我国餐厨垃圾资源化处理规模的影响因素统计学分析

祝晓燕; 陈婷; 赵莹莹; 裘晓鹏; 殷峻; 冯华军; 张进锋

doi:10.13205/j.hjgc.202103024

我国餐厨垃圾资源化处理规模的影响因素统计学分析

doi: 10.13205/j.hjgc.202103024

祝晓燕^1,2,
陈婷^1,2,3, ,,
赵莹莹^1,2,
裘晓鹏⁴,
殷峻^1,2,3,
冯华军^1,2,3,
张进锋⁵

1. 浙江工商大学环境科学与工程学院, 杭州 310018;
2. 浙江省固体废物处理与资源化重点实验室, 杭州 310018;
3. 浙江工商大学分析测试中心, 杭州 310018;
4. 中国电建集团华东勘测设计研究院有限公司, 杭州 311122;
5. 维尔利环保科技集团股份有限公司, 江苏常州 213125

基金项目:

国家自然科学基金项目（51878611，51608480）。

详细信息

作者简介:
祝晓燕(1999-),女,本科生,主要研究方向为固体废物的统计分析与决策管理。

通讯作者:
陈婷(1985-),女,博士,副教授,主要研究方向为餐厨垃圾的资源化处理与物质代谢。chenting_15@mail.zjgsu.edu.cn

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出版历程
- 收稿日期: 2020-06-03
- 网络出版日期: 2021-07-19

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

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

摘要

摘要: 为了探明我国餐厨垃圾资源化项目处理规模的影响因素，采用有序logistic回归对29个国家级餐厨垃圾处理试点项目进行统计学分析，考察人均GDP水平、服务人口、处理工艺以及地理位置对餐厨垃圾处理规模的影响，并建立相关的回归预测模型。结果显示：处理工艺和地理位置对项目处理规模的影响不显著（P>0.05），而人均GDP水平和服务人口会显著影响餐厨垃圾的处理规模（P分别为0.007和0.013），其中，服务人口对处理规模的影响更大。由此可见，为确定合理的餐厨垃圾处理规模，应重点考虑服务人口和城市人均GDP水平。基于此，建立了餐厨垃圾资源化处理规模的回归预测模型，该模型的准确性检验可达到75.86%，对于餐厨垃圾处理规模决策分析及合理评估具有参考意义。
- 餐厨垃圾 /
- 处理规模 /
- 统计学分析 /
- 服务人口 /
- 人均GDP /
- 回归预测模型
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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