PROGRESS IN GEOGRAPHY ›› 2020, Vol. 39 ›› Issue (2): 265-275.doi: 10.18306/dlkxjz.2020.02.008

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Influencing factors of low-carbon behaviors of residents in Zhengzhou City from the perspective of cognition-behavior gaps

ZHANG Jingfei1,2, ZHANG Lijun1,2,*(), QIN Yaochen1,2, WANG Xia1,2, SUN Yingying1,2, RONG Peijun3   

  1. 1. Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, Henan, China
    2. College of Environment and Planning, Hennan University, Kaifeng 475004, Hennan, China
    3. College of Tourism and Exhibition, Henan University of Economics and Law, Zhengzhou 450052, China
  • Received:2019-03-08 Revised:2019-05-05 Online:2020-02-28 Published:2020-04-28
  • Contact: ZHANG Lijun E-mail:zlj7happy@163.com
  • Supported by:
    National Natural Science Foundation of China(41671536);National Natural Science Foundation of China(41501588);Key Scientific Research Projects of Institutions of Higher Learning in Henan Province in 2017(17A170006);China Postdoctoral Science Foundation(2017M622333)

Abstract:

Residents' low-carbon behavior-cognition relationship has been widely concerned by scholars since cognition-behavior separation is a key factor that hinders the construction of low-carbon cities and the transformation of residents' lifestyle. This study constructed a spatial-behavioral/cognitive analysis framework and used large-scale field survey and remote sensing data of Zhengzhou City in 2018, as well as expert scoring method to estimate the low-carbon behavior level and low-carbon cognition level of 1485 families based on their daily travel and consumption of household energy. The color coding method was used to classify their behaviors into the green type, the forced type, the susceptible type, and the red type based on cognition-behavior gaps, and the multiple Logistic regression model was used to analyze the influencing factors of different low-carbon behaviors. The research findings show that: 1) There are large cognition-behavior gaps in low-carbon behaviors of residents in Zhengzhou City, and the proportions of the four low-carbon behaviors vary greatly. 2) Green low-carbon behaviors are mainly concentrated in areas with highly mixed land use and convenient infrastructure, and forced low-carbon behaviors, susceptible low-carbon behaviors, and red low-carbon behaviors are randomly distributed in space. 3) The influencing factors of the four types of low-carbon behaviors are very different. Residents living in areas with a high walking index score and a high level of low-carbon cognition tend to exhibit green low-carbon behaviors, and have a high level of low-carbon behavior and low-carbon cognition. In areas where housing conditions are relatively poor and residents have low levels of education, the residents tend to exhibit forced low-carbon behaviors, their low-carbon cognition level is low but low-carbon behavior level is high. Residents of high-end residential buildings tend to show susceptible low-carbon behaviors, they have a high level of low-carbon cognition but a low level of low-carbon behaviors. Young residents with high economic status tend to exhibit red low-carbon behaviors and have a low level of low-carbon behavior and low-carbon cognition.

Key words: low-carbon behavior, built environment, behavior-cognition gap, multinomial Logistic regression, Zhengzhou City