INFLUENCING FACTORS OF THE SCALE OF FOOD WASTE TREATMENT IN CHINA: STATISTICAL ANALYSIS

ZHU Xiao-yan; CHEN Ting; ZHAO Ying-ying; QIU Xiao-peng; YIN Jun; FENG Hua-jun; ZHANG Jin-feng

doi:10.13205/j.hjgc.202103024

Volume 39 Issue 3

Jul. 2021

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ZHU Xiao-yan, CHEN Ting, ZHAO Ying-ying, QIU Xiao-peng, YIN Jun, FENG Hua-jun, ZHANG Jin-feng. INFLUENCING FACTORS OF THE SCALE OF FOOD WASTE TREATMENT IN CHINA: STATISTICAL ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 172-177,53. doi: 10.13205/j.hjgc.202103024

Citation:

ZHU Xiao-yan, CHEN Ting, ZHAO Ying-ying, QIU Xiao-peng, YIN Jun, FENG Hua-jun, ZHANG Jin-feng. INFLUENCING FACTORS OF THE SCALE OF FOOD WASTE TREATMENT IN CHINA: STATISTICAL ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 172-177,53. doi: 10.13205/j.hjgc.202103024

Citation:

ZHU Xiao-yan, CHEN Ting, ZHAO Ying-ying, QIU Xiao-peng, YIN Jun, FENG Hua-jun, ZHANG Jin-feng. INFLUENCING FACTORS OF THE SCALE OF FOOD WASTE TREATMENT IN CHINA: STATISTICAL ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 172-177,53. doi: 10.13205/j.hjgc.202103024

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INFLUENCING FACTORS OF THE SCALE OF FOOD WASTE TREATMENT IN CHINA: STATISTICAL ANALYSIS

doi: 10.13205/j.hjgc.202103024

ZHU Xiao-yan^1,2,
CHEN Ting^{1,2,3
,
,},
ZHAO Ying-ying^1,2,
QIU Xiao-peng⁴,
YIN Jun^1,2,3,
FENG Hua-jun^1,2,3,
ZHANG Jin-feng⁵

1. School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou 310018, China;
2. Zhejiang Provincial Key Laboratory of Solid Waste Treatment and Recycling, Hangzhou 310018, China;
3. Instrumental Analysis Center of Zhejiang Gongshang University, Hangzhou 310018, China;
4. Huadong Engineering Corporation Limited of the Power Construction Corporation of China, Hangzhou 311122, China;
5. WELLE Environmental Technology Group Co., Ltd, Changzhou 213125, China

Received Date: 2020-06-03
Available Online: 2021-07-19

Abstract

Abstract

In order to ascertain the influencing factors of the treatment scale of food waste treatment projects, this article used ordinal logistic regression to perform a statistical analysis on 29 national-level food waste treatment pilot projects, examined the influence of four factors including GDP per capita, service population, treatment process and geographic location on the scale of food waste treatment, and established a related regression prediction model. The results showed that the treatment process and geographic location had no significant impact on the scale of food waste treatment (P>0.05), while the per capita GDP and service population would significantly affect the processing scale of food waste treatment (P=0.007, P=0.013, respectively), in which the service population had a greater impact on the processing scale. Therefore, in order to determine a reasonable scale of food waste treatment, it was important to consider the serving population and per capita GDP level. Based on this, a regression prediction model for the scale of resource processing of food waste was established, and the accuracy of the model could reach 75.86%. This model had important reference and reference significance for the decision-making analysis and reasonable evaluation of the scale of food waste treatment.
- food waste,
- treatment scale,
- statistical analysis,
- service population,
- per capita GDP,
- regression prediction model

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References(21)

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