecosystem service value of the YRD. Five distinct types of ecosystem services are selected for quantitative assessment and analysis of the YRD’s spatiotemporal evolution from 1990 to 2020. Results indicate a 63.7% decline in the total value of ecosystem services from 1990 to 2010, followed by a 16.5% increase from 2010 to 2020. The study also unveils spatial shifts in high- and low-value areas of ecosystem services and attributes these changes to rapid urbanization and alterations in land use and cover. The assessment of ecosystem service values concretizes the intangible ecosystem service functions of natural resources. This lays the foundation for establishing a mechanism that combines positive incentives and reverse pressure to achieve the economic valuation of ecosystem service.

Coastal urban areas play a pivotal role in regional economic growth, yet they are also sensitive regions where the natural environment interacts with human activities. Rapid urbanization has led to ecological issues, such as the degradation of key ecosystem services (ESs) in coastal cities. Understanding the impact of urbanization on the supply and demand of ESs in coastal cities is crucial for ensuring the healthy and sustainable development of the socio-economy in these areas. This study takes the coastal city of Qingdao as a case study, with a focus on the supply and demand relationships of seven key ESs—carbon sequestration, food production, habitat quality, recreational capacity, soil retention, water yield and marine aquaculture—as well as their responses to population growth, economic development, and spatial expansion, which are three distinct aspects of urbanization. The findings indicate that: (I) From 1992 to 2022, the evolution of different ESs exhibited variability, all displaying notable spatial imbalances. (II) During this period, the urbanization process in Qingdao had a pronounced negative effect on the supply and demand of ESs. Spatial urbanization had the most significant impact on carbon sequestration, food production, habitat quality, water yield, and marine aquaculture, while population urbanization most notably affected recreational capacity. (III) The sensitivity of different types of ESs to population, economic, and spatial urbanization varies. Carbon sequestration, food production, recreational capacity, soil retention, water yield, and marine aquaculture were most sensitive to economic urbanization, whereas habitat quality was most sensitive to spatial urbanization. This research provides a basis for decision-making in the sustainable development and comprehensive management of coastal cities and their ecosystems.

Accurately identifying ecological compensation areas and scientifically determining appropriate compensation amounts are crucial for establishing a robust ecological compensation mechanism, which in turn is key to promoting the coordinated development of ecological protection and high-quality economic growth. This study innovatively proposes a framework for ecological compensation termed “Accounting of Ecosystem Services Value–Identification of Priorities for Payers and Recipients–Calculation of Ecological Compensation Amount (ESV–PPR–ECA)”. It utilizes the InVEST model and the emergy method to assess the value of ecosystem services, constructs the Ecosystem Payment and Recipient Priority Sequence (EPRPS) Model to identify the payers, recipients, and their priorities for ecological compensation, and employs the conversion factor method to calculate the Ecological Compensation Amount (ECA). This framework aims to address the questions of “How should compensation be provided?”, “Who should compensate whom?”, and “How much compensation is necessary?”, ensuring the optimal use of ecological compensation funds and providing a scientific basis for inter-regional ecological compensation. The study’s findings indicate that the total Ecological Compensation Amount for the Yellow River Delta in 2020 was 3.848 billion RMB, with the total amount receivable being 4.032 billion RMB and the total amount payable being 184 million RMB. The compensation funds should be prioritized for tideland and the Yellow River, and venture, cropland and industrial land should be the first to contribute compensation. Additionally, the Ecosystem Service Value of the Yellow River Delta showed a declining trend from 2015 to 2020, underscoring the urgent need to establish a horizontal compensation mechanism for the region. Such a mechanism would incentivize environmental protection and the construction of ecological civilization, ultimately enhancing ecosystem service functions. Therefore, we recommend the implementation of horizontal fiscal transfers, where financial assistance is provided from paying areas to recipient areas, offering a scientific reference for the establishment of a horizontal compensation mechanism within the Yellow River Delta.

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.

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Understanding ecosystem service characteristics along urban-rural gradients is vital for enhancing the well-being of urban and rural residents. Despite this importance, prior research has neglected the dynamic evolution of urban-rural gradients during urbanization. This study investigates the spatiotemporal variations of four ecosystem services—habitat quality, carbon sequestration, water yield, and soil retention—along the urban-rural gradient in Jinghong City, China. We propose a method for identifying the gradient using the inverse S function of urban land density distribution and concentric analysis. From 2000 to 2020, ecosystem service supply capacity in Jinghong City continuously declined, indicating degradation over the two decades. The urban-rural gradient zone is classified as core area, inner urban area, suburban area, and urban periphery, each experiencing outward expansion, reflecting significant urbanization. Changes in ecosystem services along the gradient revealed consistently high losses in habitat quality, carbon sequestration, and overall services in the inner urban area, while water yield and soil retention suffered the greatest losses in the urban periphery. As urbanization expanded outward, the loss of these services shifted from the inner urban area to the suburban and urban periphery. These results support decision-making in urban planning and sustainable development for urban-rural regions.