Urban area is a major source of CO₂ and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (R_H) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m⁻²·a⁻¹) and vegetation carbon sink capacity (771.49 gC·m⁻²·a⁻¹) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Greenway is an important linear public space that meets the diverse needs of the public. With the increasing popularity of greenway construction, the study of different greenway usage behavior in different socio-economic areas is of great value to the detailed design and construction of greenway in the future. Using the theory of environment-behavior studies (EBS), this study selected representative urban greenways and suburban greenways in Beijing, China, and conducted a questionnaire survey. Descriptive statistics and the chi-squared test are used to quantitatively analyze and summarize the behavior of greenway users. It is found that user gender, educational level, and residence (i.e., permanent resident or visitor), as well as season of use, are highly similar for urban greenways and suburban greenways in Beijing. However, due to a close relationship with urban location, modified by temporal, spatial, and personal factors, different behavioral characteristics are evident as follows: (1) Urban greenways are most closely related to daily life, work and education of urban residents, with short travel distances, short single use time, high frequency of use, high social and cultural value, wide distribution of age groups and wide distribution of time periods of use. (2) Suburban greenways are an important choice for residents’ outdoor activities on weekends and holidays. It is mainly used for ecological protection and sightseeing, supplemented by sports and fitness functions. It has the characteristics of low use frequency, high income level, wide distribution of time and distance, mainly used by young and middle-aged people, and used for a single time of more than 1 hour. Natural scenery along the trail is the most important attraction factor, and waterfront space and walking space are the main use behavior characteristics.

National parks are widely recognized as a key spatial conservation strategy for simultaneously safeguarding biodiversity and sustaining ecosystem services, yet comprehensive and causally robust evaluation frameworks are still needed to accurately assess their effectiveness and support evidence-based management. This study evaluates the conservation effectiveness of Qianjiangyuan National Park (QJYNP) from 2015 to 2024 using a multidimensional index, a PSM-DID quasi-experimental framework, and interpretable machine learning. The results show that the direct policy effect was significantly positive during 2015–2020, but shifted to a negative cumulative effect by 2024. The spillover effect in the buffer zone also turned significantly negative, potentially associated with tourism-related development shifting outward. In addition, slope, temperature, and population density were identified as key drivers of EEI heterogeneity with nonlinear threshold effects, while road-related impacts intensified over time. These findings indicate that quasi-experimental approaches better capture phased policy effects than conventional descriptive comparisons, and suggest that simple boundary controls are insufficient; instead, buffer zones should be incorporated into integrated management frameworks to mitigate external development pressures.