Identifying Neighborhood Ef fects on Geohazard Adaptation in Mountainous Rural Areas of China: A Spatial Econometric Model

Li Peng · Jing Tan

Abstract In mountainous rural settlements facing the threat of geohazards, local adaptation is a self-organizing process inf luenced by individual and group behaviors.Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks.The purpose of this study was to investigate whether neighbors inf luence individuals’ adaptation decisions, as well as to unravel the mechanisms through which neighborhood ef fects exert their inf luence.We employed a spatial Durbin model and a series of robustness checks to conf irm the results.The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards.Our empirical results reveal that neighborhood ef fects are an important determinant of adaptation to geohazards.That is, a farmer’s adaptation decision is inf luenced by the adaptation choices of his/her neighbors.Furthermore, when neighbors adopt adaptation strategies, the focal individuals may also want to adapt, both because they learn from their neighbors’choices (social learning) and because they tend to abide by the majority’s choice (social norms).Incorporating neighborhood ef fects into geohazard adaptation studies of fers a new perspective for promoting disaster risk reduction decision making.

Keywords Disaster risk reduction · Geohazard adaptation · Neighborhood ef fects · Rural China · Spatial Durbin model(SDM)

1 Introduction

Climate change has led to more intense and frequent natural hazards and disasters over the past few decades.Extreme weather events, such as heavy rainfall, have triggered f loods, landslides, and mudslides in many parts of the world(Yamamura 2015; Cappelli et al.2021).Notwithstanding significant advancements in early warning systems and post-disaster recovery procedures, both the economic and non-economic ramif ications of climatic disasters are on the ascent (Lo 2013; Coronese et al.2019).Rural populations,particularly those residing in less developed nations, are notably vulnerable to recurrent climatic hazards owing to their high exposure and limited adaptive capacity (Peng et al.2020; Tan et al.2020).Literature has underscored that strategies followed by government agencies and characterized by technocratic approaches prove insuf ficient in addressing the unpredictable and multifaceted nature of such disasters(Wilby and Keenan 2012; Tran and Rodela 2019).Consequently, there is an imperative to advocate for collaborative and participatory approaches to foster ef fective adaptation measures.

Numerous studies have examined the prerequisites for disaster preparedness, mitigation, and adaptation.One prevalent perspective regards individuals as independent decision makers, with their adaptation strategies inf luenced by psychological perception (for example, risk perception and self-ef ficacy), experience, and socioeconomic characteristics(Hof fmann and Muttarak 2017; Peng et al.2020).Another line of inquiry emphasizes social inf luence as a critical driver of adaptation.Researchers realize that social networks play an important role in disaster risk reduction because they enable information and resource sharing, mental health support, mutual learning, and collaborative ef forts (Adger 2003;Lo 2013; Tan et al.2020).Several studies have found that when deciding whether to adapt to disaster risks, individuals tend to imitate their neighbors.For instance, Kunreuther and Michel-Kerjan ( 2009) found that homeowners are motivated to purchase f lood insurance if their neighbors are also insured, despite the fact that their risk perception remains unchanged.However, the majority of existing research uses qualitative research or case studies to illustrate the phenomenon, and few empirical studies have investigated how and why peers’ adaptation inf luences an individual’s adaptation decisions.

Neighborhood or peer ef fects, as def ined by Durlauf and Ioannides ( 2010), refer to the phenomenon where an individual’s behavior is directly inf luenced by the behavior of a reference group.In rural communities of China, characterized by low population mobility, neighborhood ef fects tend to be more pronounced.This heightened inf luence can be attributed to the strong bonds among residents, often spanning generations within the same village (Loh and Li 2013;Tan et al.2020).Geological hazards predominantly manifest in mountainous rural regions with relatively f ixed geographical locations.A substantial portion of the population is consistently exposed to potential low to moderate-intensity hazards, leading to the gradual development of adaptation strategies in response to persistent threats.Therefore, there is a pressing need to investigate the impact of neighborhood dynamics on long-term adaptation behaviors within China’s mountainous rural communities.

The current study aimed to: (1) Examine whether neighborhood ef fects af fect individuals’ adaptation strategy of geohazards (for example, emergency supply storage and participation in training) in rural China; (2) Explore the underlying mechanisms that drive neighborhood ef fects in the context of disaster risk adaptation; (3) Propose implications for fostering adaptation through social-based interventions.

