Measurements and Factors That Influence the Carbon Capability of Urban Residents in China
Open Access
- 23 April 2018
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 10 (4) , 1292
Abstract
Due to the rapid growth in residential energy consumption, there is an urgent need to reduce carbon emissions from the consumer side, which requires improvements in the carbon capability of urban residents. In this study, previous investigations of carbon capability were analyzed and classified into four dimensions: carbon knowledge capability, carbon motivation capability, carbon behavior capability, and carbon management capability. According to grounded theory, a quantitative research model was constructed of the carbon capability of urban residents in Jiangsu, which was used to conduct a questionnaire survey. SPSS 19.0 and LatentGOLD were employed to process the questionnaire data and the carbon capability of the residents was evaluated. The results showed that the residents of Jiangsu Province could be divided into six groups based on their different carbon capabilities, where these six major groups accounted for 28.19%, 21.21%, 18.33%, 15.84%, 9.88%, and 6.55% of the total sample. Gender, age, occupation, and educational level had significant effects on the carbon capabilities of residents, whereas the annual household income and household population had no significant effects. According to the characteristics of each cluster based on the four carbon capability dimensions, the six clusters were designated as “balanced steady cluster”, “self-restraint cluster”, “fully backward cluster”, “comprehensive leading cluster”, “slightly cognitive cluster”, and “restrain others cluster”. Quantitative analysis showed that 61.93% of the residents of Jiangsu reached the qualified rate for the carbon capability but the excellent rate was only 15.84%. Relevant policy implications are suggested based on these conclusions.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities (2017XKZD12)
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