Sidong Zhao

Urban green space supply (UGSS) is a core component of territorial spatial planning, and the advancement of the park city initiative has been temporally associated with a systematic transformation. Differentiation in UGSS is a comprehensive issue concerning ecological environment, public welfare, and high-quality urban development, and it is closely related to the achievement of United Nations Sustainable Development Goal (SDG) 11.7. This study employs a comprehensive approach combining spatiotemporal dynamic analysis (Mann–Kendall trend test and Theil–Sen slope estimation), differentiation measures (Gini coefficient and Theil index), and the explainable machine learning SHAP model to conduct a large-sample empirical analysis of 1760 towns in China from 2015 to 2024. The findings show the following: First, park city construction corresponds to notable spatiotemporal evolution of UGSS in China’s towns, with approximately 85% of towns showing a significant rise. Second, park city construction coincides with a reduction in differentiation in both the outcomes and processes of UGSS in China’s towns, with both the Gini coefficient and Theil index declining to varying degrees. An analysis of the Theil index further confirms that the observed changes in the Theil index are more pronounced in disadvantaged towns, and the differentiation in UGSS originates more from intra-regional disparities than from inter-regional gaps. Third, the differentiation in UGSS shows deep structural correlates, with the associations of socio-economic and natural ecological factors exhibiting various complex nonlinear associations such as inverted U-shape, arc shape, U-shape, and wave shape. These associations are specifically manifested as mixed directionality of associations, hierarchical intensity of associations, threshold-based evolutionary pathways, geographic spatial heterogeneity, and interactive relationships among factors. This study recommends that the policy design of park city construction and green space system planning should promptly establish a new model combining situational response, threshold management, and collaborative governance. The nonlinear and interpretable analytical paradigm constructed in this study holds significant value for achieving precise supply and equitable sharing of green space resources.

Urban park green spaces are key infrastructure for improving the quality of the living environment, enhancing residents’ well-being, and strengthening ecological resilience. Targeting 1760 county seats in China, this study combines the stock and incremental synergy analysis matrix, exploratory spatial data analysis, and explainable machine learning (EML) methods (SHAP) to systematically reveal the spatiotemporal patterns, spatial association characteristics, and nonlinear paths of influencing factors regarding the spatial configuration of park green spaces in county seats from 2015 to 2024. The low-stock expansion zone is dominant and concentrated in the western region and non-core urban agglomerations. The high-stock expansion zone is of the dominant type, concentrated in the eastern coastal areas and core urban agglomerations. The low-stock contraction zone and high-stock contraction zone are, respectively, marginalized lock-in and degradation risk types, requiring priority intervention. The dynamics of the park green space configuration, including the stock, increment, and their synergy, all exhibit significant positive spatial autocorrelation. The influences of the nine factors exhibit four major characteristics: directional mixing, intensity gradation, path nonlinearity, and regional heterogeneity. The threshold effect is widespread, and the inflection point value varies depending on the factor and region type. This study constructs a nonlinear and interpretable analytical paradigm and, based on empirical results, proposes a new governance framework of “zoning–grading–staging–synergy”. It provides large-sample empirical evidence and theoretical support for the transformation of small-town park green spaces from “sectoral management” to “spatial governance”, offering significant policy value toward achieving the precise distribution and equitable sharing of regional green space resources.

The governance of land use carbon emissions is pivotal to achieving the goals of carbon peak and carbon neutrality. Rural revitalization significantly shapes the spatiotemporal patterns and evolutionary dynamics of land use carbon emissions, yet this relationship has received inadequate attention in existing literature. This study employs a combination of decoupling models, the Boston Matrix, spatial analysis, and interpretable machine learning models to conduct an empirical analysis of 124 regions in western China. The findings reveal diversified spatiotemporal evolution trends in rural revitalization land use carbon emissions. The decoupling relationship between rural revitalization and carbon emissions demonstrates a polarized nature, with over half of the assessed regions experiencing negative decoupling effects. The role of impact factors in decoupling relationships is characterized by a mixed nature, hierarchical intensity, nonlinear pathways, spatial heterogeneity and autocorrelation. The pathways of factor effects display nonlinear forms such as wave-like, inverted U-shaped, and U-shaped patterns, with the nature and intensity of effects dynamically shifting between “threshold mutations” and “inflection reversals” as factors evolve. The spatiotemporal evolution patterns, decoupling relationships, and SHAP values all exhibit significant spatial autocorrelation and form “spatial clusters” of various shapes. The decoupling of rural revitalization and carbon emissions in western China constitutes a complex systemic endeavor, necessitating comprehensive analysis from multiple dimensions—encompassing spatiotemporal evolution patterns, decoupling relationship, nonlinear mechanisms, and spatial effects—followed by the formulation of differentiated and precision-targeted governance strategies.

Water resources serve as a rigid constraint for urban sustainable development, yet existing studies still lack sufficient understanding of the decoupling effect and its nonlinear mechanism in urban water resource utilization. This study comprehensively employs the spatiotemporal dynamic matrix, decoupling model, and explainable machine learning methods to conduct an empirical analysis of 70 small cities in Guangxi, China. Findings: (1) From the integrated perspective of stock and flow, the dynamic patterns of water use are diversified. (2) The decoupling status is generally positive, with over 60% of counties decoupling, primarily characterized by weak decoupling. However, over 30% of counties are still in an unhealthy negative decoupling state, indicating that the problem of extensive use of water resources is still prominent. (3) Water resource endowment, population, urbanization, water supply facilities, and land use complexity are key factors affecting decoupling relationships. The effects of these factors exhibit nonlinear patterns such as L, N, U, inverted U, and parabolic patterns, accompanied by pronounced threshold effects and spatial heterogeneity. (4) By integrating the analysis results of the dynamics mode and decoupling effect, this study constructs a 4 × 3 systematic decision-making toolkit. It proposes differentiated and adaptive planning strategies for 12 zoning categories, providing a scientific basis and decision-making references for refined water resource governance in similar areas worldwide. The innovation of this study lies in establishing a nonlinear analytical framework that spans the entire process of “identification—diagnosis—attribution—planning”, advancing the research paradigm in this field from linear to nonlinear approaches.