This study contributes to existing research in three ways.First, while most previous studies have emphasized the role of social networks and social capital in inf luencing adaptation behaviors, our research of fers insights on neighborhood ef fects by conducting a comprehensive examination of whether and why individuals’ adaptive measures are inf luenced by the adaptation decisions of their peers.Conf irming the existence of neighborhood ef fects helps provide important implications for enhancing the ef ficiency of disaster risk reduction policies.Second, there is a relative scarcity of empirical studies examining the mechanisms of farmers’adaptation decisions, particularly within the rural Chinese context.This study contributes to the existing literature by focusing on a region characterized by frequent geological hazards and a collective culture in China.By validating two mechanisms of neighborhood ef fects on farmers’ adaptation strategies, our research enhances the understanding of this aspect within the f ield.Third, while the spatial econometric model is extensively used for macrodata analysis, microdata research has yielded little empirical evidence.Using data with unique spatial information, this study proposes the application of a spatial econometric model to the f ield of disaster risk reduction.

This article is structured as follows.Section 2 introduces our research hypothesis.Section 3 encompasses estimation methods, data sources, and variables.Section 4 presents the research f indings.Section 5 discusses the results, limitations,and policy recommendations.

2 Research Hypothesis

According to Manski ( 1993, 2000), three ef fects are constructed to explain why an individual’s choice is typically similar to those of their peers: neighborhood ef fects (or endogenous ef fects/peer ef efcts), contextual ef efcts (or exogenous ef fects), and correlated ef fects.Neighborhood ef fects occur when an individual’s behavior is directly inf luenced by the behavior of a neighboring group.Contextual ef fects refer to the inf luence of neighbors’ characteristics on one’s behavior.Correlated ef fects describe how a group’s shared natural and social environments may lead to consistent decision making within the group (see Fig.1).Only neighborhood ef fects can generate a social multiplier ef fect,1See Manski ( 1993) for the detailed theoretical derivations.which describes a phenomenon that a small exogenous shock experienced by a targeted group has the potential for a greater inf luence and a larger aggregate level than intended (Glaeser et al.2003).For instance, governmental agencies provide disaster prevention and mitigation training to specif ic rural households.This training directly enhances the adaptive capabilities of the participating households.As these trained households become better prepared, they naturally engage with and exert inf luence on neighboring households that have not undergone the training.This interplay subsequently results in an indirect improvement in the disaster preparedness of the entire community.Consequently, the presence of this social multiplier ef fect signif icantly magnif ies the impact of the policy, thereby enhancing the overall resilience of the entire community in the face of disasters.

Neighborhood effects have been found in a range of behaviors, such as pro-environmental behavior and new technology adoption (Adjognon and Liverpool-Tasie 2015;Zheng et al.2021).These studies have shown that individuals are susceptible to the inf luence of others in their intentions and behaviors.Previous research on the role of neighborhood ef fects in disaster risk reduction has been limited.A relevant study by Tan et al.( 2021) employed the linearin-means method to identify peer ef fects in disaster-induced relocation intention.Kunreuther and Michel-Kerjan ( 2009)have also observed that residents tend to imitate their neighbors’ disaster risk reduction strategies, such as insurance purchases.However, in the context of frequent and recurrent geohazards in rural mountainous regions of China,there remains a lack of evidence regarding the existence of neighborhood ef fects among households rooted in a collectivist culture when it comes to their disaster risk adaptation actions.Based on these studies, the following hypothesis is proposed:

H1 Neighborhood ef fects exist in farmers’ adaptation strategy of geohazards.

In addition, researchers have suggested some mechanisms underlying neighborhood ef fects, such as complementarities,comparisons, convergence, and social learning, which may have varying policy implications (Bursztyn et al.2014; Tan et al.2021).According to these studies and our f ield investigation, there are two possible mechanisms by which the neighbors’ adaptation actions inf luence an individual’s decision.First, social interaction shapes group opinions about how things should be done (social norms).Second, social interaction facilitates learning processes and knowledge exchange (social learning).They are specif ically elaborated as follows.

