This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study's empirical focus is on the relocation of the Beijing municipal government, utilizing Granger causality analysis on time series data. Data on online negative emotions is sourced from Sina Weibo, while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes. The results indicated that: 1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. 2) Negative comments are more effective predictors than negative feelings; 3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; 4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; 5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. This study is expected to offer valuable insights and recommendations for institutions involved in policy formulation, implementation, and evaluation.
Research Article
Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing
https://doi.org/10.21203/rs.3.rs-4295691/v1
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negative emotions online
information search behavior
violent behavior
granger causality analysis
relocation of beijing municipal government
In the era of big data, the advent of online public opinions and the sentiments expressed by netizens have heightened the complexity of the urban relocation policy process. The surge in negative emotions observed online poses a challenge, potentially leading to offline public risk-coping behaviors. However, amid these challenges, it is crucial to recognize the positive impact of online public opinions and emotions. These digital expressions can serve as a mirror reflecting the genuine attitudes and coping mechanisms of the public during the urban relocation process. Moreover, they offer a unique opportunity to anticipate the occurrence of mass incidents. This study aims to validate the hypothesis that negative emotions online can predict the public's risk-coping behaviors in the relocation process. If confirmed, it implies that real-time data on negative emotions can be monitored during urban relocation policy implementation, enabling timely and effective prediction of risk-coping behaviors. This approach serves as a supplementary method towards conventional means of policy risk assessment.
The existing research substantiates the significant role of negative emotions in shaping public risk-coping behaviors (Ramli et al., 2014; Lei and Yang, 2016; Hu et al., 2020). Moreover, it establishes that negative emotions possess predictive capabilities regarding the public's risk-coping behaviors (Ford and Feinberg, 2020). However, the current body of research seldom addresses two critical questions: first, whether negative emotions expressed online can predict the public's risk-coping behaviors in the context of urban relocation policy; second, exploring factors that influence the accuracy of this prediction. This study aims to address these two research questions.
This paper posits hypotheses suggesting that negative emotions can predict the public's risk-coping behaviors during the Beijing Municipal Govermnent relocation. The accuracy of prediction is proposed to be influenced by types, issues, participants, and stakeholders, as derived from a comprehensive literature review. These hypotheses are then tested using the Granger Causality Analysis of time series data. The time series data on online negative emotions are sourced from Sina Weibo, while risk-coping behaviors are quantified through public information search behaviors and the incidence of violent events.
2.1 The Prediction of Risk-Coping Behaviors by Negative Emotions
2.1.1 Risk-Coping Behaviors in Urban Relocation
The displacement and resettlement resulting from urban relocation (Terminski, 2013; Wet, 2006) have garnered increasing attention from scholars. This phenomenon, deemed essential for enhancing sustainable urban development, concurrently introduces social risks to urban management. The success of relocation plans hinges on the improved quality of life and economic well-being for the relocated public, along with the fulfillment of reasonable demands (Tang et al., 2022). Successful implementation relies on cooperative efforts among all actors, stakeholders, and the public, fostering mutual understanding based on shared goals and visions (Kaida and Miah, 2015). This collaborative approach actively promotes the relocation process, attains expected policy outcomes, and contributes to urban development objectives. However, if the living environment deteriorates post-relocation, resulting in short to medium-term livelihood stagnation and poverty (Kaida and Miah, 2015), the public may engage in various risk-coping behaviors, impacting urban sustainable development and social stability. In situations of perceived risk, individuals undertake risk and response assessments, deciding on actions they deem appropriate (Ganster and Rosen, 2013; Aven, 2016). Defined as conscious, purposeful, and flexible changes in emotions, cognition, behavior, and environment in response to stress and risk, risk-coping behaviors are conscious efforts to reduce the perceived risk of a threatening event in a given situation (Lazarus and Folkman, 1984; Compas et al., 2001). Although integral to individual stress management, risk-coping behaviors also hold significant value for effective social risk management. In this study's research scenario, the coping behaviors adopted by the public to safeguard their interests introduce uncertain risks in the urban relocation process. Public risk-coping behaviors, as the focus of this paper, arise from dissatisfaction with public demands not being met. Typically manifested through information-seeking behaviors and an uptick in violent criminal acts in the relocation area, these behaviors underscore the complex dynamics at play during urban relocation-induced social changes.
Lindell and Perry (2011) identified information searching behavior as a manifestation of risk-coping behavior. In such contexts, individuals tend to engage in information search when perceiving a genuine threat and sensing an unacceptable level of personal risk. When presented with highly credible sources, the public may unquestionably comply with instructions, while ambiguity may prompt increased efforts in seeking and processing information (Huang et al., 2010). In the Internet era, given the uncertainty surrounding relocation information and the perceived threat associated with it, the public frequently turns to online platforms to acquire relevant policy information—specifically, searching for relocation policies. Hu et al. (2019) corroborated the correlation between the public's information search behavior and their risk perception through a questionnaire survey. In essence, heightened information search activity may signal increased risk perception, correlating with a greater likelihood of resistance against the relocation.
