waste classification
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2022 ◽  
Vol 14 (2) ◽  
pp. 801
Xin Shen ◽  
Bowei Chen ◽  
Markus Leibrecht ◽  
Huanzheng Du

The Chinese government is promoting a waste classification policy to solve the increasingly serious issue of cities being besieged by waste. Only few studies investigate whether residents’ understanding of garbage classification policy has an impact on their garbage classification behaviour and the nature of such impact. The purposes of this study are twofold: first, to explore conceptually the mechanism behind any moderating effects of perceived policy effectiveness (PPE) on waste classification and, second, to examine empirically if and how PPE influences the relationships between attitude (ATT), subjective norm (SN), perceived behaviour control (PBC), awareness of consequence (AC) and waste classification intention (WCI). The conceptual model of the study is developed by combining insights from the theory of planned behaviour, norm activation theory and value–belief–norm theory. A total of 351 questionnaires were administered in person to households in Bengbu, China. The results based on structural equation modelling with partial least squares show that PPE negatively moderates the relationship between AC and WCI. AC is more strongly related with the intention to classify waste when PPE is weaker. Likewise, when PPE is higher, people’s awareness of consequences becomes less important for WCI. The findings have significant implications in policymakers’ developing guidelines and offer a framework for implementing more effective waste classification policy.

Khadijah Khadijah ◽  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Rismiyati Rismiyati ◽  
Priyo Sidik Sasongko ◽  

2021 ◽  
Vol 2078 (1) ◽  
pp. 012056
Shuang Wu ◽  
Zeyu Li ◽  
Xinqiong Chen ◽  
Peiwen Zhong ◽  
Liangcai Mei ◽  

Abstract In order to better promote garbage classification, machine learning models are used to discover and solve garbage classification problems. First, the factor analysis is used to conduct field investigation and data analysis on residents' perception of waste classification. Second, convolutional neural network (CNN) is used to classify and recognize garbage images, which is used to assist the judgment of garbage classification. We should put forward some reasonable classification suggestions to better promote the problem of garbage classification.

2021 ◽  
Vol 172 ◽  
pp. 112789
Qixiang Cao ◽  
Xiaoyu Wang ◽  
Miao Yin ◽  
Shen Qu ◽  
Long Zhang ◽  

William Mulim ◽  
Muhammad Farrel Revikasha ◽  
Rivandi ◽  
Novita Hanafiah

2021 ◽  
Vol 13 (21) ◽  
pp. 11632
Yangyang Zhang ◽  
Wenfang Huang

S city in China has implemented a waste classification system and constructed a waste classification model with government-led market and public participation. In order to explore the effectiveness of waste classification input in S city, this paper conducts analyses from the points of view of the classification facility’s construction, environmental effectiveness, social acceptability and operation sustainability, based on interviews with and questionnaire surveys completed by related parties. The results show that the current waste classification facility system in S city is basically completed; high rates of both properties and residents comply with the waste classification system. S city has established a government-led waste classification pattern that depends on social participation. This pattern has been recognized and accepted by residents and is economically sustainable. At the same time, it is pointed out that the current marginal effectiveness of the waste classification input is showing a declining trend. Future investment should shift from investment in facilities and equipment to incentives for autonomous management by residents, and the corresponding evaluation of investment and effectiveness should also change accordingly. This requires the government to guide the refined management system.

2021 ◽  
Bo Feng ◽  
Ren Kun ◽  
Qingyang Tao ◽  
Han Honggui

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