Social norms entail widely accepted behavioral standards within a community, encompassing notions of “appropriate conduct” and “typical behavioral responses” in given situations.These norms function as soft constraints, diverging from more rigid legal regulations.Individuals within a social group conform to these norms in pursuit of social validation while avoiding potential social sanctions (Abrahamse and Steg 2013; Bergquist et al.2019).Consequently, groups adhering to shared social norms tend to display congruent beliefs and behaviors.Although social norms serve as potent drivers of specif ic behaviors, individuals often underestimate their inf luence due to a lack of awareness (Bergquist et al.2019).China’s collectivist culture, rooted in a strong emphasis on social connections and peer evaluation, amplif ies the impact of social norms.Members who embody qualities of sociability, cooperation, and conformity tend to garner more favorable regard than those who deviate from these norms.Rural populations, in particular, more closely follow the principle of “the rule of the mean” and are highly susceptible to inf luence from reference groups (Lo 2013).Consequently, when a norm, such as “adapting to disaster,”is established within a group, individuals are motivated to emulate the predominant choice.

Social learning, on the other hand, involves individuals observing choices made by reference groups and interpreting them as possessing greater “private signals/values,” reinforcing their own inclination toward those choices (Conley and Udry 2010).For example, Bursztyn et al.( 2014) found that an individual may infer that the consumption decisions made by others are of higher quality, thereby revising their own beliefs regarding the quality of a given product upon learning about others’ revealed preferences.In situations marked by intricate decision making, individuals lacking comprehensive information are inclined to learn from others since it substantially reduces the cost of acquiring information for a specif ic action (Tan et al.2021).According to Zhang and Maroulis ( 2021), geographical congruence is a prerequisite for social learning, as similar disaster risks enhance the comprehension and anticipation of potential threats.Rural residents living in China’s vast mountainous areas often have insuf ficient private information concerning mountain disasters and adaptation strategies.However, they have the freedom to communicate with or observe their neighbors(Tan et al.2021).Individuals can adapt their expectations and preferences based on information revealed by neighbors, which may be acquired through direct peer communication or observation of their decisions (Moretti 2011).In our study, a farmer may actively update his/her beliefs (that is, learn from others) and make choices aligned with those of the neighbors.Therefore, the following hypotheses are proposed:

H1a Social norm is a mechanism underlying the neighborhood ef fects in adaptation.

H1b Social learning is a mechanism underlying the neighborhood ef fects in adaptation.

3 Methodology

In this section, we present the methods, including the spatial econometric model, data collection, and variable selection.

3.1 Spatial Econometric Model

As it is challenging to distinguish between neighborhood ef fects, contextual ef fects, and correlated ef fects, the spatial Durbin model (SDM) is considered an appropriate approach because it includes both the spatial lags of the outcome and the neighbors’ and farmers’ private characteristics (Ajilore 2015; Läpple and Kelley 2015; Yang and Sharp 2017; Zheng et al.2021).The equation for the spatial Durbin probit model is as follows:

where the dependent variableyis a binary variable.If farmeriadopts the adaptation behavior, thenyi=1 ; if farmeridoes not take any action against disasters, thenyi=0.Wyis the spatial lag of the dependent variable, ref lecting the spatial dependence of the adaptation choices among farmers.The spatial autoregressive parameter λ measures the strength of spatial dependence and captures the coef ficient of neighborhood ef fects.Similarly,WXis the spatial lag of the independent variable, which captures the weighted average characteristics of neighboring farmers.Thek×1vectorθcaptures the contextual ef fects.Xis a matrix of independent variables.Wis ann×nspatial weight matrix, which ref lects the spatial relationships among individuals (the diagonal elementwii=0 ).＆#x1d704;nis a constant term vector.

According to previous research (Li et al.2013; Ling et al.2018), our study assumed that individuals residing within a shared village are “neighbors.” Typically, a village encompasses descendants from diverse families, implying shared kinship or deep-rooted geographic connections among its inhabitants.Each village maintains its grassroots governance structure, conducts regular village meetings to disseminate of ficial policies, administers public af fairs, and allocates resources.Consequently, individuals dwelling within a village are highly likely to be familiar with one another and susceptible to the inf luence of their fellow villagers (Tan et al.2021).Additionally, these studies posit that designating residents cohabiting within the same village as neighbors can ef fectively mitigate issues associated with self-selection bias.China’s household registration system(hukou) imposes limitations on the migration of residents into rural areas; and the establishment of rural settlements frequently spans multiple generations, with even newly constructed residential addresses primarily planned within designated homestead areas.In essence, the spatial arrangement of neighborhoods in mountainous rural regions is predominantly externally determined, and residents possess limited options in selecting their neighbors.In the benchmark model, individual interactions are represented by an inverse distance weights matrix, wherewij=d(the individuali,jin the same village,i≠j) andwij=0 (the individuali,jin dif ferent villages), indicating that individuals are more inf luenced by their “nearer neighbors.” Geographic distancedijis the distance calculated based on each household’s longitude and latitude.According to LeSage and Pace ( 2009), we estimated the spatial probit model using the Bayesian Markov chain Monte Carlo (MCMC) method.

