Raw data of garbage sorting scale

To collect data efficiently and randomly, we distributed formal survey questionnaires through an online platform from May to June 2024 (Cudjoe et al., 2022; Govindan et al., 2022; Wang et al., 2019). We utilized Sojump (http://www.sojump.com/), a reliable platform that has been used to conduct tens of millions of surveys. Initially, a survey URL was generated by Sojump platform. Subsequently, we randomly distributed the survey URL through the WeChat China network platform. It was mandatory for all respondents to undergo official real-name authentication by WeChat China to ensure the quality of the survey. All respondents received rewards upon completion of their questions. Our survey covers 31 provinces in 6 major administrative regions of China, namely North China, Northeast China, East China, Central South, Southwest, and Northwest, which nearly represents the entire country. Since our research involved random sampling, we used the equation below to calculate the minimum sample size (given a confidence level of 95%, a standard deviation value of 0.5, and an error margin of 5%) to ensure the accuracy of the measurements and results.After rigorous screening based on criteria such as response time, false answers, and extreme values, we identified 1550 valid questionnaires, resulting in an effective recovery rate of 82.5%. The data in the table is our raw data, with 1550 participant data as rows and columns as the variables we measured, namely gender, age, education, household income, family size, obligation-based intrinsic motivation, enjoyment-based intrinsic motivation, green self-identity , green self-efficacy, green self-connection, self-development proactive garbage sorting behavior, participatory proactive garbage sorting behavior, constrained proactive proactive garbage sorting behavior and garbage sorting proactive help behavior.