Environmental Sciences & Urban Planning
Spatio-temporal Evolution and Coupling Coordination Mechanism between Air Pollution Governance and Sports Venue Development
This study investigates the spatiotemporal evolution and coordination of air pollution control and sports venue construction in Fujian Province (2016-2024). Using a coupling coordination model and spatial analysis, it finds that air pollution control improved steadily, forming an inland dual-core pattern, while sports venue construction grew rapidly, displaying a coastal gradient. Coordination strengthened over time, evolving from fluctuation to sustained synergy, though regional disparities persist. High-level regions exhibit spillover effects, while underdeveloped areas face governance bottlenecks. The study recommends integrating pollution control with sports facility planning for balanced and sustainable urban development.
Executive Impact
Understand the scope and significance of this research for urban planning and public health initiatives.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The study highlights the growing interconnection between environmental pollution control and sports venue construction, driven by 'Healthy China' and ecological civilization strategies. It emphasizes a need for coordinated perspectives beyond internal sports system optimization. The theoretical basis is the sports ecological environment, which explores the dynamic relationship between sports and nature. Internationally, this field considers climate impacts, eco-friendly design, and construction pollution. In China, the concept was introduced in 1989, leading to the incorporation of air quality indicators into evaluation systems. The 'Green Olympics' initiative further integrated sports infrastructure with environmental governance. Empirical evidence shows pollution reduces health benefits of physical activity, making synchronized pollution control crucial for sustainable sports development. This research uses a coupling coordination model and spatial analysis to examine spatiotemporal coordination in Fujian Province (2016-2024), aiming to support sustainable urban sports development. Data sources include China's air quality monitoring platform (2016-2024) for all prefecture-level cities in Fujian Province, and Autonavi Open Platform API for sports venue points of interest (POIs). The methodology involves constructing an evaluation index system (Table 1) with six major air pollutants (PM2.5, PM10, SO2, NO2, CO, O3) and the number of sports venue POIs. Weights are determined using the entropy weighting method to ensure objectivity. A coupling coordination model (equations 1 and 2) quantifies the coordination degree between sports venue construction (U1) and air pollution control (U2), with coordination levels classified according to established criteria (Table 2).
Air pollution control in Fujian Province showed a clear upward trend from 2016 to 2024, though improvement rates were uneven. Initially, all cities had composite indices below 0.6 in 2016, with over half below 0.5. A rapid surge occurred between 2019 and 2020, followed by a moderation into high-level refinement. By 2024, Longyan and Nanping stabilized above 0.85, most cities clustered in the 0.7-0.8 range, and interannual fluctuations narrowed. Regional disparities persisted, with Zhangzhou remaining the lowest-ranking city, not surpassing the 0.7 median. Spatially, air pollution control exhibits a pronounced gradient and clear regional clustering (Figure 2). High-composite zones (≥ 0.8) are concentrated in Nanping and Longyan, forming a stable 'inland dual-core.' Transition zones (0.7-0.8) include Fuzhou, Xiamen, Ningde, and sometimes Quanzhou and Putian, showing strong spatial continuity. Low-composite areas (below 0.6) are exemplified by Zhangzhou, remaining relatively isolated. The spatial pattern is characterized by 'dual-core leadership, gradient distribution, and localized lag,' with strong inter-regional differences yet close spatial linkages.
Sports venue construction is fundamental for addressing fitness facility gaps and promoting mass sports. From 2016 to 2024, Fujian Province saw overall growth with significant regional imbalance (Figure 3). Fuzhou, Xiamen, and Quanzhou acted as core engines, far outpacing other cities. The initial phase (2016-2020) involved volatile growth, including a universal dip in 2020, followed by rapid growth from 2021 onwards. The leading trio entered a mature stage with indices sustained above 0.75. However, initially lagging cities like Putian and Zhangzhou showed only modest growth, highlighting persistent regional disparity challenges. Spatially (Figure 4), Quanzhou, Xiamen, and Fuzhou dominate with a pronounced and stable coastal agglomeration. Zhangzhou and Putian, initially behind, showed accelerated growth, forming secondary zones around the high-value cores. Zhangzhou surpassed 0.2 in 2021 and rose above 0.4 by 2024, while Putian exited the low-value range in 2024. Ningde and Nanping remained below 0.2 for extended periods, forming a crescent-shaped low-value zone across northwestern inland Fujian, reflecting clear development lags. Overall, sports venue construction exhibits a pronounced three-tier gradient, robust and stable over the long term.