Because the reported coef ficients in spatial probit models are not equal to the partial derivatives, LeSage and Pace( 2009) proposed a method to interpret the marginal ef fects of an explanatory variable.These marginal ef fects can be decomposed into direct and indirect ef fects.2Detailed estimation can be requested from the authors.The direct ef fect assesses the inf luence of a change in an explanatory variable onyi.Indirect ef fect, also known as spatial spillover,describes the cumulative ef fect of a change in neighboring explanatory variables on the outcome (yi) of farmeri(LeSage and Pace 2009; LeSage et al.2011; Läpple and Kelley 2015).Total ef fects are equal to the sum of direct and indirect ef fects.

3.2 Data Collection

China is a mountainous country, with approximately 70%of its territory covered by mountains, hills, and plateaus.More than one third of its total population lives in mountainous areas, and 74 million of them are directly threatened by geohazards (Cui 2014).In recent decades, extreme weather events (extreme rainfall, strong gusts, and f looding),as external triggers of geohazards, are exacerbated by global climate change.

Fig.2 Location of the surveyed village

The data used were collected from a questionnaire survey conducted in Chongqing Municipality between June and July 2018.Chongqing is a disaster-prone region in China.According to data released by the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences,3https:// www.resdc.cn/ data.aspx? DATAID= 29094% of its terrain is mountainous or hilly, and 15,647 hidden geohazards have been identif ied.In recent years, the increase and concentration of precipitation in Chongqing have exacerbated the frequency of geological disasters.The questionnaire included basic information about rural residents, psychological factors, social networks,willingness and behavior related to disaster risk reduction,among others.The questionnaire items were chosen after several rounds of trial research and expert consultation.We also placed test questions to determine the quality of the questionnaires.In particular, we recorded each respondent’s location (latitude and longitude).The stratif ied random sampling method was used to select the samples: based on the ranking of GDP, disaster distribution, and the feasibility of the survey, four counties were selected; based on population size and economic status, three to f ive townships were selected within each county.Next, one to three sample villages were selected from the rural areas of each township based on the distribution of hidden hazards (Fig.2).In each village, 10–20 households were randomly selected for the survey.Ten well-trained interviewers conducted semistructured face-to-face interviews, with each interview taking an average of 50 minutes.In the end, 516 valid questionnaires were collected, for a response rate of 100%.The questionnaires show acceptable reliability (Cronbach’s alpha greater than 0.7).

Fig.3 Sociodemographic characteristics of the respondents (N = 516)

Figure 3 presents the characteristics of the survey participants.Of the respondents, 55.2% are male.The respondents are relatively old, with only 5.8% of them under 40 years of age, and nearly half aged between 56 and 70.The level of education is low: 72.2% of the respondents have only primary education (no more than six schooling years), and only 2.7% have a bachelor’s degree or above, with an average of 5.22 years of schooling.Nearly 40% of the respondents have an average annual household income of less than 30,000 yuan, while 17.1% earn more than 80,000 yuan per year(including earnings from family members who labor in urban areas).The majority of the respondents (87.4%) stated that they have no additional property besides their local residence, and only 12.6% of the respondents own additional properties.Approximately 70% of the surveyed households have two or fewer laborers (number of family members aged 16–64, excluding students and those with poor health conditions).The sampling results are generally representative of rural China.According to the National Bureau of Statistics, the proportion of permanent residents aged 60 or older in China’s rural areas reached 23.81% in 2020, while the proportion of those aged 65 or higher reached 17.72%4The data were obtained from the Seventh National Population Census, available at https:// www.stats.gov.cn/ xxgk/ jd/ sjjd2 020/ 202105/t2021 0511_ 18172 80.html.; 91.8% of farmers have not attended a high school (< 10 years of schooling) as of 2016.5The data were obtained from the Third National Agricultural Census, detailed in the Major Data Bulletin of the Third National Agricultural Census (No.5), available at https:// www.stats.gov.cn/ sj/ tjgb/nypcgb/ qgnyp cgb/ 202302/ t2023 0206_ 19021 05.html.Rapid urbanization drives a large number of youthful and educated rural laborers to the city in search of employment.Additionally, due to restrictions in the household registration system and high housing prices,the old rural residents who used to work in cities return to their rural hometowns, thereby exacerbating the aging of the rural population.