Violent conflict behavior represents another coping strategy that may introduce social risks during the relocation process. Dissatisfaction with relocation compensation and resettlement may lead to violent protests against the relocation, or large-scale conflicts between the involved parties. Some scholars argue that certain forms of relocation inherently involve violence. For instance, Ortega's (2020) research suggests that Manila, in its pursuit of becoming a modern and investment-friendly city, forcefully relocates informal settlers to the outskirts—an inherently violent form of suburbanization. Consequently, the public may resort to corresponding violent actions in response. Begega and Kohler (2022) discussed protest behavior and the mobilization process in enterprise production relocation through case studies. Coffin et al. (2022) examined relocation delays stemming from conflict behaviors in utility relocation processes. Furthermore, a decrease in poverty and quality of life resulting from relocation may contribute to an increase in violent crimes in the relocated area. Casea et al. (2016) argue that areas of concentrated poverty and public housing face persistent issues with high rates of violent crime, while relocated residents may not necessarily experience less violence or improved safety in their new communities. Popkin et al. (2012) contend that while most relocations reduce violent crimes, the relocation of medium and high-density communities may negatively impact domestic violence crimes.
2.1.2 Negative emotions and Risk-Coping Behaviors
Research on emotions originated in psychology, attributing emotions to physiological mechanisms such as the impact of the prefrontal cortex on the amygdala. Sociologists have also extensively studied emotions, recognizing their integration with social network relations, people, places, time, space, and events, giving rise to the concept of social emotions. It is believed that the formation of social emotions relies on individuals’ perception of society. Once formed, social sentiments influence social behavior, with social emotions driving fiscal, macroeconomic, and political behaviors, according to Prechter (2003).
The internet's global reach, breaking time and space constraints, significantly enhances individual social cognition levels, contributing to the formation of social emotions. Internet users' emotions, often magnified infinitely, shape netizens' emotions and impact public social behavior. It is posited that internet users' emotions influence risk-coping behaviors, challenging the notion that social behaviors drive emotions.
When focusing on negative emotions, studies suggest their impact on risk-coping behaviors. For instance, Ramli et al. (2014) found that controlling negative emotions during group gatherings reduces aggressive and dangerous behaviors. Lei and Yang (2016) proposed that stronger negative emotions correlate with higher likelihoods of choosing high-risk coping behaviors. Hu et al. (2020) quantitatively established a positive correlation between risk perception, negative emotions, and rumor-spreading behavior, with negative emotions playing mediating roles.
Despite evidence of negative emotions driving risk-coping behaviors, there is limited research exploring whether negative emotions can predict public risk-coping behaviors in urban relocation processes. Building on prior studies, this paper hypothesizes that negative emotions online can effectively predict public risk-coping behaviors during urban relocation.
H1: Negative emotions online can predict public risk-coping behaviors in the urban relocation process.
2.2 Factors Influencing the Prediction Effect
2.2.1 Different Types of Negative emotions
Initially, emotional analysis methods were predominantly employed by computer and information science scholars, utilizing coarse-grained analysis focused solely on positive and negative attitudes. Watson and Tellegen (1985) categorized self-reported emotions into positive and negative emotions, introducing the PANAS scale for emotional measurement. Subsequent research included intensity analysis based on this coarse-grained dichotomy. Russell et al. (1989) developed a circular emotion model, classifying emotions according to pleasure and intensity, resulting in four categories: high-intensity pleasure, medium-intensity pleasure, high-intensity displeasure, and medium-intensity displeasure. As emotional analysis technology matured, scholars transitioned from a simple dichotomy to a more systematic, fine-grained analysis of public emotional information. Ekman (1993), for instance, divided emotions into six basic states: joy, sorrow, anger, fear, surprise, and disgust.
Chinese text analysis also identifies various negative emotions. One approach is fine-grained, as seen in Liu's (2016) classification of negative emotions, including anxiety, sadness, anger, disgust, and fear. Another approach, based on coarse-grained dichotomy, involves differentiating negative emotions by degree. For instance, the HowNet Dictionary separates negative feeling words from negative comment words (Qi et al., 2019; Dong and Dong, 2003). Negative feelings express strong dissatisfaction and opposition on a topic, while negative comments reflect differing views. Negative comments often reflect a more moderate public attitude than negative feelings, where the intensity of negative feelings is higher than that of negative comments. In comparison to fine-grained analysis, HowNet's classification accentuates distinctions within negative emotions. This study employs HowNet's classification to analyze the influence of different negative emotions on prediction effects. Xie and Peng (2018) confirmed that negative comments significantly impact risk perception, while negative feelings show no significant impact. This discrepancy may arise from the notion that rational negative comments are more likely to elicit public acceptance, whereas negative feelings expressed online may not influence social reality. Given the significant relationship between risk perception and risk-coping behaviors, research hypothesis 2 is as follows:
H2: Negative comments online can more effectively predict public risk-coping behaviors than negative feelings online in the urban relocation process.
