Population Migration Damages the Natural Environment: A Multilevel Investigation of the Relationship Between Residential Mobility and Pro-Environmental Behaviors

Abstract

Residential mobility is increasing worldwide, and it objectively boosts economic strength. However, frequent moves create a specific habitat in which environmental degradation is aggravated. This research explored the relationship between residential mobility and pro-environmental behavior (PEB) from the perspective of environmental adaptation. We conducted five studies to test the hypothesis that high residential mobility decreased private-sphere PEBs at both personal and regional levels. The results showed that high personal residential mobility (Study 1) and high regional residential mobility (Study 2) were negatively correlated with self-reported private-sphere PEBs. Study 3 suggested that individuals primed with a high (vs. low) residential mobility mindset showed less actual private-sphere PEBs. Studies 4 and 5 further demonstrated that the preference for collective benefits played a mediating role in this relationship. These findings extend the adverse impacts of residential mobility to natural environments and highlight the role of social habitat changes in understanding environmental degradation.

Keywords

residential mobility socioecological environment pro-environmental behavior pro-environmentalism environmental degradation

Introduction

Residential mobility, a key indicator of population migration, serves as a booster of economic development. Two centuries ago, America exploited new markets, obtained new resources, and achieved rapid economic development through population migration in westward expansion (Vandenbroucke, 2008). In recent decades, an enormous number of Chinese have been migrated from Western China to Eastern China, from rural areas to urban areas; the migrating population there reached 376 million in 2020, and in this process, the world has witnessed China’s economic boom. However, residential mobility is not without harm. Socioecological psychologists warn that high residential mobility has a negative influence on both individuals and communities (e.g., Oishi, 2010, 2014). Evidence suggests that high residential mobility contributes to individual maladjustment such as negative emotions (Jelleyman & Spencer, 2008), low-level well-being among introverts (Oishi & Schimmack, 2010), and even high mortality (Juon et al., 2003). High residential mobility is also related to crime, violence, and other antisocial behaviors in the community (McGee et al., 2011; Oishi & Kesebir, 2012; Sampson et al., 1997).

Beyond personal and interpersonal consequences, the foci of this study are the environmental costs of residential mobility. History shows that migration contributes to economic growth, but it may backfire and exacerbate environmental degradation. For example, bison were nearly wiped out during the westward expansion in the United States (Billington & Hedges, 1949), and residents in China have suffered from severe air pollution as mobility climbed (Huang et al., 2019). Thus, do these co-occurrences suggest the malevolence of residential mobility on the natural environment? This research investigates the relationship between residential mobility and pro-environmental behavior (PEB) to clarify whether population migration has a bearing on environmental destruction.

PEB and Social Habitats

Environmental protection is a social issue requiring cooperation and concerted efforts of all living forces of society. Thus, when it comes to the issue of protecting environment, the contribution of specific PEB is little but essential. To some extent, environmental protection is a kind of public goods that demands creation and maintenance (Dawes, 1980; Kollock, 1998), and personal PEBs can play a part in maintaining these public goods. However, the engagements of PEBs are often with self-sacrifices such as time, energy, and money. That is, PEBs, in many cases, involve a fundamental trade-off between individual benefits and collective benefits. Past research demonstrates that concern for collective benefits plays a crucial role in PEBs (Takala, 1991). Thus, if a social habitat placed little importance on collective benefits, people who live there might be less likely to engage in PEBs. Instead, they may pursue individual benefits at the sacrifice of natural environment.

In this research, we postulate that environments with high residential mobility are a kind of social habitat that disregards collective benefits, perhaps thereby hindering habitants’ PEBs. In terms of measurement, residential mobility can be divided into personal residential mobility and regional residential mobility. The former underlines the number of times an individual relocates, whereas the latter stresses the percentage of people in a given neighborhood who have recently relocated (Oishi, 2010). Both personal residential mobility and regional residential mobility create a specific habitat where individuals may experience high unpredictability and weak interpersonal bonds (Ellis & Del Giudice, 2019; Zuo et al., 2018). For a better adaptation, individuals may calibrate their minds and behaviors at an optimum level according to this habitat as well as its characteristics (Ellis & Del Giudice, 2019; Larsen & Buss, 2008). In this process, the trade-offs between individual benefits and collective benefits might be influenced. Accordingly, nonecological or apathetic behaviors could be encouraged, which may further lead to ecological tragedies, such as the abuse of bison and the mismanagement of industrial fumes.

The Effects of High Residential Mobility on Pro-Environmental Trade-Off

We next elaborate how a social habitat with high residential mobility discourages the preference for collective benefits by undermining innate altruism and weakening structural systems that safeguard collective benefits.

First, high residential mobility might undermine the prerequisites for both kin altruism and reciprocal altruism. In the perspective of social dilemma, nonecological or apathetic behaviors are individually rational (Kollock, 1998; Weber et al., 2004; Yamagishi & Cook, 1993). Altruism makes it possible for individuals to get out of individual rationality. Animals, including humans, tend to benefit and affiliate with genetically related individuals, which is known as kin altruism (e.g., Hamilton, 1964; Lieberman & Lobel, 2012). Kin altruism often enhances fitness by increasing a genetic relative’s chances of reproduction and survival (Sng et al., 2018). For individuals with high personal residential mobility, however, frequent moves make them far away from genetically related individuals. In the same vein, for an area with high regional residential mobility, genetic relatedness could also decrease. Such niches diminish the payoff utility of kin altruism, and thus individuals may show less kin-directed behaviors. Some evidence supports this viewpoint. For example, Lieberman and Lobel (2012) found that coresidence duration predicts the level of kin altruism. McNamara and Henrich (2017) studied kin altruism in Yasawa Island in which geographic factors limit residential mobility and found that residents there showed strong cooperation. Being with less kin altruism, individuals give more weight to their individual benefits than collective benefits. Therefore, PEBs will be discouraged in mobile habitats.

Reciprocal altruism is another important type of altruism. Individuals benefit others with the expectation that the recipients will reward themselves in the future. Reciprocal altruism is a biologically adaptive mechanism (Trivers, 1971). But this claim heavily depends on reoccurrence. Durable and frequent interactions ensure a payoff of the investments from the recipients or a third party (Axelrod & Hamilton, 1981). By contrast, if the interaction only happens once, reciprocal altruism is unlikely to emerge. And this situation is common to see in mobile habitats. For individuals with high personal residential mobility, frequent moves break their ties with neighborhood, school, and company (Cavanagh & Huston, 2006). It is unpredictable whether they will meet these people again in the future. If individuals move away, there is a risk of no returns on their reciprocal altruism. Likewise, for an area with high regional residential mobility, the recipients that individuals ever interacted with are likely to move out of this area, and thus individuals’ kindness cannot be paid off (Zuo et al., 2018). In response, people with high mobility may show less reciprocal altruism but a higher motivation for immediate individual benefits.