3.3 Variables

The dependent variable in this study is binary, assuming a value of 1 for the sample that prepared disaster emergency items or participated in disaster mitigation training and drills, and 0 for the sample that did not adopt any adaptation strategies.Prepared for emergency encompasses the proactive actions undertaken by households to assemble and maintain essential supplies and resources, including provisions such as food, water, medical supplies, emergency shelter materials, and other necessary provisions.These measures are intended for use during or immediately after a disaster, aiming to ensure readiness and enhance resilience.Participation in disaster mitigation training and drills refers to the active involvement of individuals in structured training programs that cover various aspects of disaster risk adaptation, such as hazard identif ication, evacuation procedures,emergency response protocols, f irst aid techniques, and other relevant skills and knowledge.Additionally, it is crucial to acknowledge that local government authorities play a signif icant role in disaster prevention and preparedness within these communities.Many of the hazard adaptation actions undertaken by residents rely on government initiatives or assistance.For instance, participation in disaster mitigation training and drills requires government organization and facilitation, while residents have the right to decide whether to participate.This highlights the collaborative ef fort between authorities and the community in preparing for and responding to potential hazards.

For the control variables, referring to existing studies, we control a series of psychological variables that may inf luence adaptation behaviors, including risk perception, self-ef ficacy,and response ef ficacy (Lindell et al.2016; Peng et al.2019;van Valkengoed and Steg 2019).Based on Slovic ( 1987)and Yang et al.( 2020), risk perception is measured using three statements: “You believe that a disaster will occur in the future,” “If a disaster occurs, your family’s house, land,and life may be af fected,” and “When you think of natural hazards and disasters like mudslides and landslides, you feel afraid and scared.” The scale assumes that 1 corresponds to“completely disagree” and that 5 corresponds to “completely agree.” The risk perception level is determined by averaging the responses to the three queries, with a higher value indicating a greater risk perception.We asked “Do you believe that disasters can be managed through certain behaviors?”(0 = do not know, 1 = cannot be, 2 = partially, 3 = can be)to evaluate respondents’ conf idence in adaptation strategies(that is, response ef ficacy).This study used the statement“Although the occurrence of disasters cannot be prevented,you can take some preventive measures to reduce the loss” to measure self-ef ficacy, with 1 representing low self-ef ficacy and 5 representing high self-ef ficacy (Mertens et al.2018).

In addition, control variables related to social interactions are included.“Public access” refers to disaster prevention and mitigation services or facilities provided by the public sector (for example, warning signs, drills, and prevention cards) with yes/no responses.Collective resources ref lect the collective capital of a village.Referring to Tan et al.( 2020), we chose four items: “The village leaders are capable,” “The community has the resources/capacity to solve the problems,” “The community has joined forces with organizations/institutions outside the village to help solve the problems,” and “The community has implemented some disaster preparedness policies/programs to respond to future disasters.” Neighborhood trust is the degree to which a respondent has faith in their neighbors.The interpersonal connection reveals the emotional bond between farmers and surrounding residents.If farmers have positive relationships with their neighbors, they may communicate and share more information daily (Tan et al.2021).Place dependence focuses on the connection between a farmer and his/her area of residence, which is measured by the statements: “You are proud to live in this village,” “You feel that you cannot leave this village and its people,” and “You like this village more than any other place” (Williams and Vaske 2003;Walker and Ryan 2008; Song et al.2019; Peng et al.2020).All variables are measured on a 5-point Likert scale, with a higher score indicating a higher level of neighborhood trust,collective resources, interpersonal relationships, and place dependence.

Furthermore, this study controled for some demographic characteristics.The selected variables are described in Table 1.

