Evaluating low-carbon construction of urban green spaces in China through a process management perspective

Urban green spaces are vital carbon sinks and play a central role in low-carbon city development. However, traditional evaluations often focus on outcome indicators such as green space area and greening rate, neglecting the governance performance within the construction process. To bridge this gap, we introduce a five-process PDCOA (Plan-Do-Check-Outcome-Act) framework, applying Python-based web scraping and automated text mining to conduct an integrated assessment of 296 prefecture-level cities in China. Our results reveal a national average score of 44.10, with scores ranging from 10.42 to 92.81 (Beijing). Significant regional disparities emerge, with East China (mean score: 50.08) leading the nation, while regions like Southern China (38.25) lag considerably. The process evaluation uncovers a systemic imbalance: while cities perform relatively well in the P (59.81) and D (57.60) processes, they falter in the C (38.94), A (36.10) and O (31.21) processes. This study demonstrates that effective governance, not resource endowment, determines success in low-carbon green space development. It underscores the urgent need to shift from outcome-only metrics to process-driven continuous improvement, prioritizing adaptive feedback mechanisms and differentiated spatial strategies to strengthen urban green space governance.