Second, high residential mobility habitats lack effective operating mechanisms to protect collective benefits within common goods. To solve social dilemmas, as psychologists noted, we should not only keep an eye on people’s innate altruistic tendencies but also create external structural systems to promote commitments, supervision, and sanctions (Kollock, 1998; Weber et al., 2004). Specific to PEBs, we first need a commitment system to elicit and enhance individuals’ commitment to environmental protection. Abundant evidence suggests that commitment plays an important role in promoting engagement in PEBs (e.g., Afsar & Umrani, 2020; Terrier & Marfaing, 2015). Communication herein is an imperative tool to elicit commitment by sharing information among community members and increasing trustworthiness (for a review, see Kollock, 1998). However, in a mobile environment, be it personal or regional, individuals have few opportunities for face-to-face communications to speak out and understand others’ possible decisions in PEBs. With the uncertainty of others’ choices, individuals may behave selfishly to avoid free riders in environmental protection. In addition, environmental propaganda and sermon from communities and local government agencies are unlikely to continually influence individuals who move frequently, nor work effectively in areas with high residential mobility. Thus, propaganda and sermon may also fail to generate environmental commitment. Less commitment to collective benefits could finally decrease PEBs.

The monitoring system was another important part of the operating mechanisms to protect collective benefits. It is widely believed that monitoring could contribute to collective benefits (e.g., Kollock, 1998; McCain, 2007). In particular, Zuo and colleagues (2018) found that under surveillance, people primed with a high residential mobility mindset decrease their antisocial behaviors. The monitoring system works through wrongdoing exposure and conscience. Unfortunately, high anonymity in a mobile area creates a barrier to identifying people who transgress or infringe on collective benefits (e.g., littering). The identifiability for individuals with high personal residential mobility is low, too. It is hard to connect them to their transgression. Furthermore, moral pressure varies with identifiability. Environments with high residential mobility are anonymous, thereby creating less group pressure. Therefore, people who act selfishly in these niches are unlikely to be judged by their consciences; nonecological behaviors then may occur.

Sanction system constitutes the last part of the operating mechanism to protect collective benefits. Although sanction itself can be costly, it is effective in sustaining cooperation (Baldassarri & Grossman, 2011; Sutter et al., 2010). For example, Yamagishi (1986) found that severe sanctions improve contributions to public goods of individuals with low trust. But in an environment with high residential mobility, low identifiability may free transgressors from actual sanctions. Reputation plays an important role in social sanctions. Generally, efforts toward collective benefits confer a good reputation; a good reputation is beneficial to obtaining social support and high status, thereby improving the chance of successful survival and reproduction. However, it will be hard for people to gather relevant information about a person who moves frequently. High regional residential mobility will be harmful to reputational information transmission (Zuo et al., 2018). In other words, selfish acts in such niches carry a lower risk of social sanction. Consequently, collective benefits will be less concerned and thus PEBs will be discouraged.

Different Roles of Residential Mobility in Two Types of PEB

For the above reasons, we hypothesized that increasing residential mobility (both personal residential mobility and regional residential mobility) is negatively related to PEB. The types of PEBs are distinguished in this research as well. PEB is considered to be multidimensional, and it is generally divided into two types: private-sphere PEB and public-sphere PEB (e.g., Lu et al., 2017; Stern, 2000; Tam & Chan, 2017). However, we expected that residential mobility may play different roles in these two types of PEBs.

Private-sphere PEBs refer to the practical behaviors carried out by individuals in their daily life and work. Through these behaviors (e.g., garbage classification, water and electricity conservation, and recycling), individuals can protect the environment through their own individual efforts (Lu et al., 2017; Mi et al., 2020; Stern, 2000). By contrast, public-sphere PEBs focus on conveying an attitude toward and concern regarding environmental problems (Lu et al., 2017). Such behaviors involve civic engagement in the sociopolitical arena, such as signing petitions, complaining about environmental pollution events, and donating money to conservation causes (Larson et al., 2015).

We hypothesized that residential mobility was related to private-sphere PEBs but unrelated to public-sphere PEBs for two reasons. A less-important reason is that private-sphere PEBs are more costly in time and energy and usually diminish hedonic happiness (Larson et al., 2015), whereas public-sphere PEBs sometimes provide additional benefits (e.g., recognition) other than environmental protection. This difference makes the gains between the two types of PEBs unequal. A major reason is that private-sphere PEBs are mainly motivated by environmental protection whereas public-sphere PEBs may be driven by other motivations (e.g., political engagement). There is some evidence for this claim. For example, a cross-cultural study shows that environmental awareness is more correlated with private-sphere PEBs than with public-sphere PEBs (Tam & Chan, 2017), which implies the diverse motivations of public-sphere PEBs. In that case, public-sphere PEBs are not mere contributions to public goods, and thus we can no longer discuss public-sphere PEBs within the trade-off between individual benefits and collective benefits. For these reasons, this research focuses on the effect of residential mobility on private-sphere PEBs rather than on public-sphere PEBs.

The Current Research

Five studies were designed and conducted to test our hypothesis that residential mobility is negatively related to private-sphere PEBs. All studies were conducted in China where migrations frequently occur and environmental problems are increasingly pressing. Specifically, Study 1 measured individuals’ personal residential mobility and their self-reported private-sphere and public-sphere PEBs with the aim of verifying the association between personal migrations and private-sphere PEBs. Study 2 explored how regional residential mobility related to individuals’ self-reported private-sphere and public-sphere PEBs based on individuals residing in different areas as subjects in hopes of verifying the association between migrations within an area and private-sphere PEBs. Study 3 tested the causal relationship between residential mobility and private-sphere PEBs. We primed participants’ personal residential mobility mindset and examined whether such mindset influenced subsequent PEBs. Notably, we measured participants’ actual but not self-reported PEBs. We focused on one typical private-sphere PEB, that is, paper saving behavior, which reflects the awareness of resource conservation (Van Den Broek et al., 2017). Studies 4 and 5 further examined the mediating role of the preference for collective benefits in the association of residential mobility and private-sphere PEBs. We manipulated personal residential mobility mindsets in Study 4 and regional residential mobility in Study 5. We then required participants to indicate their preferences for collective benefits and their willingness to engage in some specific PEBs.