2.2.2 Negative Emotions on Different Issues
Issue setting is a form of agenda setting (Kiousis and Shields, 2008), wherein the media accentuates specific matters to influence the public's attitudes or understanding. It is widely acknowledged that framing issues significantly impact public awareness and behavior (Dunaway et al., 2010; Iyengar and Simon, 1993). Therefore, the paper posits that negative emotions related to different issues will differ significantly in predicting risk-coping behaviors. Grounded in policy process theory, public policy undergoes stages such as formulation, implementation, evaluation, and termination (Dye, 2013). Thus, issues within a public policy can be classified into three types: policy formulation, policy implementation, and policy effectiveness. The policy formulation process is more likely to draw public attention and discussion than other stages (Zhang et al., 2018). Thus, the third hypothesis of this paper is presented as follows:
H3: Negative emotions related to the issue of relocation policy formulation can more effectively predict public risk-coping behaviors than those related to issues of relocation policy effectiveness and relocation policy implementation.
2.2.3 Negative Emotions from Different Participants
Online emotions serve as a reflection of the public's mindset and behaviors. Numerous studies have confirmed that personal characteristics significantly influence public risk-coping behaviors (Iravani, 2012; Neblett et al., 2011). Therefore, variations in the subjects of emotions may result in different risk-coping behaviors. Public opinion participants can be classified in various ways, such as categorizing them as opinion leaders, active users, or inactive users based on the enthusiasm of their online behaviors (Weeks et al., 2015), or as government, media, and the public based on their roles in online posting (Dong, 2018).
In contrast to negative emotions expressed by individual citizens, emotions emanating from government and media are subject to "gatekeepers" and may not directly mirror public opinion (McCarthy et al., 1996). Thus, this paper proposes that emotions expressed by individuals are more effective in predicting risk-coping behaviors compared to those from government or media, leading to the formulation of the fourth hypothesis.
H4: Negative emotions expressed by individuals are more effective in predicting public risk-coping behaviors than those expressed by institutions in the urban relocation process.
2.2.4 Negative Emotions from Different Stakeholders
Stakeholders, initially an economic concept, refer to individuals or entities with claims to a company's cash flow. In the realm of social science or management research, stakeholders encompass any entity affected by organizational decisions and actions in the external environment. As per stakeholder theory, numerous risk factors influencing social stability often escalate into significant social conflicts not solely due to the actual magnitude of the risk, which can be challenging to control and resolve. Instead, these conflicts are largely rooted in varying risk perceptions among different stakeholders (Wang et al., 2014).
From a relevance perspective, stakeholders can be categorized into three groups: key stakeholders, non-key stakeholders, and marginal stakeholders (Famiyeh, 2017; Feuvre et al., 2016). Key stakeholders are essential resources that institutions rely on for survival and development; their support crucial for the institution's continued existence and growth. While the existence and development of institutions are intertwined with the support of non-key stakeholders, the degree of dependence on them for enterprise survival and development is notably lower than that of key stakeholders. Marginal stakeholders, on the other hand, are not indispensable for the institution's existence and development.
Numerous studies have explored the differences in risk perception among various stakeholders. The majority of research findings indicate that the risk perception of key stakeholders surpasses that of other stakeholders (Williams et al., 1999; Vasi and King, 2012). As a result, risk-coping behaviors originating from key stakeholders are anticipated to be higher than those from other stakeholders. Therefore, this paper proposes Hypothesis 5.
H5: Negative emotions expressed by key stakeholders are more effective in predicting public risk-coping behaviors than those expressed by non-key stakeholders or marginal stakeholders in the urban relocation process.
3.1 Case Study
Beijing's population had already exceeded 20 million in 2012, as shown in Fig. 1. If not controlled, overpopulation will lead to a series of megacity-related issues, such as traffic congestion, a decline in service levels, resource shortages, and the destruction of the ecological environment. In this context, both the central government and the Beijing government are contemplating the possibility of seeking a breakthrough. The relocation policy implemented by the Beijing Municipal Government is a public policy aimed at evacuating the population.
Figure 2 summarizes the policy process of the Beijing Government Relocation. Since the beginning of 2015, rumors about the relocation by the Beijing Municipal Government had been circulating among the public. Many people initially reacted with disbelief, but those who were well-informed began to purchase houses in the Tongzhou District of Beijing. By July 2015, Beijing's stance on the implementation of the Beijing-Tianjin-Hebei Cooperative Development Plan was adopted, confirming the government's policy of relocating to Tongzhou. On November 25, 2015, during the Eighth Plenary Session of the Eleventh Beijing Municipal Committee of the Communist Party of China, it was announced that all municipal administrative institutions in Beijing would collectively or partially move to the Tongzhou Administrative Vice-Center in 2017, with the relocation schedule being determined for the first time.