4 Results

In this section, we present the main empirical f indings,including the results of the benchmark model, marginal ef fects, and robustness checks.Two potential mechanisms,as explained in Sect.2, are also examined in this section.

4.1 Benchmark Model

This study used LeSage and Page’s ( 2009) spatial regression to estimate the coef ficients of neighborhood ef fects.The estimates are presented under model 1 of Table 2.The McFadden pseudo-R 2 demonstrates the strong goodness-off it of our spatial Durbin probit model.The spatial lag coeff icient (λ= 0.389,t= 5.218) is signif icantly positive at the 1% level, indicating the existence of neighborhood ef fects;that is, an individual’s adaptation decision is inf luenced by the adaptation decisions made by others in the village.Besides, the individual’s characteristics, such as age, skills mastery, risk perception, self-ef ficacy, response ef ficacy,interpersonal relationships, collective resources, neighborhood trust, and place dependence have signif icant ef fects on the adaptation to hazards.Finally, the results in the second column of Table 2 indicate that the spatial lag terms of the response ef ficacy and interpersonal relationships are statistically signif icant, indicating that an individual’s adaptation decision is inf luenced by the characteristics of their neighbors.Therefore, contextual ef fects exist.

Table 1 Descriptions of the selected variables

4.2 Marginal Ef fects

The estimates＆#x1d6fd;,＆#x1d703;from the spatial Durbin probit model are not straightforwardly interpretable.To reveal the marginal ef fects of each explanatory factor on geohazard adaptation,the marginal ef fects were decomposed into direct and indirect ef fects (model 2 in Table 2).The estimates of direct ef fects capture the ef fects of farmers’ characteristics on their adaptation decisions.The results of the direct (marginal) ef fects are shown in column 4 of Table 2.Individuals who have a younger household head, family members with non-agricultural skills, high risk perception, strong selfef ficacy, strong response ef ficacy, good interpersonal relationships, high collective resources, high trust in neighbors,and high place dependence are more likely to adopt adaptation behaviors.The skills mastery variable is signif icantly positive at the 1% level, suggesting that the presence of non-farming family members increases the likelihood of adaptation by 13.2%.This f inding is consistent with that of Tan et al.( 2021), who found that households were more likely to migrate if they had skilled members working in non-agricultural occupations.Regarding the psychological factors, risk perception, self-ef ficacy, and response ef ficacy all have positive and statistically signif icant direct ef fects.Each unit increase in risk perception, self-ef ficacy, and response ef ficacy raises the adaptation probability by 9.5%,8.6%, and 7.4%, respectively.These results have been widely conf irmed by previous studies (Trainor et al.2015; Lindell et al.2016).Interpersonal relationships, collective resources,place dependence, and neighborhood trust all exhibit a signif icant positive ef fect.There is a sizeable (12.2%) direct impact of interpersonal connections on adaptation behavior.

The estimates of the indirect ef fects capture the impact of one’s neighboring farmers’ characteristics on an individual’s adaptation decision (column 5 of Table 3).Neighborhoods with skilled family members and high risk perception, selfef ficacy, response ef ficacy, interpersonal relationships, and trust in the community contribute to an individual’s adaptation to hazards.In particular, the presence of a familymember with non-agricultural skills in a neighboring household would encourage a farmer to adopt adaptation measures.One possible explanation is that the competent neighbor in rural communities obtains information from a greater variety of channels, which could increase one’s adaptation behaviors through communication.Farmers’ adaptation is also signif icantly inf luenced by other farmers’ interpersonal relationships and neighborhood trust, implying that a harmonious social atmosphere will increase the adaptability of rural settlements to disasters.This f inding is in line with previous studies that highlighted the importance of social capital on community disaster resilience (Peng et al.2020;Tan et al.2020).

Table 2 Results of the spatial Durbin probit model

The total ef fects are the sum of direct and indirect ef fects and represent the overall impact of a change in a particular explanatory variable on the probability of adaptation.Households with other non-farming skills are 20.7% more likely to adopt hazard adaptation strategies (13.2% from direct ef fects and 7.5% from indirect ef fects) than households that rely solely on agriculture.The total ef fect of interpersonal relationships on the dependent variable is 19.1%,of which 12.2% arises from direct ef fects and 6.9% from indirect ef fects.Other social interaction and psychological perception variables also exhibit signif icant total ef fects (see model 2, column 6).