Study 1

This study aimed to clarify the relationship between personal residential mobility and PEBs. We measured participants’ number of moves as an index of personal residential mobility. Individuals’ relocation history shapes their microhabitat. The conditions and organisms in the immediate vicinity could be different between individuals who move frequently and those who have not moved. Frequent moves demotivate altruism and increase the possibility of escaping from monitoring and sanctions for wrongdoings, which may facilitate the preference for individual benefits. Thus, we expected that individuals who had experienced more relocations would show fewer private-sphere PEBs.

Method

Participants

To determine the sample size, we conducted an a priori power analysis using G*Power 3.1 (Faul et al., 2007). The previous research on residential mobility showed a small to medium effect size (see Zuo et al., 2018); thus, we expected a similar effect size (r = .25). With power set to .85 and alpha at .05, the power analysis suggested collecting data from N = 137 participants. To account for participant attrition and data exclusions, 189 Chinese participants were recruited from a campus bulletin board system. Two underage participants and 11 participants who failed an attention check in the questionnaire were excluded, leaving a final sample size of 176 (111 women). The ages ranged from 18 to 43 years, with a mean of 23.80 years (SD = 3.30).

Measures and procedures

After giving consent, participants completed a survey through an online system. The survey consisted of a series of questionnaires including personal residential mobility, PEBs, demographic variables (gender, age, and socioeconomic status [SES]), and some other variables used in separate research. Finally, the participants were debriefed and rewarded.¹

Personal residential mobility

To measure participants’ personal residential mobility, they were required to answer the following question: “How many times have you moved to a totally new neighborhood or town since your kindergarten age?” The mean number of total moves was 2.20 (SD = 1.86), ranging from 0 to 10. Because the distribution of house moves was positively skewed (skewness = 1.11, kurtosis = 1.69), we added 1 to each value and then took the log of 10. We used the data after logarithmic transformation in the statistical analyses.²

PEBs

The Chinese version of Private and Public Environmentally Oriented Behaviors (Hunter et al., 2004) was used to measure PEBs. The scale consisted of two dimensions: private-sphere PEBs and public-sphere PEBs. Both dimensions were measured through three items (e.g., “sort glass, or tin, or plastic, or newspapers and so on for recycling” for private-sphere PEBs and “been a member of any group whose main aim is to preserve or protect the environment” for public-sphere PEBs). Participants indicated their agreements on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). The corresponding items were averaged to create measures for private-sphere PEBs (M = 3.61, SD = 1.29; α = .56)³ and for public-sphere PEBs (M = 2.12, SD = 1.19; α = .60). Although the reliability of subscales was relatively low, the scale as a whole had an acceptable reliability in our study (α = .67).

Demographic variables

Participants were also required to report some demographics in the survey including gender (0 = men, 1 = women), age, and SES. In particular, participants were asked to rate the SES of their family (M = 2.50, SD = 0.73) on a 5-point Likert-type scale (1 = the lower class, 5 = the upper class).

Results and Discussion

Correlation analyses were conducted to examine whether personal residential mobility was correlated with PEBs (see Table 1). The results showed that personal residential mobility was significantly and negatively correlated with private-sphere PEBs (r = −.21, p = .006). By contrast, there was no significant relationship between personal residential mobility and public-sphere PEBs (r = −.08, p = .315). The results were consistent with our expectation.

Table 1.

Descriptive Statistics and Correlations Among the Variables, Study 1.

Variables	1	2	3	4	5
1. Personal residential mobility	—
2. Gender	.06	—
3. Age	.16*	−.16*	—
4. SES	.20**	.22**	−.03	—
5. Private-sphere PEBs	−.21**	−.04	.08	−.01	—
6. Public-sphere PEBs	−.08	−.02	−.01	−.15*	.39***

Note. SES = socioeconomic status; PEB = pro-environmental behavior.

p < .05. **p < .01. ***p < .001.

To control for the shared variance between personal residential mobility and demographic variables, we further conducted two multiple regressions (see Supplemental Table 1). The analyses of the regression where we controlled for gender, age, and SES showed consistent results. Personal residential mobility was a significant predictor of private-sphere PEBs (β = −.236, t = −3.08, p = .002, 95% confidence interval [CI] = [−1.893, −0.413]), whereas its prediction on public-sphere PEBs was not significant (β = −.047, t = −0.60, p = .548, 95% CI = [−0.902, 0.481]). Taken together, individuals who had experienced frequent moves were inclined to reduce their efforts in private-sphere PEBs; however, their efforts in public-sphere PEBs were much the same as others’ efforts.

The findings suggested that personal residential mobility was related to private-sphere PEBs but was unrelated to public-sphere PEBs. It implied that residential mobility is associated not only with negative interpersonal consequences but also with environmental problems. We further tested the relationships at the regional level in the next study.

Study 2

Mobility perceptions are influenced not only by one’s relocation history but also by others’ relocations in the surroundings. When others’ relocations occur frequently, individuals will feel that their social habitats are unstable even if they themselves have experienced few relocations. High regional residential mobility can demotivate altruism and weaken the external systems that facilitate cooperation. Thus, one may assume that regional residential mobility is negatively related to private-sphere PEBs. In this study, to examine whether provincial residential mobility is correlated to private-sphere PEBs, individuals distinguished by the provinces of China were required to report their private-sphere and public-sphere PEBs.

Method

Participants and procedure

The data for this study were derived from the 2013 Chinese General Social Survey (CGSS), which is a large-scale and continuous social survey program conducted nationwide in China for which all of the data are open access. The questionnaires of the CGSS 2013 involved participants’ demographics, their frequency in conducting a series of PEBs, and some other variables irrelevant to this research. This survey covers 28 provinces in mainland China (Xinjiang, Hainan, and Tibet were not included) and sampled 11,438 cases (5,756 men and 5,682 women). All participants were questioned through face-to-face interviews. One participant did not report his or her age, and the ages of the others ranged from 17 to 97 years, with a mean of 48.60 years (SD = 16.39). The effective units of data for each province ranged from 100 to 602.