Since December 2015, the negative effects of the policy have surfaced one after another. The most immediate consequence of the policy is the surge in housing prices in Tongzhou and its neighboring areas. The spike in house prices has led to sudden wealth for some individuals, while others have experienced a loss of income, resulting in social injustice and exacerbating social conflicts. For instance, on November 8–9, 2015, four consecutive intentional injury cases related to demolition and relocation took place in Xinzhuang Village, Yanjiao Town, Sanhe City, Hebei Province (CNR, 2015).
Although fraught with numerous risks, the relocation of the Beijing Municipal Government proceeded. By the end of December 2017, only a few departments had adhered to the relocation schedule outlined in November 2015, and the initial batch of ministries and commissions commenced the move to Tongzhou. On January 11, 2019, four groups comprising the Beijing Municipal Committee of the Communist Party of China, the Beijing Municipal People's Government, the Beijing People's Congress, and the Beijing Political Consultative Conference collectively relocated to Tongzhou, officially implementing the policy.
Spanning nearly four years from its initial announcement to implementation, the policy had been widely discussed on the Internet since the emergence of rumors. Simultaneously, risk-related issues such as rising housing prices, education, and healthcare garnered public interest, with the repercussions of policy risks still ongoing. This paper selects this policy as a representative case of relocation policies to explore solutions for various challenges arising from China's rapid economic development and investigate the relationship between online negative emotions and public risk perception. Based on the case analysis, the data for analysis was obtained from April 30, 2015, to April 30, 2019.
3.2 Variables of Negative Emotions
3.2.1 Acquisition of Analytical Data
Presently, Sina Weibo remains as the most popular and influential microblogging platform in China; thus, it was chosen as the sample source. Using keywords such as "Beijing government relocation," "Beijing relocation to Tongzhou," "Beijing government eastward relocation," and "Tongzhou New Town," searches were conducted on Sina Weibo from April 30, 2015, to April 30, 2019. Over 20,000 public posts about this policy were collected. Through the emotional judgment system developed by Boryou Technology (https://www.boryou.com/, accessed on August 12, 2022), 3,133 posts on Sina Weibo related to negative emotions were identified. After manual data cleaning, which involved removing information unrelated to the policy, eliminating duplicates, and excluding non-negative emotions, 933 posts related to the relocation policy of the Beijing Municipal Government were retained. The time series data for the number of posts containing negative emotions were organized to align with the data on public risk-coping behaviors.
3.2.2 Manual Coding of Variables
| Factors | Variables | Manual Coding |
|---|---|---|
| Types | A1: Negative feelings | If the content of the text is expressions of strong dissatisfaction and strong opposition, then code 1. If not, code 0. |
| A2:Negative comments | If the content of the text is expressions of different views about the public policy, and the opposing attitude is more moderate, then code 1. If not, code 0. | |
| Issues | B1: Policy formulation | If the content of the text is to express opinions of policy making (such as agenda setting, policy plan, etc.), then code 1. If not, code 0. |
| B2: Policy implementation | If the content of the text is to express opinions of policy implementation (e.g. group incidents, rumors, etc.), then code 1. If not, code 0. | |
| B3: Policy effectiveness | If the content of the text is to express opinions of the policy effect (for example the policy caused the rise of housing prices, the policy caused the problems of education, medical care, etc.), then code 1. If not, code 0. | |
| Participants | C1: Institutions | If the publisher is media or government, the code is 1, if not 0. |
| C2: Individuals | If the publisher is an ordinary person, the code is 1, if not 0. | |
| Stakeholders | D1: Key Stakeholder | If the negative information is published in Beijing, the code is 1. If not, the code is 0. |
| D2: Non-Key stakeholder | If the negative information is published in Tianjin or Hebei, the code is 1. If not, the code is 0. | |
| D3: Marginal stakeholders | If the negative information is not published in Beijing, Tianjin or Hebei, the code is 1. If not, the code is 0. |
Two coders with backgrounds in linguistics and public administration were selected and trained to independently perform the coding. The credibility of the results is considered acceptable if the repetition rate between the two coders exceeds 90%. The manually encoded data is then organized into time series data.
3.3 Measurement of Public Risk-coping behaviors
Based on the literature review, two distinct categories of public risk-coping behaviors in urban relocation have been identified: violent protests against relocation and violent criminal acts within the relocated areas. This paper assesses public risk-coping behaviors in the urban relocation process through an examination of these two dimensions.
3.3.1 Information Searching Behaviors
This paper employed the Baidu Index to gauge public information searching behaviors. The time series data capturing the public's search volume related to the "Beijing government relocation" policy were obtained through the Baidu search index (http://index.baidu.com, accessed on June 1, 2022).
3.3.2 Violent Crime Behaviors
In previous studies, violent crimes have typically been derived from official statistics. The violent crimes investigated in this paper require time series statistical analysis in conjunction with online public opinion. Online data can be captured at specific intervals, even down to the minute or second, while offline official statistics are typically reported annually or quarterly, posing challenges for correlation studies. Therefore, this paper utilizes big data mining to acquire daily data on violent crimes. Davenport et al. (2011) employed news reports to study public behaviors. Traditional newspaper media, unlike new media, tends to track crisis events, offering insights into the essence of these events. Hence, this paper initially identifies violent crime behaviors in the relocation area using the "newspaper database" from the China National Knowledge Infrastructure (CNKI). Keywords such as "Beijing Tongzhou," "violence incident," "mass incident," "petition," and "crime" were used in the search, and the data on behaviors were manually assessed and filtered.
