1,206
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Internet use and willingness to participate in garbage classification: an investigation of Chinese residents

ORCID Icon & ORCID Icon
Pages 788-793 | Published online: 17 Jun 2020
 

ABSTRACT

This study contributes to the literature by investigating the factors factors affecting Chinese residents' willingness to participate (WTP) in garbage classification, paying particular attention to the role of Internet use. Data are drawn from the 2016 China Labour-force Dynamics Survey (= 13,499) and analysed using a recursive bivariate probit model. The results show that Internet use can motivate people to classify the household garbage, and it increases Chinese residents’ WTP in garbage classification by 4.7 percentage points. The results also show that WTP in garbage classification of Chinese residents is largely influenced by their Internet use via smartphones. Our findings highlight the importance of distributing garbage classification information via Internet media, especially smartphones.

Disclosure statement

There is no conflict of interest.

Data availability statement

The data that support the findings of this study are available from the leading author, Wanglin Ma, upon reasonable request.

Additional information

Funding

The authors would like to acknowledge the funding support from the National Natural Science Foundation of China [71903062]; and Lincoln University commerce faculty seed fund project [INT5066].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 205.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.