Measures

PEBs

In CGSS 2013, a 10-item scale was developed to measure residents’ PEBs. Participants were required to indicate how often they had engaged in a series of PEBs in the previous year. Similarly, the scale consisted of two dimensions: private-sphere PEBs and public-sphere PEBs. However, in this case, both dimensions were measured through five items (e.g., “sorting garbage” for private-sphere PEBs and “donating for environmental protection” for public-sphere PEBs). Participants rated the frequency on a 3-point scale (1 = never, 2 = seldom, and 3 = usually). The corresponding items were averaged to create measures for private-sphere PEBs (n = 11,370, M = 1.84, SD = 0.47; α = .67) and for public-sphere PEBs (n = 11,381, M = 1.19, SD = 0.32; α = .76).

Individual-level predictors

Participants were also required to report some demographics in the survey including gender (1 = men, 2 = women), age, and SES. In particular, participants completed the MacArthur Scale of subjective SES (Adler et al., 2000) to assess their subjective SES (43 participants were unreported, M = 4.31, SD = 1.68). Participants were given a drawing of a ladder with 10 rungs of social class (1 = the lower class, 10 = the upper class), and they were required to indicate their own social class. These demographic variables were used as individual-level predictors of PEB in the following analyses.

Regional-level predictors

Four regional-level variables were used to predict PEBs in this study. All regional data were obtained from the National Bureau of Statistics of China (NBSC; http://www.stats.gov.cn). The most recent data as of 2020 were used. Specifically, we first calculated regional residential mobility based on data reported in the latest 2010 People Census of China. The census used registration data to report the number of people who immigrated into or emigrated for 2010. We calculated the proportions of the population that relocated based on the registrations and used it to represent regional residential mobility. In addition, some other regional data—including resident population for 2010, administrative area for 2010, and disposable income for 2013 (M = 18.84, SD = 7.84)—were obtained from the NBSC; these variables were controlled for at the regional level.

Results and Discussion

We used hierarchical linear modeling (HLM; Bryk & Raudenbush, 1992) to examine whether regional residential mobility was related to residents’ PEBs. Specifically, two multilevel models were constructed to predict PEBs from gender, age, SES, regional residential mobility, disposable income, resident population, and administrative area. For each multilevel model, one dimension of PEB was identified as the dependent variable (see Table 2). Each time, we first constructed a null model in which no predictors were entered to support the HLM. The intraclass correlation coefficients (ICC; Kreft & De Leeuw, 1998) were 0.127 for private-sphere PEBs (χ² = 1,877.57, p < .001) and 0.149 for public-sphere PEBs (χ² = 1,880.48, p < .001), indicating that 12.7% of the variance for private-sphere PEBs and 14.9% of the variance for public-sphere PEBs could be explained by regional-level variation.

Table 2.

Summary of PEBs Hierarchical Linear Models, Study 2.

PEB	χ² in null model	Individual-level predictors in random model	Regional-level predictors in full model
Private-sphere PEBs	1,877.57*	Gender (γ = .027, p = .008)Age (γ = −.003, p < .001)SES (γ = .023, p < .001)	Residential mobility (γ = −.003, p = .021)Disposable income (γ = .009, p < .001)Resident population (γ = −.001, p = .125)Administrative area (γ = −.001, p = .175)
Public-sphere PEBs	1,880.48*	Gender (γ = −.032, p < .001)Age (γ = −.002, p < .001)SES (γ = .017, p < .001)	Residential mobility (γ > −.001, p = .972)Disposable income (γ = .001, p = .544)Resident population (γ = .001, p = .387)Administrative area (γ > −.001, p = .325)

Note. PEB = pro-environmental behavior; SES = socioeconomic status.

p < .001.

We then conducted random coefficient models in which gender was uncentered while age and SES were grand-mean centered as predictors. The results showed that gender played a role in private-sphere PEBs (γ = .027, p = .008); women reported more private-sphere PEBs than did men. SES (γ = .023, p < .001) was positively related to private-sphere PEBs whereas age (γ = −.003, p < .001) was negatively related to private-sphere PEBs. For public-sphere PEBs, gender also played a role (γ = −.032, p < .001); however, men reported more public-sphere PEBs. SES (γ = .017, p < .001) positively predicted public-sphere PEBs whereas the prediction of age (γ = −.002, p < .001) was significantly negative. The results from random models were consistent with the previous findings (Hunter et al., 2004; Pisano & Lubell, 2017).

Next, to account for between-group variations in the effects on PEB, we further constructed full models. Both individual-level and regional-level predictors (except for gender) were grand-mean centered.

Overall, the individual-level equation was specified as follows

PEB = β_{0} + β_{1} (gender) + β_{2} (age) + β_{3} (SES) + r .

(1)

The regional-level model was specified as follows:

\begin{array}{l} β_{0} = γ_{00} + γ_{01} (regional residential mobility) \\ + γ_{02} (disposable income) \\ + γ_{03} (resident population) \\ + γ_{04} (administrative area) + u_{0} . \end{array}

(2)

β_{1} = γ_{10} + u_{1} .

(3)

β_{2} = γ_{20} + u_{2} .

(4)

β_{3} = γ_{30} + u_{3} .

(5)

In this model, γ₁₀ to γ₃₀ are the average slopes of gender, age, and SES for PEBs. Furthermore, γ₀₁ to γ₀₄ indicate the effects of regional residential mobility, disposable income, resident population, and administrative area on PEBs. The HLM analyses in the full models revealed that private-sphere PEBs were negatively related to regional residential mobility (γ = −.003, p = .021). Private-sphere PEBs were also positively related to disposable income (γ = .009, p < .001); the relationships between private-sphere PEBs and resident population (γ = −.001, p = .125) and administrative area (γ = −.001, p = .175) were not significant. By contrast, the relationship between public-sphere PEBs and regional residential mobility was not significant (γ > −.001, p = .972); public-sphere PEBs could not be predicted by disposable income (γ = .001, p = .544), resident population (γ = .001, p = .387), or administrative area (γ > −.000, p = .325). Therefore, the results supported our hypothesis (see Supplemental Materials for additional analyses). We found that individuals living in mobile areas were less engaged in private-sphere PEBs than people living in stable areas. We calculated this effect size using the method recommended by Lorah (2018); in the present study, f² = .019, indicating that regional residential mobility independently explains approximately 2% of the variance in private-sphere PEBs. According to the guidelines for interpretation of f², 0.02 is a small effect (Cohen, 1992), indicating that the present effect is small.