Moreover, official microblog accounts serve as extensions of government functions, and each piece of information released through them can be considered credible. Thus, government microblogs are selected as sources of data on violent crime behaviors. Given that the "Beijing Municipal Government Relocation" policy primarily impacted Beijing and Hebei, the official microblog accounts of the Beijing Municipal Public Security Department, "@ Ping An Beijing," and the Hebei Provincial Public Security Department, "@ Hebei Public Security Network Spokesman," were chosen to gather crime data. Keywords such as "violent events," "mass events," "petitions," and "crimes" were used in the search, and manual judgment was applied to determine whether the behavior constituted a violent crime. By combining newspaper data and government microblog data, a time series dataset of violent crime behaviors was compiled.
3.4 Granger causality analysis
Granger causality analysis, pioneered by Clive W. J. Granger, the 2003 Nobel Prize winner in economics, has been widely employed by scholars to examine the predictive relationship between variables (Gelper et al., 2007; Remzi and Çankal, 2020). This test was initially developed to analyze Granger causality in economic variables. Granger defined Granger causality as dependence on the variance of the best least squares prediction using all the information at some point in the past.
The original hypothesis of the Granger causality test is as follows:
Hypothesis 0
Variable x cannot Granger cause variable y;
Hypothesis 1
Variable x can Granger cause variable y.
The calculation formula is presented in Formula 1. If the p-value is less than 0.05, it indicates that H1 is established; if the p-value is greater than 0.05, it means H0 is established.
Formula 1. Granger causality analysis.
This paper employs the Granger causality analysis method to examine the predictive impact of online negative emotions on public risk perception, through three steps: unit root test, VAR model construction, and establishment of the optimal lag date. The analysis was conducted using Eviews software.
4.1 Verification of the prediction of public risk perception by negative emotions
4.1.1 Unit Root Test
Only stationary series can be used for the Granger test. The Unit Root Test was employed to assess whether the data represent stationary series. The time series data for negative emotions and risk perception underwent the Augmented Dickey-Fuller (ADF) test in Eviews. Table 2 shows that at a 1% significance level, the variables of negative emotions, information searching behaviors (ISB), and violent crime behaviors (VCB) are stationary and meet the conditions for modeling. Consequently, an unconstrained VAR model was established.
| t-Statistic | Prob.* | Test critical values | |||
|---|---|---|---|---|---|
| 1% level | 5% level | 10% level | |||
| Negative emotions | -24.92146 | 0.0000 | -3.964525 | -3.412980 | -3.128488 |
| ISB | -8.230661 | 0.0000 | -3.964534 | -3.412985 | -3.128490 |
| VCB | -3.599641 | 0.0059 | -3.434779 | -2.863383 | -2.567800 |
4.1.2 VAR Model Construction
The VAR model was estimated using the AR root estimation method to test the stationarity of the results. AR root estimation is based on the principle that if the reciprocal of all root modules of the VAR model is less than 1, indicating that all root modules are within the unit circle, then the model is stable. Conversely, if the reciprocal of all root modules of the VAR model is greater than 1, meaning that all root modules are outside the unit circle, then the model is unstable. This paper constructed two VAR models: negative emotion and information search behavior (Model 1), negative emotion and violent criminal behavior (Model 2). Figures 3 and 4 display the Inverse roots results of the two VAR models. As shown in the Figures, no root is outside the unit circle, confirming that the estimated VAR models satisfy the stability condition.
4.1.3 Granger causality test and optimal lag date establishment
The optimal lag time, determined by both the Akaike Information Criterion (AIC) and Schwarz Information Criterion (SC) in models 1 and 2, was found to be 3. Therefore, VAR (Lag = 3) was selected for the test, and the results are presented in Table 3. The probability that negative emotion does not have Granger Causality of information search behavior is only 0.0000. Hence, negative emotions can predict information search behaviors in advance. Additionally, the probability that negative emotion does not have Granger Causality of violent crime behavior is only 0.0189 (< 0.05). Therefore, negative emotions can predict violent crime behaviors in advance. The research hypothesis 1 was confirmed in both models.