As the global urban economy accelerates its transition from an “industrial economy” to a “service economy”, consumption has replaced investment as the core engine driving economic development. Commercial land serves as the physical foundation for consumer activities and plays a vital role in boosting urban economic vitality, enhancing residents’ quality of life, and promoting regional sustainable development when appropriately allocated. This study constructs a technical framework for analyzing the mismatch between commercial land supply and residential consumption demand, along with its impact mechanism, based on the integrated application of the multidisciplinary quantitative models such as the Boston Consulting Group Matrix (BCGM), Exploratory Spatial Data Analysis (ESDA), Decoupling Model (DM), and Explainable Machine Learning (EML). It conducts empirical research across 87 county-level cities in Gansu Province. The findings reveal that commercial land supply and consumption demand exhibit dynamic diversification, with prominent regional disparities and spatial autocorrelation characteristics. Commercial land in Gansu faces a severe mismatch, with demand exceeding supply and supply exceeding demand occurring simultaneously, and the former holding absolute dominance. The formation of mismatched relationships is influenced by many factors, exhibiting significant path nonlinearity, spatial non-stationarity, and relational interactivity. It is suggested that strategies of planning zoning and regional coordination be developed for mismatch governance, and differentiated management measures be implemented based on local conditions. This will provide a scientific basis for commercial territorial space planning and consumption policy design.

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development.

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system.

Wood is a natural and high-quality material for green and low-carbon buildings, and it is increasingly winning the favor of architects and consumers against the background of “dual carbon”. Exploring the current characteristics and trends of wood consumption in the construction industry (WCCI) and identifying its influencing factors are of great value for the scientific management of WCCI and the improvement of the comprehensive utilization efficiency of wood in the construction industry. In this study, the Boston Consulting Group Matrix and Geodetector were used in combination for empirical analysis of WCCI in China from 2000 to 2021. It is found that the changes in WCCI in China present a diversified trend with significant regional differences. The WCCI market at the provincial scale in China is divided into star, lost, potential, and marginal spaces. WCCI in China has very complex influencing factors and their mechanisms of action, and the interaction of its factor pairs is manifested as bifactor enhancement and nonlinear enhancement. This study provides a great application value for rational planning of wood resource utilization and pushing buildings into a low-carbon and green future, and it inspires the government to pay more attention to the design of spatial policies rather than industry policies and more attention to the design of policy combinations rather than individual policies. In addition, in the management of WCCI under the background of ecological civilization, it is necessary to escape the set pattern of the wood industry and force more use of wood in buildings in the design of constraint policies for non-wood building materials (such as glass, aluminum, steel, cement, and other high-carbon and energy-intensive building materials).

To achieve the goals of “carbon peaking and carbon neutrality”, this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example. We defined the carbon intensity of urban industrial land as the industrial carbon emissions per unit area of land, which is a spatial mapping of urban industrial economic development and carbon spillover and a key indicator for urban and territorial spatial planning oriented towards the “dual carbon” goal. Findings: The carbon emission density of industrial land in the YREB varied greatly between cities and exhibited significant positive spatial autocorrelation. In addition, the geographical pattern and spatio-temporal evolution model of the urban industrial land carbon emission density had a very complex driving mechanism, and different factors had significant synergistic effects. Therefore, it is suggested that while striving towards the goal of “dual carbon”, the government should incorporate the carbon emission density indicator of urban industrial land into the urban and territorial spatial planning system, and based on the threshold of the medium suitable density, they should design differentiated management policies according to concrete urban policies and encourage cooperation among cities to jointly promote carbon emission management of urban industrial land. In policy design, emphasis should also be placed on highlighting the interactive effects of foreign direct investment, fiscal expenditure, and the number of patent authorizations as well as constructing a combination of policies centered around them to better leverage the impacts of globalization, government intervention, and innovation.

Logistics land is the spatial carrier for the development of logistics enterprises. Its evolution mode and driving mechanism determine the level of high-quality development of the logistics industry, and serve as an important basis for urban planning and territorial spatial planning. This study introduced a Boston consulting group (BCG) matrix and geographically weighted regression (GWR) spatial econometric models to carry out empirical research on the Yangtze River Delta (YRD), in an effort to provide scientific information for evidence-based decision-making by governments and enterprises. The scale and ratio of logistics land (LLS and LLR) in the YRD showed significant spatial heterogeneity and autocorrelation, cities with large logistics land use converging from clusters to belts from 2000 to 2020, and agglomerations with high logistics land ratio (LLR) migrating from inland to coastal areas. Diversified models of logistics land evolution also emerged, such as high scale–high speed cities, low scale–low speed cities, high scale–low speed cities, and low scale–high speed cities. In addition, the driving mechanism of LLS and LLR was very complex, with a great difference in the intensity, nature and spatial effects of the influence of different factors. The inspiration from empirical case studies is urgent to revise the planning norms and clarify the LLS and LLR control standards for logistics land use. Meanwhile, the synergistic development target of the logistics industry in the new era is changing from the manufacturing industry to the commerce and trade industry; the establishment of planning zoning and the designing of differentiated management policies significantly improve the planning applicability.