Study 2 revealed that regional residential mobility was related to private-sphere PEBs. Although the effect size is small, it extended the influence factors of PEBs to regional environmental cues.⁴ Taken together, Studies 1 and 2 evidenced that both personal residential mobility and regional residential mobility were related to private-sphere PEBs. Some recent studies of pro-environmentalism stress the uses of a multilevel approach that integrates both individual and regional factors as predictors of environmental behavior (Pisano & Lubell, 2017). We believe that our research is a good attempt to explore pro-environmentalism from both individual and regional perspectives.

Study 3

Studies 1 and 2 showed that residential mobility was negatively related to private-sphere PEBs; however, the causal relationship remains unknown. Therefore, Study 3 manipulated personal residential mobility by priming different residential mobility mindsets to test the causality. In addition, this study measured actual private-sphere PEBs because the PEBs in Studies 1 and 2 were self-reported. In particular, we focused on paper saving behavior. We selected paper use as the target behavior because it is relevant to students (as the subjects in this study were students) and because it is relevant in various cultures (Van Den Broek et al., 2017). We hypothesized that individuals primed with a high (vs. low) personal residential mobility mindset would show fewer paper saving behaviors.

Method

Participants

We first ran a power analysis to identify the number of participants required using G*Power 3.1 (Faul et al., 2007). The effect size was determined based on the previous research (see Zuo et al., 2018) with an estimate of d = 0.60. The power analysis revealed that 102 participants would be needed to achieve 85% power (1 − β) at a .05 alpha level. In total, 112 undergraduate and graduate students were recruited in exchange for monetary compensation. In total, 10 participants were excluded for not completing the task as requested, leaving a final sample of 102 individuals (22 men and 80 women). Participants’ ages ranged from 17 to 29 years, with a mean of 21.44 years (SD = 2.43).

Measures and procedures

Participants completed the study in a cubicle. They were informed that the tasks in the study were designed for two different goals: a writing task for measuring imagination and a paper-cutting task for evaluating the quality of scissors. Participants were first invited to complete a 10-min writing task in which we manipulated residential mobility mindsets (Oishi et al., 2012). Participants were randomly separated into two groups (mobile condition and stable condition). Participants in the mobile condition (N = 50) were required to imagine the following: “You are offered a job that you have always wanted. The job also involves moving to a different location every other year.” By contrast, participants in the stable condition (N = 52) were required to imagine as follows: “You are offered a job that you have always wanted. The job also involves living in one area for at least the next 10 years.” Participants then were required to write down their descriptions and feelings of the lifestyle in the corresponding scenes. This paradigm has been proven to be effective in manipulating residential mobility mindsets (Oishi et al., 2012).

Participants’ paper saving behaviors were then assessed in a paper-cutting task. This task was developed in the work of Catlin and Wang (2013) to measure PEBs; it has been demonstrated to be effective. The cover story of this task was to evaluate the performance of new scissors. Specifically, a new pair of scissors and a stack of approximately 500 g of plain, white A4 paper were provided for each participant, and there was an empty trash can in the laboratory. Participants were asked to cut five different shapes (namely, triangles, squares, trapezoids, pentagons, and hexagons) out of the paper, ostensibly for the purpose of gaining experience with the scissors and providing an evaluation. In addition, participants were informed that they could evaluate the scissors through some additional methods such as observing and cutting more shapes. Participants then rated the scissors on a variety of dimensions using 7-point semantic differential scales with endpoints such as bad/good, inferior/superior, would not buy/would buy, dislike/like, and dull/sharp. We weighed the paper both before and after the task and obtained the paper consumption data for each participant. Lower paper consumption means a higher level of paper saving behavior. Participants were also required to report their gender and age. Finally, they were debriefed and thanked.

Results and Discussion

To examine whether participants in the two conditions were homogeneous, we first conducted a chi-square test and an independent sample t test, and no differences were found between the two conditions in terms of gender and age (ps > .55). We also conducted independent-samples t tests to examine whether there were differences in evaluating the scissors between the two conditions, and no significant differences were found for any dimension (ps > .24).

We then conducted an independent-samples t test to examine the differences in paper saving behavior between the mobile condition and the stable condition (see Figure 1). The result showed that compared with participants in the stable condition (M = 4.98, SD = 1.23), participants in the mobile condition (M = 6.16, SD = 2.96) used more paper in the cutting task, t(100) = 2.61, p = .011, 95% CI = [−2.081, −0.277], d = 0.52, which means that individuals with high residential mobility mindset showed a lower level of paper saving behavior than individuals with low residential mobility. To control for the shared variance between residential mobility mindset, gender (0 = men, 1 = women), and age, we conducted a hierarchical multiple regression in which paper consumption was used as the outcome variable. The dependent variables were assessed from two blocks using the enter method: gender and age, and residential mobility mindset (see Supplemental Table 2). The result showed that residential mobility mindset significantly and positively predicted participants’ paper consumption (β = .251, t = 2.58, p = .011, 95% CI = [0.265, 2.063]). Therefore, our hypothesis was supported when we controlled for participants’ gender and age.

Figure 1.

Paper consumption as a function of residential mobility, Study 3.

In Study 3, we focused on one of the typical actual private-sphere PEBs, paper saving behavior, and we found that individuals who were primed with a high (vs. low) personal residential mobility mindset showed fewer paper saving behaviors. The results revealed that residential mobility was not only related to self-reported private-sphere PEBs but it was also negatively related to actual PEBs. The study further demonstrates the causal relationship between personal residential mobility and private-sphere PEBs.

Study 4

Study 4 further explored whether the preference for collective benefits mediate the relationship between personal residential mobility and private-sphere PEBs. In addition, we added a control condition in this study because we were unable to determine whether the effect in Study 3 was because a high residential mobility mindset discouraged PEBs or because a low residential mobility mindset contributed to PEBs.

Method

Participants

A power analysis was conducted to identify the number of participants required using G*Power 3.1 (Faul et al., 2007). The effect size was determined based on Study 3 with an estimate of f = .26. The power analysis revealed that 165 participants would be needed to achieve 85% power (1 − β) at a .05 alpha level. In total, 234 Chinese adults were recruited in exchange for monetary compensation on a third-party platform. Forty-five participants were excluded due to failure in the attention check, leaving a final sample of 189 individuals (88 men and 101 women). Participants’ ages ranged from 18 to 51 years, with a mean of 27.77 years (SD = 6.24).

Measures and procedures

Participants completed all the tasks online. To better capture the effect of residential mobility on PEBs, participants were required to imagine that they had started a new life in a new society called Bimboola, and they were asked to give their response in this particular context (Jetten et al., 2015). Specifically, they were informed that

One day, you wake up, and you suddenly find yourself in a new society called Bimboola. Your age, gender, knowledge, and skills have not changed. You realize that you are going to spend the rest of your life in this society . . . A few years later, you have become used to the life in Bimboola. You have established new relationships there and found a job you like in this society. You are now living in City A.