| Null Hypothesis | F-Statistic | Prob. | Lag |
|---|---|---|---|
| Model1: Negative emotions does not Granger Cause Information Searching Behaviors | 33.6063*** | 0.0000 | 3 |
| Model 2: Negative emotions does not Granger Cause Violent Crime Behaviors | 3.33127* | 0.0189 | 3 |
| Note 1. Model1: Max lag(= 3) selected according to Akaike information criterion (AIC) by building VAR model. AIC(Lag = 1),17.29692; AIC(Lag = 2),17.28203 AIC(Lag = 3),17.23450; SC(Lag = 1), 17.31872; SC(Lag = 2),17.31836; SC(Lag = 3),17.28536. | |||
| Note 2. Model 2: Max lag(= 3) selected according to Akaike information criterion (AIC) by building VAR model. AIC(Lag = 1), 6.938467; AIC(Lag = 2), 6.818072; AIC(Lag = 3), 6.774964; SC(Lag = 1), 6.960723; SC(Lag = 2), 6.855167; SC(Lag = 3), 6.826896. | |||
| Note 3. * significant at 0.05; **significant at 0.01; ***significant at 0.001. | |||
4.2 Verification of the factors influencing the prediction effect
Table 4 indicates that at the 1% significance level, all variables related to types, issues, participants, and stakeholders are stationary and meet the conditions for modeling. Unconstrained VAR models were constructed for each variable and risk perception, and the AR root estimation method was employed to test the stationarity of the estimated results of the VAR model. After the test, no root was found outside the unit circle, confirming that the estimated VAR model satisfies the stability condition.
| Factors | Variables | t-Statistic | Prob.* | Test critical values | ||
| 1% level | 5% level | 10% level | ||||
| Types | Negative comments | -17.24982 | 0.0000 | -3.434624 | -2.863315 | -2.567763 |
| Negative feelings | -36.97040 | 0.0000 | -3.434618 | -2.863312 | -2.567762 | |
| Issues | policy formulation | -17.24982 | 0.0000 | -3.434624 | -2.863315 | -2.567763 |
| policy implementation | -36.74461 | 0.0000 | -3.434618 | -2.863312 | -2.567762 | |
| policy effectiveness | -18.88733 | 0.0000 | -3.434624 | -2.863315 | -2.567763 | |
| Participants | Institution emotions | -17.61006 | 0.0000 | -3.434624 | -2.863315 | -2.567763 |
| Individual emotions | -26.63745 | 0.0000 | -3.434618 | -2.863312 | -2.567762 | |
| Stakeholders | Key stakeholders | -25.65244 | 0.0000 | -3.434618 | -2.863312 | -2.567762 |
| Non-key stakeholders | -6.016446 | 0.0000 | -3.434664 | -2.863333 | -2.567773 | |
| Marginal stakeholders | -18.71895 | 0.0000 | -3.434624 | -2.863315 | -2.567763 | |
To assess Hypotheses 2 to 5, Granger causality analysis was conducted to evaluate the overall predictive impact of the variables representing ten influencing factors on public risk-coping behavior.
Table 5 presents the analysis results for public risk-coping behavior, as indicated by information search behavior. The findings revealed that among the two variables representing negative emotion types, negative comments (A1, 8.30***) and negative feelings (A2, 4.11**), the probability that negative comments were not the Granger cause of risk-coping behavior was less than 0.001. In contrast, the probability that negative feelings were not the Granger cause of risk-coping behavior was less than 0.01. Thus, negative comments can better predict public risk-coping behavior, confirming Hypothesis 2.
Regarding the analysis of issues, policy formulation (B1, 10.36***) was found to predict risk-coping behavior, while policy implementation and policy effectiveness were not identified as Granger causes of risk-coping behavior. The probabilities were 0.446 (B1) and 0.503 (B2). Therefore, public risk-coping behavior cannot be predicted by negative emotions related to these two issues, supporting Hypothesis 3.
Analysis of participants indicated that negative emotions from institutions (C1, 0.33) could not predict public risk-coping behavior, while negative emotions from individuals (C2, 16.38***) might be the cause of public risk-coping behavior. Thus, Hypothesis 4 was validated.
In the variables related to stakeholders, the probability that negative emotions from key stakeholders (D1, 14.01 ***) were not the Granger cause of public risk-coping behavior was less than 0.001. This suggests that negative emotions from key stakeholders can predict risk-coping behavior, while negative emotions from non-key stakeholders (D2, 0.048) and marginal stakeholders (D3, 0.17) cannot predict risk-coping behavior. Therefore, Hypothesis 5 was confirmed.
| A1 | A2 | B1 | B2 | B3 | C1 | C2 | D1 | D2 | D3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Lag | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| SC | 17.19 | 13.64 | 16.61 | 14.03 | 15.38 | 16.02 | 16.41 | 16.63 | 13.80 | 15.75 |
| F | 8.30*** | 4.11** | 10.36*** | 0.89 | 0.78 | 0.33 | 16.38*** | 14.01*** | 0.048 | 0.17 |
| P | 0.000 | 0.007 | 0.000 | 0.446 | 0.503 | 0.803 | 0.000 | 0.000 | 0.986 | 0.914 |
| Note. * significant at .05; **significant at .01; ***significant at .001. | ||||||||||
Table 6 presents the analysis results for public risk-coping behavior represented by violent crime behavior. The outcomes indicated that among the two variables representing negative emotion types, negative comments (A1, 9.09***) and negative feelings (A2, 1.90), the probability that negative comments were not the Granger cause of risk-coping behaviors was less than 0.001. In contrast, the probability that negative feelings were not the Granger cause of risk-coping behavior was greater than 0.05. Therefore, negative comments can better predict public risk perception, thus confirming Hypothesis 2.