The coupling coordination degree model was applied to analyze the coordinated development between sports venue construction and air quality levels across the nine prefecture-level cities from 2016 to 2024 (Figure 5). During 2016-2020, Xiamen, Fuzhou, and Quanzhou consistently ranked highest in coupling coordination, with Xiamen showing particularly stable development (0.800 by 2020). Most other cities were below 0.500, indicating disordered or barely coordinated stages and significant regional imbalances. From 2021-2024, the province's coordinated development level significantly improved, with all cities transitioning out of the disordered recession state. Zhangzhou and Putian steadily ascended into the Coordinated development stage, while other cities showed slower but continuous improvement. Spatially (Figure 6), coordination exhibits clear gradient differentiation and clustered agglomeration. High coordination areas (Xiamen, Fuzhou, Quanzhou) exceed 0.9, forming a continuous high-value coastal corridor. Zhangzhou and Putian, as secondary zones, improved their coordination, reflecting partial spatial transmission. Ningde, Nanping, Sanming, and Longyan largely fall within the over-harmonization range (0.4-0.6), forming low-value plates that contrast with the coastal high-value corridor, showing significant regional disparities and strong inertia in the spatial structure. Typological differentiation (Figure 7) reveals distinct patterns: Fuzhou and Xiamen (balanced and mature), Quanzhou (improvement-oriented), Zhangzhou and Putian (latecomer catch-up), Sanming and Nanping (environmental advantage with weak coordination improvement), and Longyan and Ningde (intermediate stage). These paths reflect city-specific resource endowments, functional positioning, and governance strategies, highlighting multi-path synergy. In conclusion, air pollution control showed steady improvement, while sports venue construction rapidly expanded later. Coordination strengthened, but disparities persist. High-level regions exhibit spillover effects, but a significant gap remains with backward regions, risking solidification without targeted measures. The study notes limitations, including potential time lags in data and limited scope to six pollutants in one region, suggesting future dynamic analysis and expanded coverage.
Enterprise Process Flow
| Region Type | Key Characteristics (2016) | Key Characteristics (2024) | Coordination Trajectory |
|---|---|---|---|
| Coastal Core (e.g., Xiamen, Fuzhou, Quanzhou) |
|
|
Balanced & Mature Coordination Model |
| Coastal Secondary (e.g., Zhangzhou, Putian) |
|
|
Latecomer Catch-up Type |
| Inland Core (e.g., Nanping, Longyan) |
|
|
Environmental Advantage with Weak Coordination Improvement |
| Inland Transition (e.g., Sanming, Ningde) |
|
|
Intermediate Stage with Stable Coordination |
Xiamen's Exemplary Coordinated Development
Xiamen consistently demonstrated one of the highest and most stable coupling coordination degrees throughout the analysis period (2016-2024), reaching 0.800 by 2020 and exceeding 0.9 in advanced coupling coordination by 2024. This can be attributed to its simultaneous high levels of air pollution control and significant investment in sports infrastructure. As a coastal city, Xiamen leveraged its resources to achieve a balanced and mature coordination model. The strategic integration of environmental governance with urban planning for sports facilities has created a virtuous cycle, enhancing both public health and sustainable urban development.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could realize by implementing AI-driven insights for urban planning and resource management, inspired by this research.
Your AI Implementation Roadmap
A typical phased approach to integrate AI-driven insights into your urban planning or environmental management strategies, drawing from the study's findings.
Phase 1: Initial Assessment & Data Integration
Leverage existing environmental data streams and sports facility databases. Conduct initial analysis to identify baseline coordination levels and regional disparities, similar to the Fujian Province study. Focus on data cleaning and standardization. Establish key performance indicators (KPIs) for both environmental quality and sports infrastructure.
Duration: 2-4 WeeksPhase 2: Predictive Modeling & Scenario Planning
Implement AI-driven predictive models to forecast future air pollution trends and sports venue demand. Develop coupling coordination models tailored to your specific region. Simulate various development scenarios (e.g., increased sports investment, stricter pollution controls) to understand their impact on coordination. Identify potential governance bottlenecks.
Duration: 4-8 WeeksPhase 3: Strategic Action & Monitoring Framework
Based on scenario analysis, formulate integrated urban development strategies. This includes aligning sports facility planning with pollution control measures. Establish a real-time monitoring system using IoT sensors for air quality and venue utilization. Implement feedback loops to allow for adaptive management and continuous improvement, mimicking Fujian's shift to high-level refinement.
Duration: 8-12 WeeksReady to Transform Your Operations?
Connect with our AI specialists to discuss how these insights can be tailored to your enterprise and start building a smarter future.