4.3 Robustness Checks

Four additional robustness tests were conducted to determine the reliability of the empirical results of the spatial econometric model.

Table 3 Results of the mechanism analysis

First, we changed the spatial Durbin probit model to the spatial auto-regressive (SAR) probit model.The f indings indicate that the spatial lagλis still signif icantly positive,which validates the presence of neighborhood ef fects in farmers’ adaptation behavior.Then, this study adopted different spatial weight matrices:

6 Full results are available upon request from the authors.

4.4 Mechanism Analyses

4.4.1 Social Norms

The more a farmer wants to conform to other village residents, the more he/she is inf luenced by social norms.This study introduced a variable “interpersonal importance”into the benchmark model, which measures how much a farmer agrees with the statement “It is important for you to keep in pace with other residents in the village”(1–5: strongly disagree–strongly agree).Table 3 shows that the spatial lag coef ficient of the spatial Durbin model remains signif icant at the 1% level.The spatial decomposition results indicate that the “interpersonal importance”variable has a signif icant positive impact on adaptation behavior.A one-unit increase in interpersonal importance raises the probability of engaging in adaptation behavior by 14.5%.The more farmers value conformity with others,the more they are constrained by social norms.

4.4.2 Social Learning

As argued by Tan et al.( 2021) and Bursztyn et al.( 2014),people with higher social status have a higher informational advantage when making decisions, and individuals with less information are more likely to learn from those with more information.In mountainous rural areas where information is insuf ficient, it is expected that low social status families will learn from high social status families,while high social status families will be less inf luenced by low social status groups.To investigate the social learning mechanism, the samples were separated into two groups based on their self-evaluated social status: high-status group (N = 252) and low-status group (N = 263).For farmers in the high social status group, our study focused on analyzing the extent to which they are inf luenced by their low social status neighbors in making adaptation decisions.To facilitate this investigation, we made specif ic adjustments to the weight matrix of the spatial Durbin model.These adjustments involve settingwij=d−1ijwhen both farmeriand farmerjreside in the same village, as well as farmerjbelonging to the low social status group.Besides, we set the spatial weight values between high social status group farmers within the same village to zero,that is,wij=0 , thereby excluding considerations of the impact of high social status neighbors on farmeri(high social status).Likewise, for farmeriin the low social status group, our focus shifted to assessing the impact of their high social status group neighbors.Specif ically, we setwij=d−1ijwhen both farmeriand farmerjare situated in the same village, and farmerjbelongs to the high social status group.For all other cases,wijwere set to 0.

The neighborhood effect coefficients in panel B of Table 3 show that the spatial lag coef ficient for the high social status group is 0.129, which signif icantly dif fers from that in the low social status group of 0.302.This f inding suggests that residents with higher social status are less inf luenced by the adaptation strategies of their lower-status neighbors compared to the extent to which lower social status residents are inf luenced by their higher social status neighbors.In essence, the decision making of farmers with informational advantages tends to be relatively independent, while individuals at an informational disadvantage are more inclined to learn adaptation choices from neighbors with greater access to information.

5 Discussion, Limitations, and Policy Implications

The results of the spatial econometric analysis conf irm the existence of neighborhood ef fects in geohazard adaptation.This f inding is consistent with that of Esham and Garforth ( 2013), who also discovered that farmers learn about climate change adaptation measures primarily from observing neighboring farmers.Moreover, social learning and social norms have been identif ied as two mechanisms that underlie the neighborhood ef fects in farmers’ geohazard adaptation.

Social norms, in contrast to policies and laws, are a“soft” normative constraint.Within the context of Chinese rural culture, individuals tend to conform their thoughts and actions to those of other group members (Lo 2013).When the majority of a community adopts adaptation strategies, participation becomes the norm in the small society.Thus, non-participating families become a minority within the group and are likely to be labeled as misf its, isolated, and socially def icient (Lo 2013; Tan et al.2021).A potential negative evaluation puts them at risk of damaging their social image or even losing aid.Therefore,farmers’ behavioral decisions that imitate those around them may be inf luenced by social norms.This conclusion is also consistent with Blume et al.’s ( 2015) theoretical derivation, which states that, in order to achieve the Bayes-Nash equilibrium, social pressure compels individuals to minimize the disparity between their behavior and the group’s average level.

