降水对蔬菜价格的冲击效应——以广州为例

doi:10.18402/resci.2021.02.09

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

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.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. 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/Abstract

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

翟志宏, 江民星, 常春英. 降水对蔬菜价格的冲击效应——以广州为例[J]. 资源科学, 2021, 43(2): 304-315.

ZHAI Zhihong, JIANG Minxing, CHANG Chunying. Impact of precipitation on vegetable prices: Taking Guangzhou City as an example[J]. Resources Science, 2021, 43(2): 304-315.

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变量符号		变量名称	变量取值	均值	标准差	最小值	最大值
月度	rain_m	降水量	月降水量/mm	169.8	161.6	0.0	834.6
	lncx_m	菜心价格	月菜心价格,取对数	1.675	0.376	0.800	2.867
	lnsc_m	生菜价格	月生菜价格,取对数	1.398	0.444	0.311	2.773
	lndj_m	豆角价格	月豆角价格,取对数	1.908	0.356	1.059	2.989
日度	rain_d	降水量	日降水量/mm	5.6	15.2	0.0	222.1
	lncx_d	菜心价格	日菜心价格,取对数	1.662	0.411	0.000	3.401
	lnsc_d	生菜价格	日生菜价格,取对数	1.383	0.477	0.000	3.584
	lndj_d	豆角价格	日豆角价格,取对数	1.895	0.381	0.956	3.178

变量		Lag	LL	LR	FPE	AIC	HQIC	SBIC
月度	lncx_m	12	-915.278	19.903*	646.366*	12.142*	12.534	13.107
	lnsc_m	8	-971.266	10.183	1065.01*	12.645*	12.911	13.301
	lndj_m	12	-870.738	24.297*	369.118*	11.582*	11.974	12.547
日度	lncx_d	8	-16726.4	42.168	1.673*	6.190*	6.205*	6.232
	lnsc_d	12	-16859.5	8.862	2.046*	6.392*	6.414	6.454
	lndj_d	19	-14614.0	15.592	1.638*	6.169*	6.206	6.275

	原假设	chi²	df	Prob>chi²
月度数据	rain_m不是lncx_m的格兰杰原因	69.341	12	0.000
	lncx_m不是rain_m的格兰杰原因	22.946	12	0.028
	rain_m不是lnsc_m的格兰杰原因	67.123	8	0.000
	lnsc_m不是rain_m的格兰杰原因	21.523	8	0.006
	rain_m不是lndj_m的格兰杰原因	49.436	12	0.000
	lndj_m不是rain_m的格兰杰原因	39.801	12	0.000
日度数据	rain_d不是lncx_d的格兰杰原因	71.249	8	0.000
	lncx_d不是rain_d的格兰杰原因	13.021	8	0.111
	rain_d不是lnsc_d的格兰杰原因	109.230	12	0.000
	lnsc_d不是rain_d的格兰杰原因	13.865	12	0.309
	rain_d不是lndj_d的格兰杰原因	52.739	19	0.000
	lndj_d不是rain_d的格兰杰原因	34.912	19	0.014

时期	lncx_m对lncx_m 的贡献	rain_m对lncx_m 的贡献	lnsc_m对lnsc_m 的贡献	rain_m对lnsc_m 的贡献	lndj_m对lndj_m 的贡献	rain_m对lndj_m 的贡献
1	1.000	0.000	1.000	0.000	1.000	0.000
2	0.879	0.121	0.883	0.117	0.969	0.031
3	0.879	0.121	0.880	0.121	0.959	0.041
4	0.870	0.130	0.875	0.125	0.945	0.056
5	0.873	0.127	0.873	0.128	0.935	0.065
6	0.840	0.160	0.881	0.119	0.937	0.063
7	0.804	0.196	0.871	0.129	0.937	0.063
8	0.791	0.209	0.850	0.150	0.929	0.071
9	0.779	0.221	0.833	0.167	0.928	0.072
10	0.773	0.227	0.834	0.166	0.922	0.078
11	0.774	0.226	0.838	0.162	0.910	0.091
12	0.760	0.240	0.841	0.160	0.911	0.089
13	0.758	0.242	0.839	0.161	0.893	0.107
14	0.759	0.241	0.829	0.172	0.900	0.101
15	0.761	0.239	0.819	0.181	0.903	0.097
16	0.764	0.236	0.815	0.185	0.903	0.097
17	0.759	0.241	0.815	0.185	0.903	0.097
18	0.749	0.251	0.816	0.184	0.901	0.099
19	0.747	0.253	0.814	0.186	0.900	0.100
20	0.746	0.254	0.808	0.192	0.892	0.108

时期	lncx_d对lncx_d 的贡献	rain_d对lncx_d 的贡献	lnsc_d对lnsc_d 的贡献	rain_d对lnsc_d 的贡献	lndj_d对lndj_d 的贡献	rain_d对lndj_d 的贡献
1	1.000	0.000	1.000	0.000	1.000	0.000
2	1.000	0.000	0.999	0.001	1.000	0.000
3	1.000	0.000	0.999	0.001	0.999	0.001
4	1.000	0.000	0.997	0.003	1.000	0.001
5	0.999	0.001	0.995	0.005	1.000	0.000
6	0.998	0.002	0.991	0.009	0.999	0.001
7	0.995	0.005	0.986	0.014	0.998	0.002
8	0.991	0.009	0.979	0.021	0.996	0.004
9	0.983	0.017	0.968	0.032	0.994	0.006
10	0.977	0.023	0.957	0.043	0.992	0.008
11	0.971	0.029	0.946	0.054	0.990	0.010
12	0.965	0.035	0.935	0.066	0.989	0.012
13	0.961	0.039	0.923	0.077	0.988	0.012
14	0.956	0.044	0.912	0.088	0.988	0.012
15	0.952	0.048	0.903	0.097	0.988	0.012
16	0.948	0.052	0.894	0.106	0.988	0.012
17	0.945	0.055	0.886	0.114	0.988	0.012
18	0.942	0.059	0.878	0.122	0.989	0.011
19	0.938	0.062	0.871	0.129	0.989	0.011
20	0.936	0.064	0.864	0.136	0.989	0.011
21	0.933	0.067	0.858	0.142	0.990	0.010
22	0.931	0.069	0.852	0.148	0.990	0.010
23	0.929	0.072	0.847	0.154	0.990	0.010
24	0.927	0.074	0.841	0.159	0.990	0.010
25	0.925	0.075	0.836	0.164	0.991	0.009
26	0.923	0.077	0.832	0.168	0.991	0.009
27	0.921	0.079	0.827	0.173	0.991	0.009
28	0.920	0.080	0.823	0.177	0.991	0.009
29	0.918	0.082	0.819	0.181	0.991	0.009
30	0.917	0.083	0.816	0.184	0.992	0.008

降水对蔬菜价格的冲击效应——以广州为例

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

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