Global aging and intensifying heat extremes heighten outdoor thermal risk for older adults. Concurrent micrometeorological, physiological, and thermal-sensation data were collected from older park users in Xi’an, China, during sitting, slow walking, and square dancing. Predictors selected by statistical and machine learning methods were fed into gender-specific gradient boosting machine models of thermal sensation. Results indicated that: 1) Neutral physiological equivalent temperature (NPET) shifted markedly with activity. Older females showed lower NPET than older males during sitting, while during slow walking and square dancing, they exhibited higher NPET than males. 2) Older women maintained higher heart rates (HR) than men across all activities; while older men displayed a U-shaped core temperature (Tc) curve during slow walking. Limb skin temperatures (ST) exhibit more pronounced fluctuations with increasing activity intensity, whereas chest ST remained stable. 3) Thermal sensation vote (TSV) predictors differed by gender and activity. For men, ST dominated during sitting, HR during walking, and Tc during dancing; for women, TSV correlated strongly with STupper arm during sitting, while correlated jointly with Tc and STchest during square dancing. 4) The gender-specific TSV model, requiring only two to three localized physiological inputs, outperforms conventional indices in accuracy and feasibility. It directly informs age-friendly park design, enhances outdoor safety for older adults, and supports sustainable urban development.
A comparative review of the alignment between climate adaptation policies and studies helps identify and narrow their gaps, thereby enhancing the effectiveness of climate adaptation actions in the human settlements. This study systematically traced the development of policies and studies to reveal the evolution of their interactions. An analytical framework for assessing alignment was constructed across four dimensions—level of attention, adaptation target, adaptation action, and adaptation scale—to evaluate the alignment within core concerns. The results indicate that climate adaptation policies have embraced global coordination, while studies have gradually shifted from a theoretical focus to applied practice. The policy–research relationship has evolved from study-led development through parallel advancement to bidirectional interaction, though the efficiency and depth of their synergy remain limited. Across core concerns, overall alignment was relatively strong. Higher alignment was observed in cities and built environments, climate risk and disaster management, and water resource management; coastal areas and health and well-being showed moderate alignment; and ecosystems and biodiversity exhibited relatively low alignment. Alignment is generally strong in terms of adaptation target and action, but weak in regard to adaptation scale, with future scenario simulations and studies at regional and community scales remaining insufficient. Future studies should move beyond “outcome alignment” to “mechanism alignment” by optimizing policy–research bidirectional feedback mechanisms, promoting interdisciplinary collaboration and exchange mechanisms, and enhancing coordinated support through resource allocation and governance mechanisms. These findings contribute to the climate adaptation governance theory and provide insights and guidance for the future development of both policies and studies.
Mental health disorders have become a growing challenge globally. As research continues to emphasize the restorative properties of the environment, natural landscapes are increasingly recognized as an effective means to reduce disorders. Research on healthy landscapes may be enhanced and, in some cases, uniquely informed by human response data; however, the existing literature provides limited and insufficient synthesis on this topic. To address the gap, this study first proposes a four-stage classification framework for new measurement technologies based on the intrinsic processing phase through which individuals respond to environmental stimuli—neural processing, physiological adjustment, behavioral expression, and subjective representation—each aligned with its corresponding phase of the body’s response. Within each stage, a narrative review then synthesizes current technologies, their key indicators, applications, and potential mechanisms in healthy landscape research. Finally, we identify three emerging healthy landscape research directions based on the current research gaps, following: 1) applying multimodal measures for mechanistic insights, 2) clarifying affective subtype variability in environmental response, and 3) identifying multilevel mechanisms through embodied big data modeling. Overall, this work provides a theoretical lens and methodological foundation for probing complex human–environment interactions and for designing precision interventions in the digitally enhanced healthy landscapes.