Concerning the three variables related to relocation issues, the predictive effect of policy formulation (B1, 8.54***, p < 0.001) was superior to policy implementation (B2, 3.28**, p < 0.01) and policy effectiveness (B3, 2.85*, p < 0.05). The probability that negative emotions on the policy formulation issue were not the Granger cause of risk-coping behavior was lower, validating Hypothesis 3.
Among the two variables representing negative emotions from different participants, emotions from institutions (C1, 3.66**) and emotions from individuals (C2, 10.36***), the probability that negative emotions from individuals were not the Granger cause of risk-coping behavior was less than 0.001. In contrast, the probability that negative emotions from institutions were not the Granger cause of risk-coping behavior was less than 0.01. Thus, negative emotions from individuals can better predict public risk-coping behavior, confirming Hypothesis 4.
In the variables related to stakeholders, the probability that negative emotions from key stakeholders (D1, 7.54***, P = 6.E08) were not the Granger cause of public risk perception was the least. This suggests that negative emotions from key stakeholders can better predict risk-coping behavior than negative emotions from non-key stakeholders (D2, 1.92*, P < 0.05) and marginal stakeholders (D3, 5.33***, P = 7.E05). Therefore, Hypothesis 5 was verified.
| A1 | A2 | B1 | B2 | B3 | C1 | C2 | D1 | D2 | D3 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Lag | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 6 | 16 | 5 |
| SC | 6.67 | 3.097 | 6.04 | 3.47 | 4.83 | 5.46 | 5.92 | 6.16 | 3.08 | 5.15 |
| F | 9.09*** | 1.90 | 8.54*** | 3.28** | 2.85* | 3.66** | 10.36*** | 7.54*** | 1.92* | 5.33*** |
| P | 2.E-08 | 0.0909 | 6.E-08 | 0.0060 | 0.0144 | 0.0027 | 9.E-10 | 6.E-08 | 0.0156 | 7.E-05 |
| Note. * significant at .05; **significant at .01; ***significant at .001. | ||||||||||
5.1 Summary of Major Findings and Contributions
The present research verified the impact of negative emotions on public risk-coping behaviors (Ramli et al., 2014; Lei and Yang, 2016; Hu et al., 2020; Ford and Feinberg, 2020), but fewer studies focused on the prediction of negative emotions on public risk-coping behaviors. This study verified that negative emotions can effectively predict public information searching behaviors and violent crime behaviors using the case of relocation policy of Beijing Municipal Government. Our findings further explored the factors influencing the prediction effect. The results indicated that: 1) The publics’ negative emotions expressed online towards the relocation policy can predict the public risk-coping behaviors in advance; 2) The negative comments can better predict the public risk-coping behaviors than the negative feelings; 3) The negative emotions on issue of relocation policy formulation can better predict the public risk-coping behaviors than on issues of relocation policy effectiveness and relocation policy implementation. 4) The negative emotions from individuals can better predict public risk-coping behaviors than from institutions. 5) The negative emotions from key stakeholder can better predict the public risk-coping behaviors than from non-key stakeholder or marginal stakeholder.
These findings have two contributions: (1) This study promotes the prediction of public risk-coping behaviors in the relocation policy. In the process of the relocation policy, in order to effectively reduce the public risk perception, which determines the successful implementation of the policy, the relocation policy related departments should pay attention to the negative public opinion on the network in a timely manner, because the negative emotions can effectively predict the pubic risk-coping behaviors. If the content of the policy is adjusted according to the negative emotions, the policy risk can be reduced. In the practice of relocation policy, we should pay attention on the negative emotions from the key stakeholders and individuals, the negative emotions on issue of policy formulation and the negative comments. (2) This study adds a case of the relocation policy of Beijing Municipal Government to the research fields of urban relocation policies in China. In the process of rapid economic development, China has to face the problems of urban relocations and it is of great significance that the relocation policies are successfully implemented. This study also further improves on our understanding of relocation policy from the perspective of risk-coping behaviors.
5.2 Limitations and Suggestions for Future Research
The primary limitation of this study lies in the inherent constraints associated with manual coding. The identification of negative emotion types and issues related to negative emotions relies on human judgment, introducing the possibility of deviations. To address this concern, the study implemented a rigorous approach by training two independent coders. However, the potential for subjectivity remains. Future research endeavors could explore the application of big data analysis methods to achieve more objective identifications, mitigating the limitations associated with manual coding.
Author Contribution
Xie qihui wrote the main manuscript text. Wu hongyu and zhangruwen collected the data. All authors reviewed the manuscript.
- Anjuke, 2016. House price of Yanjiao in 2016. https://www.anjuke.com/fangjia/beijing2016/yanjiao/ (accessed 21 April 2023).
