In the context of the global construction of low-carbon cities and residents’ pursuit of healthy living, the improvement in the urban walking environment has gradually been emphasized in the field of planning and transportation research. Using Harbin, China, as an example, this paper combines gradient boosting decision trees (GBDTs) and impact-asymmetry analysis (IAA) methods to explore the differences in residents’ preferences for the pedestrian environment needs in old and new urban areas, analyze the asymmetric relationship between walking environment factors and overall satisfaction, and provide a sound basis for the renewal and reconstruction of the walking environment in old urban areas and the improvement of the walking environment in new urban areas. The factors affecting the pedestrian environment in the old and new urban areas are similar and different, with the aesthetics and safety and the aesthetics and comfort of the pedestrian environment having a greater impact on the old and new urban areas, respectively. According to the results of the IAA, the old city should focus on improving green landscaping, street furniture, the uncivilized behavior of pedestrians, pavement encroachment, barrier-free facilities, and the speed of motor vehicles; the new city should focus on improving the building facade effect, the uncivilized behavior of pedestrians, and green landscaping.

Rangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching communities to cope with future changes. Herein, we examined how climate regulates the interannual variability of GPP in California’s diverse annual rangeland, based on the contemporary records of satellite derived GPP at 500-meter resolution since 2001. We built gradient boosted regression tree models for 23 ecoregion subsections, relating annual GPP with 30 climatic variables, to disentangle the partial dependence of GPP on each climate variable. The machine learning results showed that GPP was most sensitive to growing season precipitation, with a reduction in GPP up to 200 g C/m2/yr when growing season precipitation decreased from 400 to 100 mm/yr in one of the driest subsections. We also found that years with more evenly distributed growing season precipitation had higher GPP. Warmer winter minimum air temperature enhanced GPP in approximately two-thirds of the subsections. In contrast, average growing season air temperatures showed a negative relationship with annual GPP. When the pre-trained models were forced by downscaled future climate projections, changes in the predicted rangeland productivity by mid- and end of century were more remarkable at the ecoregion subsection scale than at the state level. Our machine learning-based analysis highlights key regional differences in GPP vulnerability to climate and provides insights on the intertwining and potentially counteracting effects of seasonal temperature and precipitation regimes. This work demonstrates the potential of using remote sensing to enhance field-based rangeland monitoring and, combined with machine learning, to inform adaptive management and conservation within the context of weather extremes and climate change.

Harbin, China, has a large population density and a large number of motor vehicles. To alleviate traffic congestion, based on the survey data of bike-sharing riders in the new and old urban areas of Harbin in May 2022, this paper uses an impact-asymmetric analysis and gradient enhancement decision tree to analyse the asymmetric relationship between bike-sharing travel environment elements and cyclists’ satisfaction, and the optimisation strategy for the bike-sharing riding environment was obtained so that more residents can choose to ride. This research shows that the infrastructure of the motorway in the old urban area had the greatest impact on the overall satisfaction, while the travel quality of the shared bikes in the new urban area had the greatest impact on the overall satisfaction. In addition, due to the differences in urban environments and satisfaction, planning directions are different when satisfying cyclists in the new and old urban areas. The old urban area should emphasise cycling comfort and road coherence to provide a good travel environment; however, the new urban area should focus on the operation of shared bikes to meet the needs of cyclists. Therefore, future research should formulate refined improvement strategies for different regions.