Shanshan Guo

Land use transition pathways have different ecological effects, but the impacts of key conversion processes on habitat quality remain insufficiently understood. Habitat quality was defined as a unitless 0–1 InVEST index of the landscape’s relative capacity to support biodiversity, derived from land-cover suitability and exposure to anthropogenic threats. This study identified key transition pathways affecting habitat quality in the Huaihe River Eco-Economic Belt. Land use data from 2003 to 2023 were analyzed using a coupled PLUS–InVEST framework to simulate land use and habitat quality under four scenarios for 2033, while contribution analysis quantified pathway effects. Cropland continuously decreased, whereas the aggregated impervious-surface category, comprising built structures and paved or artificially sealed surfaces, expanded. Mean habitat quality increased from 0.588 to 0.628, a modest absolute increase of 0.040 on the 0–1 scale, and was higher in the south than in the north. A small number of pathways dominated habitat-quality change; cropland and forest conversion to impervious surfaces were the principal negative pathways. Scenario differences were modest, indicating potential trade-offs among agricultural production, ecosystem conservation, and habitat quality; at the transition-pathway level, the Ecological Protection and Sustainable Development scenarios reduced the negative habitat-quality contributions of specific high-risk conversions, particularly cropland-to-impervious-surface conversion.

Ecological resilience (ER) describes the ability of ecosystems to resist, adapt, and recover from external shocks. How to improve ER has become a crucial component of high-quality development (HQD) in the new era. Therefore, there is an urgent need to investigate the synergistic relationship between HQD and ER. In this study, taking the Yellow River Basin as an example, long-term time series data (2008–2022) were used at the provincial scale. Then we developed a logical framework to reveal the interrelationship and intrinsic mechanism between HQD and ER. Next, we explored the spatiotemporal coupling characteristics of HQD and ER using a comprehensive evaluation model and coupling coordination analysis. We found that from 2008 to 2022, the comprehensive level of HQD and ER fluctuated, and the coupling coordination degree showed significant spatial distribution characteristics. Meanwhile, from 2008 to 2022, the spatial evolution level of HQD increased in 89% of the study regions, while ER improved markedly across most regions. Furthermore, using principal component analysis (PCA), we analyzed the driving factors for HQD and ER. The analysis revealed that economic development, green innovation, livelihood improvement, and ecological adaptation play significant roles in promoting the coordinated development of HQD and ER. This research can serve as a reference and methodological guidance for achieving high-quality and sustainable development in the Yellow River Basin.

As a pivotal driver of high-quality development, new quality productivity (NQP) forms an indispensable synergistic relationship with land use efficiency (LUE) for achieving regional sustainability. Based on panel data from 29 prefecture-level cities in the Central Plains Urban Agglomeration (CPUA) from 2010 to 2023, this study integrates the entropy-weighted TOPSIS method, super-efficiency Slack-Based Measure (SBM) model, Malmquist index, and fixed-effects models to systematically explore the spatiotemporal evolution of NQP and its underlying impact mechanism on LUE. Key findings reveal: (1) The comprehensive NQP index of the CPUA increased from 0.280 to 0.828, exhibiting a “stepwise rise” trend, with a spatial pattern characterized by a “core–secondary–periphery” three-tier gradient distribution. Zhengzhou, as the core growth pole, played an innovative leading role, while peripheral cities (e.g., Handan, Hebi) remained constrained by resource-dependent economic structures, with NQP indices consistently below 0.2. (2) The average LUE in the study area increased from 0.917 to 1.031. Cities within Henan Province generally performed better than those in Hebei, Shanxi, and Anhui provinces. Total factor productivity grew at an average annual rate of 16.4%, with technological progress serving as the primary driver. (3) NQP exerts a significantly positive impact on LUE, yet with notable heterogeneity: large-scale cities enhanced intensive land use substantially through technological agglomeration and industrial upgrading; cities with scarce arable land and high economic development levels effectively leveraged NQP to boost LUE; in contrast, small cities, regions rich in arable land, and areas with low economic development have not established effective synergistic mechanisms, hindered by limited technological absorption capacity, path dependence, and factor bottlenecks. This study provides empirical support and actionable insights for optimizing land resource allocation and advancing coordinated development between NQP and LUE in similar urban agglomerations.

Understanding the socioecological nexus between urbanization and ecosystem health (EH) is crucial for formulating sustainable development policies. While prior research has focused on this topic, critical gaps persist in characterizing distributional polarization and decomposing inequality drivers within coupled human–environment systems—particularly in China’s Yellow River Basin (YRB), a strategic region undergoing concurrent ecological restoration and urbanization. The integration of the kernel density estimation and Theil index establishes a robust analytical framework to effectively overcome spatial heterogeneity limitations in regional disparity research. Therefore, this study combines the coupling coordination degree (CCD), nonparametric kernel density estimation, and Theil decomposition to examine the complex interactions between urbanization and the ecosystem health index (EHI) across 538 county-level units from the perspective of spatial heterogeneity. The key findings reveal the following: (1) Urbanization exhibited phased enhancement yet maintained elementary developmental stages overall, with a distinct spatial gradient descending from the eastern/central riparian counties to the western hinterlands. (2) The EHI showed a marginal upward trend, yet 80.29% of the counties persisted in the suboptimal ecological health categories (EHI-1 to EHI-3), with gains concentrated in high-vegetation mountainous areas (45.72%) versus declines in economically developed areas. (3) The CCD evolved from a mild imbalance (II-1) to low coordination (III-1) but with significant special differences—the midstream and downstream CCD improved markedly, while the upstream counties remained the weakest. (4) Intragroup disparities, particularly among the counties in the middle reaches, were the primary drivers of CCD disequilibrium across the YRB, contributing 87.9% to the overall inequality. In contrast, the downstream regions exhibited significant improvements in the coordination levels, accompanied by the emergence of distinct “multi-polarization” patterns. The findings provide refined and differentiated decision-making references for effectively narrowing the gap in coordinated development in the YRB.

With the rapid development of urbanization, China is facing problems, such as uncoordinated regional development, imbalanced land space development, and ecological environment pollution. This poses a huge threat to the sustainable development of China’s economy and society. Therefore, there is an urgent need to determine how to coordinate the relationship between the space exploitation, economic development, and ecological environment (SEE) of urban areas. In this study, taking the Henan Province as an example, long time-series data (2000–2020) were used, at a city scale. Then, we developed a logical framework to reveal the interrelationship and intrinsic mechanism between SEE. Next, we explored the spatiotemporal coupling characteristics of SEE using a linear weighting method and a coupling coordination analysis. We found that, from 2000 to 2020, the comprehensive level of SEE showed an obvious trend of change, and different cities have different coupling coordination degrees. However, the overall coupling coordination level is steadily developing and tending to improve. Furthermore, with the spatial autocorrelation method, we analyzed spatial correlation patterns and collaboration/trade-off relationships for SEE. Through the analysis, positive correlation types (HH, LL) cluster significantly and negative correlation types (HL, LH) have low clustering. Meanwhile, we found significant spatial differences in cooperation/trade-off relationships between different years. This research can serve as a reference and as methodological guidance for achieving coordination and sustainable development of the economy, space, and environment.