Impact of precipitation on vegetable prices: Taking Guangzhou City as an example

ZHAI, Zhihong; Jiang, Minxing; CHANG, Chunying

doi:10.18402/resci.2021.02.09

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Bartlett sphericity test was performed on the data of five scales. From the perspective of total variance interpretation, the values of each scale were all greater than 90%, thereby indicating that the data can better explain the influencing factors of the port–city relationship [ 24 ]. Stationarity test on the influencing factors using the Formula (8) for each scale data for system stability analysis as shown in the VAR system stability discriminant graph ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

Multi-Scale Influencing Factors and Prediction Analysis: Dongxing Port–City Relationship

Zhang

et al. 2022

IJERPH

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This study calculates the dynamic concentration index, explores the evolution of the relationship between the Dongxing port and city, and predicts its future. The results indicate that the relationship between the port and city has three development stages, namely the low-level balanced development stage (2001–2008), the port development stage (2009–2014), and the urban development stage (2015–2019). Based on the country (China and Vietnam), province (Guangxi Zhuang Autonomous Region), district (Fangchenggang City), county (Dongxing City), and individual (resident) levels, a multi-scale index system of influencing factors was developed. The impulse response function model analyzed the influential factors in the relationship between port and city development. The influence is as follows: country (China) > country (Vietnam) > county > individual > city > province. Finally, the relationship between port and city development was predicted using an auto-regression differential moving average model. It is expected that Dongxing City will gradually transition from a port- and city-dominated stage to a new stage of coordinated development. Thus, by improving the proportion of the secondary and tertiary industries, managing the population density, introducing foreign capital, enhancing the innovation level, and improving the traffic facilities, high-quality development in Dongxing port–city can be achieved.

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Section: Resultsmentioning

confidence: 99%

Multi-Scale Influencing Factors and Prediction Analysis: Dongxing Port–City Relationship

Zhang

et al. 2022

IJERPH

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Exploring determinants of county suburban vegetable planting via the fsQCA-NCA approach

Wei,

Tang,

2024

Front. Sustain. Food Syst.

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Taking 26 county suburban areas of significant vegetable growers in Henan Province as a case study, this paper constructs a theoretical framework for market demand, market supply, and market environment on the basis of the conditions constraining the scaling up of vegetable production in county suburbs. Six indicators are selected to measure the vegetable planting scale in different counties/districts. Using fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) methods, ten configurations are analyzed. The results show that the main paths for achieving large-scale vegetable production in county suburban areas rely primarily on market demand, followed by market environment. On the basis of these findings, to improve vegetable production levels in Henan Province, efforts should focus on aligning with the willingness of vegetable growers, enhancing both the soft and hard environments for vegetable production, and nurturing and introducing leading vegetable enterprises to build the vegetable industry chain. These measures will gradually promote the realization of large-scale vegetable production in county suburban areas.

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Impact of precipitation on vegetable prices: Taking Guangzhou City as an example

Cited by 2 publications

References 11 publications

Multi-Scale Influencing Factors and Prediction Analysis: Dongxing Port–City Relationship

Multi-Scale Influencing Factors and Prediction Analysis: Dongxing Port–City Relationship

Exploring determinants of county suburban vegetable planting via the fsQCA-NCA approach

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