As urbanization accelerates, urban residents face increasing life stress and mental health challenges, while urban green spaces that provide restorative experiences play an important role in promoting physical and mental well-being. However, most green-space accessibility studies have paid limited attention to whether residents can obtain specific health-supporting services, such as therapeutic landscape benefits. To address this gap, this study proposed a Healing Opportunity Assessment Model that incorporates park therapeutic quality into a potential accessibility model and calculates the Healing Opportunity Index (HOI) to measure residents’ opportunities to obtain therapeutic landscape services within a 15-min active transport threshold. Using Harbin as a case study, the results indicate that fitness facility quantity (0.180), waterscape attractiveness (0.150), and service-facility convenience (0.144) are the most important factors affecting park therapeutic quality. Under the 15-min active transport threshold, the distribution of healing opportunities remains highly uneven, suggesting that access to health-supporting therapeutic functions is still insufficiently balanced and that substantial improvement is needed in the current urban park system. This study connects park accessibility with residents’ opportunities to obtain therapeutic landscape benefits, providing quantitative support for identifying underserved communities and improving the equitable provision of health-supporting green-space services.

Urban functional zones (UFZ) serve as fundamental units of human activity, and their spatial configurations significantly influence urban carbon emissions. However, current research often overlooks the heterogeneity of UFZ forms and the complex, multi-scale relationships underlying their impact on emissions. To address this gap, this study investigates the Yangtze River Delta (YRD) urban agglomeration by delineating UFZ from the dual perspectives of block function and form. By integrating multi-scale spatial analysis with explainable machine learning, this study establishes a morphological indicator system covering three dimensions: density, morphology, and structure. The results reveal a pronounced scale dependency in the effects of UFZ form on carbon emission intensity (CEI), with optimal analytical scales identified at 14 km for density, 5 km for morphology, and 7 km for structure. Notably, variations within these dimensions lead to distinct patterns of impact intensity, even within the same functional category. Furthermore, most indicators exhibit nonlinear, threshold-dependent effects on CEI. These findings provide actionable guidance for fine-grained urban planning and carbon mitigation strategies.