资源科学 ›› 2021, Vol. 43 ›› Issue (2): 304-315.doi: 10.18402/resci.2021.02.09

• 资源经济 • 上一篇    下一篇

降水对蔬菜价格的冲击效应——以广州为例

翟志宏1,2(), 江民星3,4,5(), 常春英6   

  1. 1.暨南大学产业经济研究院, 广州 510632
    2.广东省气候中心,广州 510080
    3.南京信息工程大学商学院,南京 210044
    4.南京信息工程大学江北新区发展研究院,南京 210044
    5.南京信息工程大学江苏人才强省建设研究基地,南京 210044
    6.广东省环境科学研究院,广州 510045
  • 收稿日期:2020-02-26 修回日期:2020-07-05 出版日期:2021-02-25 发布日期:2021-04-25
  • 通讯作者: 江民星
  • 作者简介:翟志宏,男,江西九江人,博士研究生,高级工程师,研究方向为气候经济学。E-mail: zhaizhgz@163.com
  • 基金资助:
    国家自然科学基金项目(71903099);江苏省高等学校自然科学研究面上项目(19KJB610019);南京信息工程大学人才启动基金项目(2018r089)

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

ZHAI Zhihong1,2(), JIANG Minxing3,4,5(), CHANG Chunying6   

  1. 1. Institute of Industrial Economics, Jinan University, Guangzhou 510632, China
    2. Guangdong Climate Center, Guangzhou 510080, China
    3. Business School, Nanjing University of Information Science & Technology, Nanjing 210044, China
    4. Development Institute of Jiangbei New Area, Nanjing University of Information Science & Technology, Nanjing 210044, China
    5. Research Center for Prospering Jiangsu Province with Talents, Nanjing University of Information Science & Technology, Nanjing 210044, China
    6. Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China
  • Received:2020-02-26 Revised:2020-07-05 Online:2021-02-25 Published:2021-04-25
  • Contact: JIANG Minxing

摘要:

降水对蔬菜价格波动有重要影响,分析降水的蔬菜价格冲击效应对菜价预测及市场供需调整具有重要意义。本文首先构建了一个包含降水因素的蔬菜供需动态模型,揭示逐日降水对菜价的影响机制,并阐明了降水对不同蔬菜冲击效应存在异质性的原因;然后基于广州市2004—2018年逐日的菜心、生菜和豆角3种蔬菜零售价格数据及降水数据,采用VAR模型、脉冲响应函数和预测方差分解方法来验证上述影响机制,并量化降水在月和日尺度上对3种蔬菜价格的冲击效应。研究发现:①一定条件下逐日过量降水对菜价具有正向冲击作用,即导致菜价上涨,且冲击效应与蔬菜需求价格弹性呈反向关系。②菜价受降水的冲击在不同时间尺度上异质性明显,日尺度的冲击效应表现更为敏感,降水对菜心、生菜和豆角价格冲击在日尺度上分别在第16、20和10 d达到最大,随后减弱;而月尺度的冲击效应具有明显滞后性(滞后一个月),冲击较强但持续性不强。③日降水对菜心、生菜和豆角价格波动的贡献率分别约8.3%、18.4%和1.0%;月尺度降水相对日尺度降水而言,对3种菜价的影响更大,贡献率分别约为24.0%、18.1%和10.7%。最后,本文提出了针对降水过量时期稳定蔬菜价格的政策建议。

关键词: 蔬菜价格, 冲击效应, 菜价波动, 降水, VAR模型, 脉冲响应函数, 广州

Abstract:

Precipitation variations have important impacts on vegetable prices. Identifying and analyzing the shock effects of precipitation on vegetable prices are of great significance for vegetable prices forecasting and market supply and demand adjustment. Based on a dynamic model of vegetable supply and demand with precipitation factors, we revealed the mechanism of daily precipitation impulse on the price of vegetables and the cause of the heterogeneity of precipitation impact on different vegetables. Subsequently, based on daily and monthly retail vegetable prices (cabbage, lettuce, and asparagus bean) and precipitation data in Guangzhou City from 2004 to 2018, we used vector autoregression (VAR), impulse response function (IRF), and forecast error variance decompositions to verify the identified mechanism, and analyzed the effect of precipitation on the prices of the three vegetables. The results are as follows. (1) Under certain conditions, daily excess precipitation has a positive impact, which will stimulate vegetable prices, and the impact is inversely related to the price elasticity of vegetable demand. (2) The impact of precipitation on the price of vegetables has clear heterogeneity on different temporal scales, and the daily scale is more sensitive than the monthly scale. Daily precipitation impacts on cabbage, lettuce, and asparagus bean prices reach their maximum at 16, 20, and 10 day, respectively and then weakened. Monthly precipitation impact, which is strong, is reflected in the next month but not lasting. (3) Compared to daily precipitation, monthly precipitation has a greater impact on vegetable prices. Daily precipitation impacts on cabbage, lettuce, and asparagus bean contribute 8.3%, 18.4%, and 1.0% of the price fluctuations, respectively, while monthly precipitation impacts contribute to a higher degree of 24.0%, 18.1%, and 10.7%. According to the results, this article proposed serval countermeasures to ensure the stable supply of vegetables.

Key words: vegetable price, shock effect, vegetable price fluctuation, precipitation, vector autoregression (VAR), impulse response function (IRF), Guangzhou