Yankai Wang

Dr. Yankai Wang

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
22 November 2023 in Foods
Verified
Journal Article
Open access
PDF file
YWYankai Wang
Yankai Wang
Haochen SHIHaochen SHI
Haochen SHI
YZYuyang Zhang
Yuyang Zhang
XLXinjian Li
Xinjian Li
Miaoxi ZhaoMiaoxi Zhao
Miaoxi Zhao
BSBinbin Sun
Binbin Sun
Food self-sufficiency has long been regarded as essential for understanding and managing urban and regional food systems; however, few studies have examined the food self-sufficiency of megacity regions within a comprehensive framework that distinguishes different types of agricultural land (i.e., arable land, horticultural landscapes, and waters). To fill these gaps, we took the Pearl River Delta as a case study and quantified the foodsheds of different types of agricultural land by calculating the land footprint of food consumption. On this basis, food self-sufficiency is defined as the ratio of available and required agricultural area for regional food demand. The results indicated that the self-sufficiency level provided by the arable land in the Pearl River Delta is low and cannot realize self-sufficiency at the regional and urban levels. The horticultural landscapes can provide self-sufficiency at the regional level, whereas the regions with water cannot, as their foodsheds extend over the boundary of the Pearl River Delta. For arable land, establishing a localized regional food system requires expanding the foodshed size. These findings provide evidence that megacity regions may face increasing difficulties in achieving self-sufficiency in the near future. This research can improve policymakers’ understanding of the sustainability and resilience of regional food systems in megacity regions.

29 July 2024 in Applied Geography
Verified
Journal Article
Closed access
Haochen SHIHaochen SHI
Haochen SHI
LYLinlin Yao
Linlin Yao
QLQiqi Liu
Qiqi Liu
YWYankai Wang
Yankai Wang
ZWZongcai Wei
Zongcai Wei
DMDing Ma
Ding Ma

3 July 2025 in Landscape and Urban Planning
Verified
Journal Article
Closed access
HHHao Huang
Hao Huang
Haochen SHIHaochen SHI
Haochen SHI
XLXinjian Li
Xinjian Li
YWYankai Wang
Yankai Wang

29 April 2025 in Applied Geography
Verified
Journal Article
Closed access
YWYankai Wang
Yankai Wang
QLQuanyu Liu
Quanyu Liu
TMTao Mei
Tao Mei
Haochen SHIHaochen SHI
Haochen SHI
MZMiaoxi Zhao
Miaoxi Zhao
BSBinbin Sun
Binbin Sun

28 July 2023 in Regional Studies, Regional Science
Verified
Journal Article
Open access
YWYankai Wang
Yankai Wang
BSBinbin Sun
Binbin Sun
Haochen SHIHaochen SHI
Haochen SHI

9 December 2025 in Land
Verified
Journal Article
Open access
PDF file
YXYing Xu
Ying Xu
YWYankai Wang
Yankai Wang
HLHelin Liu
Helin Liu
JHJialei Huang
Jialei Huang
YHYulin Huang
Yulin Huang
MLmei luo
mei luo
Understanding the spatiotemporal characteristics of residents’ leisure travel distances (hereafter referred to as “RLTD”) and their underlying influencing factors is pivotal to reducing leisure travel costs and enhancing travel experiences. However, scholars have yet to identify leisure travel behavior and quantify RLTD accurately, and the nonlinear effects of the built environment on such distances remain underexplored. Therefore, this study, selecting Guangzhou as the case, employed multi-source data to measure RLTD and utilized a random forest model to explore the nonlinear relationship between the built environment and RLTD. Our findings are as follows. (1) Leisure activities among Guangzhou residents are dominated by short- and medium-distance travel (<10 km). Furthermore, RLTD exhibits significant spatiotemporal heterogeneity: on weekdays, it follows a zonal pattern where distances increase from the urban core to the periphery; conversely, on weekends, low-RLTD areas show a multi-center agglomeration pattern. (2) Proximity to central business districts (CBD) and large commercial centers, as well as optimal parking facility provision, emerge as the strongest predictors of RLTD on both weekdays and weekends. (3) All built environment variables exert nonlinear effects on RLTD, with distinct thresholds between weekdays and weekends. Additionally, a noticeable interaction effect is observed between the “distance to CBD” variable and other covariates. This study implies that when designing targeted interventions to promote residents’ leisure travel experience, policymakers should account for the temporal variations in how the built environment complexly influences RLTD.