Participants then completed a writing task that was quite similar to the task in Study 3 with the aim of priming different residential mobility mindsets (Oishi et al., 2012). They were randomly separated into three groups: mobile condition, stable condition, and control condition. Participants in the mobile condition (N = 63) were informed that “the job involves moving to a different city every year.” Participants in the stable condition (N = 63) were informed that “the job involves living in City A for at least 10 years.” Participants in the control condition (N = 63) were not presented with any job information, nor were they invited to write down their feelings. We also conducted a manipulation check in this study through two items (“My life in Bimboola involves frequent moves” and “My residential condition in Bimboola is very stable”; α = .91). All the participants indicated their agreement on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). A high value after recoding means high residential mobility.

Next, we measured participants’ willingness to engage in private-sphere PEBs (M = 5.18, SD = 1.56; α = .72) and their preference for collective benefits (M = 4.83, SD = 1.30; α = .90), successively. Four items were developed to measure private-sphere PEBs (e.g., “I will purchase environmentally friendly cleaning products”). Four items were developed to measure the preference for collective benefits (e.g., “I am willing to make a certain personal sacrifice for community and city”). For these scales, participants indicated their agreement on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree).

In addition, participants in this study reported their gender, age (0 = men, 1 = women), and SES (1 = the lower class, 9 = the upper class). We also measured the preference for short-term benefits (M = 4.50, SD = 1.30; α = .82) because some research found it predicted lower PEBs (e.g., Corral-Verdugo & Pinheiro, 2006; Enzler, 2015). Four items partly adopted from the work of Joireman et al. (2012) were used to measure the preference for short-term benefits (e.g., “I believe it is unnecessary to sacrifice the present for the future since future outcomes can be dealt with at a later time”). Together with demographics, the preference for short-term benefits was added as covariate in the analysis.

Results and Discussion

We first conducted a one-way analysis of variance (ANOVA) to ascertain the effectiveness of the residential mobility mindset manipulation. The results showed that there was a significant effect in the manipulation check items among the three conditions (F = 109.65, p < .001). The results of post hoc test showed that participants in the mobile condition (M = 5.52, SD = 1.69) perceived their life to be more unstable than participants in the stable condition (M = 2.34, SD = 1.28; least significant difference (LSD): p < .001) and participants in the control condition (M = 2.41, SD = 1.10; LSD: p < .001). However, there was no significant difference between the stable condition and the control condition (LSD: p = .771). This probably suggests that those participants generally assume that they have a stable residential life.

An ANOVA was also conducted to test whether conditions could predict private-sphere PEBs. The result showed that there was no significant difference in private-sphere PEBs among the three conditions (F = 2.33, p = .100). The results of independent sample t tests revealed that participants in the mobile condition (M = 4.94, SD = 1.25) showed lower willingness to engage in private-sphere PEBs than participants in the stable condition, M = 5.37, SD = 1.07, t(124) = 2.09, p = .038, 95% CI = [0.023, 0.842], d = 0.37; no significant differences was found between the control condition (M = 5.23, SD = 1.12) and the mobile condition, t(124) = 1.39, p = .167, 95% CI = [−0.125, 0.712], d = 0.25, or between the control condition and the stable condition, t(124) = 0.71, p = .478, 95% CI = [−0.247, 0.525], d = 0.13. Given that the stable condition and the control condition showed no significance in the manipulation check items, we combined these two conditions into one group and compared them with the mobile condition. The results showed that participants in the mobile condition showed lower willingness to engage in private-sphere PEBs than the merged group, t(187) = 2.05, p = .041, 95% CI = [0.014, 0.712], d = 0.31. Therefore, it seemed that participants who were primed with a high personal residential mobility mindset decreased their private-sphere PEBs.

Next, we tested whether personal residential mobility manipulation had indirect effects on private-sphere PEBs via the preference for collective benefits. Only the data in the mobile and stable conditions were included in this part. The result of the regression analysis where age, gender, SES, and the preference for short-term benefits were controlled for (see Supplemental Table 3) revealed that participants in the stable condition (M = 5.07, SD = 1.22) tended to have a preference for collective benefits compared with participants in the mobile condition, M = 4.37, SD = 1.43, β = −.261, t = −3.11, p = .002, 95% CI = [−0.583, −0.129]. The reference for collective benefits significantly and positively predicted private-sphere PEBs, β = .677, t = 10.02, p < .001, 95% CI = [0.467, 0.696]. We performed a mediation analysis to test the indirect effect with the PROCESS for SPSS (Hayes, 2013) using 5,000 bootstrap samples. The result showed that the indirect effect via the preference for collective benefits was significant, SE = .06, 95% CI = [−0.296, −0.069], and that the direct effect was not significant after considering the preference for collective benefits, t = −0.28, p = .780, 95% CI = [−0.153, 0.115]⁵ (see Figure 2).

Figure 2.

Mediation model, Study 4.

Study 4 replicated the findings in Study 3, suggesting that participants primed with high (vs. low) personal residential mobility mindsets tend to have lower willingness to engage in private-sphere PEBs. This tendency can be explained by their preference for collective benefits.

Study 5

Study 5 examined whether the preference for collective benefits mediates the relationship between regional residential mobility and private-sphere PEBs. We manipulated participants’ perception of regional residential mobility and asked them to indicate their willingness to engage in private-sphere PEBs and preference for collective benefits.

Method

Participants

A G*power priori analysis (Faul et al., 2007) revealed that with an estimate of d = 0.60, 102 participants would be needed to achieve 85% power (1 − β) at a .05 alpha level. In total, 140 Chinese adults were recruited in exchange for monetary compensation on a third-party platform. Three participants were excluded for answering regularly, and five participants have been excluded for not completing the writing task as request or poor Chinese. The final sample includes 132 participants (48 men and 84 women). Participants’ ages ranged from 19 to 58 years, with a mean of 30.70 years (SD = 7.58).

