Climate change has become a major worldwide problem, and land use/cover change has consistently played a crucial role in impacting the carbon cycle within terrestrial ecosystems. Territorial spatial planning stands as a relatively good policy option for the low-carbon model. The spatial correlation between carbon emissions and land use was established through environmental parameters in this paper. The territorial spatial structures in 2035 and 2060 under two scenarios of natural evolution and low-carbon development were simulated through the PLUS model. The results indicate that the spatial pattern of decreasing carbon emissions centered on towns, cities, mines, and industries is related to regional economic development, the distribution of forests, and the urban ecological environment. The implementation of territorial spatial planning aids in achieving carbon neutrality, whereas the low-carbon development scenario is more focused on it, which can provide ideas for territorial spatial planning adjustments. Both scenarios result in a large area of fallow land, indicating some conflict between farmland protection and low-carbon development. Optimizing management measures, energy structure, and industrial layout and strengthening regional coordination are key to promoting low-carbon development. This study might be useful in formulating regional carbon-neutral policies and improving territorial spatial planning.

Human needs alter ecosystem services (ESs), which then affect human needs. Based on the human needs theory, human needs in ESs are classified into four major categories of needs. A monetization method was used to quantify the ecosystem service need value (ESNV), the ES value equivalent method was used to calculate the ES supply value (ESSV), and the correlation coefficient method was used to understand the relationships and mutual influences among human needs. Finally, the supply and demand relationships were compared to obtain the gap between the supply of and demand for ESs. Taking Hechi City, a city with a predominantly karst landscape, as an example, we found that: (1) the total area of high-value areas and generally high-value areas in Hechi City increased by 10,935.15 km², whereas the total area of low-value areas decreased by 11,094.07 km². (2) A positive correlation exists between the various demands. Spiritual needs and material needs are moderately significantly positively correlated, with a correlation coefficient of 0.54. Security needs are positively correlated with material needs, with a correlation coefficient of 0.6. (3) The ESNV and ESSV in Hechi City are slightly imbalanced. Du’an requires an external ecosystem supply of CNY 21.9192 million for living material, while Jinchengjiang has the potential to export a water supply value of CNY 22.9934 million to external ecosystems. (4) The centers of gravity of various types of supply and demand are distributed primarily within Jinchengjiang District, only the center of gravity of production material needs is in the northern part of Du’an County. The distance between the centers of gravity of the demand and supply for living materials increased by 2100.44 m, the distance between the centers of gravity of the demand and supply for oxygen security increased by 639.15 m. The results are complementary to current research on ESs from the perspective of ES demand. These findings can thus provide scientific foundational information to help decision makers understand the balance between the supply and demand of ESs in a region and promote the construction of a human-harmonious ecosystem.

The quantification of ecosystem service demand value serves as a critical bridge connecting human well-being with ecological management. Addressing the current academic gap in valuation frameworks that precisely couple with supply classification systems and are difficult to integrate into Decision Support Systems (DSS), this study develops an ecosystem service demand analytical model. Based on ecological characteristics and administrative divisions, mainland China was divided into six management zones. Guided by Human Need Theory, a demand classification system was constructed. By integrating socio-economic big data with symbolic regression algorithms, we decoded the quantitative relationships between population scale and various demand values across regions, satisfying the requirements of DSS for rapid computation and real-time simulation. Results show that: (1) Spatial Distribution Characteristics: Within the population interval below 5 million, the demand values for various services in the Yellow River Basin Ecological Restoration Coordination Zone are higher than those in other regions under the same population base. (2) Evolutionary Patterns of Demand: The simulation curves reveal distinct environmental carrying capacity thresholds across all regions. Beyond these critical points, the marginal fulfillment costs of ecosystem services surge, driving a rapid upward trend in demand value. (3) Model Accuracy and Application: With the introduction of a time-factor correction, the average model error is controlled within 10%, and the accuracy is improved by 20%. This study establishes a classification and accounting framework that balances computational simplicity with realistic alignment, achieving multi-scale quantitative assessment of demand value and providing core algorithmic support for ecosystem service decision support systems.