Due to the uncertainty of disaster risks, farmers face high learning costs when making decisions regarding geohazard adaptation.Therefore, farmers who lack information are likely to optimize their decisions by obtaining information from other neighbors.Individuals are constrained by their bounded rationality, but communication and interaction with others can enhance disaster awareness and adaptation behavior.When farmers observe their neighbors (especially households with higher social status or information) adopting adaptation behaviors, they assume that these authoritative village residents have more private information regarding the ef ficacy of adaptation,and thus adopt the same measures as other farmers.Much like what several interviewees mentioned, “I noticed that our village’s teacher had also stocked up on emergency supplies.He is well-educated and knowledgeable, so I f igured these items must be ef fective to some extent.That is why I decided to buy them as well.” This learning mechanism has also been demonstrated in disaster-induced migration studies (Tan et al.2021).

Admittedly, this study has certain limitations.First, our study primarily focused on the neighborhood ef fects of whether to adopt two fundamental geohazard adaptation strategies, with limited discussion on the impact of neighborhood ef fects on the degree of participation and many other types of adaptation strategies.Future research should strive to include a broader range of adaptation strategies and consider the intensity of adaptation for a more comprehensive analysis.Second, addressing endogeneity challenges in spatial probit models remains a complex task,and this study has not yet tackled potential endogeneity issues.Although our research has undergone a series of robustness tests, further investigation could greatly benef it from addressing the endogeneity problem to enhance the study’s credibility.Additionally, while our survey areas encompass the most extensive and representative geological disaster-prone areas in China, future research would greatly benef it from collecting data from other regions.Diversifying the geographic scope of the study could provide valuable insights into regional variations in geohazard adaptation strategies.Lastly, our study did not dif ferentiate between villages based on factors such as population size and proximity to urban areas.These distinctions could yield valuable insights into variations in the inf luence of social learning versus social norms on adaptation in different village settings.Future research endeavors should explore these nuances to provide a more holistic understanding of the subject matter.

The f indings of this study suggest that incorporating a perspective of neighborhood ef fects can enhance the ef fectiveness of interventions aimed at promoting geohazard adaptation, given that policies af fect not only target groups but also non-target groups.First, it is dif ficult for a policy or initiative to simultaneously cover all groups, as the implementation of a single policy in rural China necessitates door-to-door advocacy and grassroots of ficials’ mobilization.Therefore,the government could initially encourage participation from groups that are more receptive to adaptation, such as rural cadres, village elites, and farmers with higher levels of education.Through the process of learning among rural residents,more groups that have not yet comprehended relevant policies can increase their participation awareness.Second, the publicity used to encourage geohazard adaptation can convey what others are doing and/or what others disapprove of.Moreover, residents who adapt to disasters deserve recognition (for example, through ceremonies in rural communities).Notably,the governments should ensure that the information related to disasters and adaptation is both obvious and accurate; otherwise, the neighborhood ef fects could result in irrational group decision making.

6 Conclusion

This study investigated neighborhood ef fects on adaptation in geohazard-prone rural areas of China.Leveraging the survey data from 516 participants with unique spatial information, we employed the spatial Durbin model to capture neighborhood and contextual ef fects.Four robustness tests were conducted by modifying the benchmark model and spatial weight matrices.The results underscore the pivotal role of individuals’ neighbors in shaping their adaptation decisions in mountainous rural areas of China.The adaptation choices and characteristics of neighbors wield a signif icant inf luence over rural residents’ adaptation behaviors.Social learning and social norms are the mechanisms underlying neighborhood ef fects: On the one hand, traditional Chinese values of collectivism and conformity increase group pressures on farmers.Social interactions through communication or observation compel farmers to shape their beliefs about what is socially acceptable (social norms).On the other hand, when confronted with uncertain and complex disaster conditions, social interactions between neighbors provide farmers with more private information, encouraging them to adopt others’ adaptation behaviors (social learning).

AcknowledgmentsThis work was supported by the National Natural Science Foundation of China (Grant No.42071222), the Sichuan Science and Technology Program (No.2022JDJQ0015), the Fundamental Research Funds for the Central Universities (No.2023CDSKXYGG006), and the Tianfu Qingcheng Program (No.ZX20220027).The authors are grateful for the receipt of these funds.

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