Measures and procedures

Participants completed all the tasks online. We first manipulated regional residential mobility through a writing task. Participants were required to imagine living in a virtual society with three cities (A, B, and C). They were informed that “the population size (all around 5 million), economic development, residents’ income, and employment situation of these three cities are quite similar. The only difference among them is the population migration.” A was described as a city with low mobility in which only 2% of residents have experienced relocation in the past year, and the cumulative number of population migration in the past 10 years is 0.4 million. B was described to be a city with moderate mobility in which 10% of residents have experienced relocation in the past year, and the cumulative number of population migration in the past 10 years is 2 million. C was described to be a city with high mobility in which 50% of residents have experienced relocation in the past year, and the cumulative number of population migration in the past 10 years reaches 10 million. Two figures were presented to help participants understand the difference clearly (see Supplemental Figure 1a and 1b). Participants were randomly allocated into two conditions: mobile condition and stable condition. Participants in the mobile condition (N = 66) were asked to envision living in City C, while participants in the stable condition (N = 66) were guided to imagine living in City A. Participants then wrote down how population migration will affect local life and interpersonal relationships compared with the other two cities. Participants answered a question (“What do you think of the level of population mobility in your city”) on a 7-point Likert-type scale (1 = very low, 7 = very high) for the manipulation check.

We then measured participants’ willingness to engage in private-sphere PEBs. Participants were informed that there are some advocacies in the virtual city they lived in. Seven advocacies were developed, and each advocacy involves both the benefits and costs of a specific PEB (e.g., “To alleviate the shortage of power supply, the city you live in advocates that residents turn their air conditioner up to 28 ℃, but this may make you feel hot”). Participants need to indicate how likely other residents in this city are to engage in this activity and how likely themselves are to engage in this activity on a 7-point Likert-type scale (1 = not likely, 7 = very likely), respectively. Thus, we obtained two indicators of PEBs: other PEBs (M = 4.71, SD = 1.03; α = .84) and self PEBs (M = 5.45, SD = 0.98; α = .79). We believed that both indicators reflected the willingness to engage in private-sphere PEBs. After that, participants completed the same items of the preference for collective benefits (M = 5.45, SD = 1.05; α = .90) used in Study 4. They finally reported their gender, age (0 = men, 1 = women), and SES (1 = the lower class, 9 = the upper class).

Results and Discussion

An independent sample t test was conducted to ascertain the effectiveness of regional residential mobility manipulation. Participants in the mobile condition (M = 5.73, SD = 1.13) reported a higher level of population migration than participants in the stable condition (M = 2.47, SD = 1.50), t(130) = −14.08, p < .001, 95% CI = [−3.716, −2.800], d = 2.46, indicating a successful manipulation.

The results of independent sample t tests suggested that participants in the mobile (vs. stable) condition showed a lower willingness to engage in private-sphere PEBs. Specifically, participants in the mobile condition (M = 4.36, SD = 1.03) maintained that other residents would exhibit fewer PEBs than that of participants in the stable condition (M = 5.06, SD = 0.91), t(130) = 4.12, p < .001, 95% CI = [0.362, 1.031], d = 0.72. Participants in the mobile condition (M = 5.24, SD = 1.11) were less likely to engage in the PEBs than participants in the stable condition (M = 5.66, SD = 0.79), t(130) = 2.49, p = .014, 95% CI = [0.085, 0.750], d = 0.43. Consistent with our expectation, regional residential mobility decreases private-sphere PEBs. In addition, participants in the mobile condition (M = 5.23, SD = 1.16) reported a lower preference for collective benefits than those in the stable condition (M = 5.67, SD = 0.89), t(130) = 2.43, p = .017, 95% CI = [0.080, 0.791], d = 0.42.

Next, we performed mediation analyses to test the indirect effect via the preference for collective benefits with the PROCESS for SPSS (Hayes, 2013) using 5,000 bootstrap samples. Age, gender, and SES were added as covariates in these analyses. For other PEBs, the result showed that the indirect effect via the preference for collective benefits was significant (SE = .06, 95% CI = [−0.231, −0.006]) and the direct effect remained significant after considering the preference for collective benefits, t = −3.37, p = .001, 95% CI = [−0.331, −0.086] (see Figure 3). For self PEBs, the result showed that the indirect effect via the preference for collective benefits was significant (SE = .07, 95% CI = [−0.262, −0.004]) and that the direct effect was significant after considering the preference for collective benefits, t = −1.31, p = .194, 95% CI = [−0.209, 0.043] (see Figure 4).

Figure 3.

Mediation model for other PEBs, Study 5.

Figure 4.

Mediation model for self PEBs, Study 5.

Study 5 demonstrated that regional residential mobility could curb private-sphere PEBs, and the preference for collective benefits could mediate this effect. Together with Study 4, this study uncovered the mechanism underlying the relationship between residential mobility and PEBs.

General Discussion

In the context of worldwide population migration and environmental degradation, the overall aim of the current research was to investigate the role of residential mobility on PEBs. The results across all five studies supported our hypothesis that high residential mobility could decrease private-sphere PEBs. Specifically, we found that both personal residential mobility and regional residential mobility were negatively related to private-sphere PEBs but were not correlated with public-sphere PEBs in the first two studies. Study 3 clarified the casual relationship between personal residential mobility and private-sphere PEBs. Studies 4 and 5 further revealed the mediating mechanism underlying the relationships. Manipulated personal residential mobility and regional residential mobility decreased the preference for collective benefits and thus led to fewer private-sphere PEBs. Our findings enriched the literature regarding the causes of nonecological behaviors and extended these causes to changes in our social habitats.

There is no doubt that population migration can energize national economic strength. Mobile areas tend to be rich lands. Residents living in such places have many opportunities to advance their material standards of living. Medical and educational conditions have improved greatly in many countries in the past decades during the worldwide wave of migration. However, migrations are not without costs. Some costs are probably directly connected to mobility per se such as negative emotions (Jelleyman & Spencer, 2008) and high mortality (Juon et al., 2003). Some other consequences are associated with adaptation to new social habitats with high mobility. Social habitats with high residential mobility are characterized by high unpredictability, low interdependence, and high anonymity (Trivers, 1971; Zuo et al., 2018). As was suggested by extant studies, these characteristics can shape people’s minds and behaviors, thereby bringing about changes in strategies of self-identity (e.g., Oishi et al., 2007, 2009) and interpersonal relationship (e.g., Lun et al., 2013; Oishi & Kesebir, 2012). Our findings support the proposition that migrations may lead to ecological costs, which manifest in the changes in the strategies in interacting with nature.

The relationship between residential mobility and private-sphere PEBs can be explained by the preference for collective pursuit. Individuals usually consider PEBs in a gain goal frame (Lindenberg & Steg, 2007). Particularly, decisions regarding PEBs involve a trade-off between personal benefits and collective benefits. Despite the fact that PEBs often require personal sacrifice, the eventual outcomes (i.e., a good natural environment) of these actions are favorable for all. In that case, PEBs are conducive to public goods. The consideration of collective interests may thus determine the decisions regarding PEBs. In social habitats with high residential mobility, migrations dilute genetic relatedness and decrease reoccurrence. Moreover, migrations also undermine the basis for kin altruism and reciprocal altruism, which further demotivates the concern for collective benefits (Lieberman & Lobel, 2012; Zuo et al., 2018). In addition, such habitats often lead to poor management of transgressions. Migrations hinder the development of commitment. What’s worse, high anonymity in the habitats increases the difficulty in monitoring and punishing transgressions that harm collective benefits (Kollock et al., 1998). These characteristics in mobile habitats facilitate a selfish tendency rather than a preference for collective benefits. Therefore, PEBs, especially those that require significant personal sacrifice, would be discouraged by high residential mobility.

This research inspires us to understand PEB from the perspective of environmental adaptation. How human beings interact with nature (ecocentrism, anthropocentrism, or environmental apathy) is not only determined by personal attributes—such as traits (e.g., Huang et al., 2019), values (e.g., Nilsson et al., 2004), and education experience (e.g., Klineberg et al., 1998)—but also by the social habitats where humans live. Our social habitats can be profiled from different societal factors. Many of these factors are associated with PEBs, such as social class (e.g., Laidley, 2013), cultural variation (e.g., Zheng & Yoshino, 2003), and urban versus rural residence (e.g., Yu, 2014). But more importantly, researchers need to clarify why these societal factors can affect PEBs beyond values and educations, which is beneficial for us to improve PEBs in a targeted manner. From a perspective of environmental adaptation, PEB is not a static outcome of education and knowledge of environmental protection. Instead, it is an attempt by individuals to adapt themselves to specific habitats. We will be surprised to find that nonecological behavior is not isolated but is interrelated with behaviors in other domains. And all these behaviors are formed in the process of adaptation to the environment. This perspective could help make policy and create propaganda tactics to protect the environment. When the focus of PEBs is consistent with individuals’ success in terms of survival and reproduction, they will be more willing to engage in PEBs, thereby mitigating against environmental degradation. Large social changes, such as the industrialization of a country and the process of capitalization, have a profound influence on our social habitats. Apart from the increase in residential mobility, some other factors also undergo changes, such as parasite stress, demographic diversity, and economic inequity. It is worth exploring how these factors resulting from large socioeconomic movements will affect PEBs from the perspective of environmental adaptation.

Interestingly, a line of studies field called environmental migration has relevance to the topics in the current research. Environmental migration focuses on how environmental changes influence human migrations instead of the opposite (Henry et al., 2004). Environmental changes, such as natural disasters and the gradual deterioration of environmental conditions, could cause migrations. These environmental migrations are at the center of a spectrum between forced migration and voluntary movement (Bates, 2002; Henry et al., 2004). Our findings in the current study and those obtained in the environmental migration research should complement each other. On the one hand, environmental changes prompt humans to move; in this case, migration is adopted as a human coping strategy to the natural environment (Bates, 2002). On the other hand, moves, in turn, cause environmental changes, which reflect human influences on the natural environment. Together, these phenomena show how we humans interact with the natural environment.

There are several limitations in the current work that give rise to interesting directions for future research. First, individual living environments are defined both by specific personal experiences and by different social contexts. Although both personal residential mobility and regional residential mobility were considered in this research, we did not investigate the impact of their interaction on pro-environmentalism. There is a possibility that the consistency between personal experience and social context could further affect PEBs. Future research can integrate regional and personal residential mobility in the same study to test this idea. Second, this research investigated the relationship between residential mobility and PEBs statically whereas the relationship might change dynamically. We conducted this research in China within a social context where environmental degradation and residents’ environmental concerns arise from nothing. In this period, the effect of residential mobility on PEBs was confirmed. However, this effect might be reduced in a situation where environmental degradation goes beyond a certain tolerance level or in a situation where people’s environmental concerns have greatly increased. In such situations, people are more likely to recognize the harm caused by nonecological behaviors, and they are thus motivated to engage in PEBs to change environmental conditions (Barber et al., 2003). Therefore, future research can test our hypothesis in different contexts in terms of social development stages. Third, we did not investigate the relationship between residential mobility and PEBs in a normative goal frame. We assume people consider PEB in a gain goal frame without intervention. However, when a normative goal is stressed (Lindenberg & Steg, 2007), the relationship might be changed. Detailed rules, including environmental protection (e.g., garbage sorting), tend to be established in big cities where residential mobility is generally high. Then, compared with individuals with low residential mobility, individuals with high residential might act more ecologically in some PEBs. Moving forward, research could investigate their relationships in terms of normative goals.

Conclusion

How the migration of population intertwines with pro-environmentalism is crucial for the efforts to prevent environment degradation. In this study, we proposed that high residential mobility is related to fewer private-sphere PEBs. Across five studies, we tested the hypothesis at both individual and regional levels, with both self-reported and actual private-sphere PEBs. The first two studies showed that both personal residential mobility and regional residential mobility were negatively related to private-sphere PEBs but not to public-sphere PEBs. Study 3 clarified the causality by manipulating residential mobility mindsets. Studies 4 and 5 found that the preference for collective benefits could mediate the relationship between personal residential mobility and PEBs and the relationship between regional residential mobility and PEBs, respectively. The findings extended the adverse impacts of residential mobility to natural environments. In addition, this research added to the literature concerning the factors of pro-environmentalism and suggested searching for causes of environmental degradation based on the changes in our social habitats.

Supplemental Material

sj-docx-1-psp-10.1177_01461672221079451 – Supplemental material for Population Migration Damages the Natural Environment: A Multilevel Investigation of the Relationship Between Residential Mobility and Pro-Environmental Behaviors

Supplemental material, sj-docx-1-psp-10.1177_01461672221079451 for Population Migration Damages the Natural Environment: A Multilevel Investigation of the Relationship Between Residential Mobility and Pro-Environmental Behaviors by Shijiang Zuo, Pan Cai, Niwen Huang, Fang Wang and Pujue Wang in Personality and Social Psychology Bulletin

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported by the National Natural Science Foundation of China (Grant No. 31971012).

ORCID iDs

Shijiang Zuo

Niwen Huang

Supplemental Material

Supplemental material is available online with this article.

Notes

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