- Aven, T., 2016. Risk assessment and risk management: Review of recent advances on their foundation. Eur. J. Oper. Res. 253, 1–13. https://doi.org/10.1016/j.ejor.2015.12.023.
- Begega, S.G., Khler, H.D., 2022. Workforces and local communities against corporate restructuring: a comparative case study of resistance to plant closures in Northern Spain. Soc. Mov. Stud. 21, 355-371. https://doi.org/10.1080/14742837.2021.1884975.
- Casas, A., Duell, J., O'Malley, T., Documet, P., Garland, R., Albert, S., Fabio, A., 2016. Housing Relocation Policy and Violence: A Literature Review. Trauma Violence Abus. 17, 601-610. https://doi.org/10.1177/1524838015603211.
- CNR, 2015. Four intentional injury cases in Yanjiao (Continued): 12 people were arrested and the reconstruction of the village involved was not started. http://news.cnr.cn/native/gd/20151113/t20151113_520491798.shtml (accessed 21 April 2023)
- Coffin, G.J., Wang, J., Howard, I.L., 2022. Utility Relocation Concept Plan for Mitigation of Utility Relocation Delays. J. Pipel. Syst. Eng. Pract. 13, 05021013. https://doi.org/10.1061/(ASCE)PS.19491204.0000630.
- Compas, B.E., Connor-Smith, J.K., Saltzman, H., Thomsen, A.H., Wadsworth, M.E., 2001. Coping with stress during childhood and adolescence: Problems, progress, and potential in theory and research. Psychol. Bull. 127, 87–127. https://doi.org/10.1037/0033-2909.127.1.87.
- Davenport, C., Soule, S.A., Armstrong, D.A., 2011. Protesting while black? The differential policing of American activism, 1960 to 1990. Am. Sociol. Rev. 76, 152-178. https://doi.org/10.1177/0003122410395370.
- DeWall, C.N., Baumeister, R.F., Chester, D.S., Bushman, B.J., 2016. How Often Does Currently Felt Emotion Predict Social Behavior and Judgment? A Meta-Analytic Test of Two Theories. Emot. Rev. 8, 136-143. https://doi.org/10.1177/1754073915572690.
- Dong, L.F.,2018. Research on the Main body of online public opinion formation stage based on SD evolutionary game theory. Inform. Sci. 36, 24-44. https://doi.org/10.13833/j.issn.1007-7634.2018.01.005.
- Dong, Z.D., Dong, Q., 2003. HowNet - A hybrid language and knowledge resource. In Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering, Beijing, Peoples R China, 26-29, pp. 820-824. https://doi.org/10.1109/NLPKE.2003.1276017.
- Dunaway, J., Branton, R.P., Abrajano, M.A., 2010. Agenda Setting, Public Opinion, and the Issue of Immigration Reform. Soc. Sci. Q. 91, 359-378. https://doi.org/10.1111/j.1540-6237.2010.00697.x.
- Dye, T.R., 2013. Understanding public policy, 14th ed., Pearson: Boston.
- Ekman, P., 1993. Facial expression and emotion. Am. Psychol. 48, 384-392. https://doi.org/10.1037/0003-066X.48.4.384.
- Famiyeh, S., 2017. Socially responsible mining using project stakeholder identification and management: An exploratory study. J. Glob. Responsib. 8, 151-168. https://doi.org/10.1108/JGR-10-2016-0026.
- Ford, B.Q., Feinberg, M., 2020. Coping with Politics: The Benefits and Costs of Emotion Regulation. Curr. Opin. Behav. Sci. 34, 123-128. https://doi.org/10.1016/j.cobeha.2020.02.014.
- Frith, C., 1995. Descartes' Error: Emotion, Reason and the Human Brain. Perception.24, 1223-1224.
- Ganster, D.C., Rosen, C.C., 2013. Work stress and employee health: A multidisciplinary review. J. Manag. 39, 1085–1122. https://doi.org/10.1177/0149206313475815.
- Gelper, S., Lemmens, A., Croux, C., 2007. Consumer sentiment and consumer spending: decomposing the Granger causal relationship in the time domain. Appl. Econ. 39, 1-11. https://doi.org/10.1080/00036840500427791
- Harris, P.L., Olthof, T., Terwogt, M.M., Hardman, C.E., 1987. Children's Knowledge of the Situations that Provoke Emotion. Int. J. Behav. Dev. 10, 319-343. https://doi.org/10.1177/016502548701000304.
- Hu, W., Wang, Q., Chen, H.L., Jiang, Y.H., 2020. The relationship between public risk perceptions and unintentional/intentional rumor-mongering behaviors in the Newcastle pneumonia outbreak: the mediating role of negative emotions. Chinese Journal of Clinical Psychology. 28, 734-738. https://doi.org/4.10.16128/j.cnki.10053611.2020.04.017.
- Hu, X.N., Guo, X.S., Lian, C., Zhao, H.Z., 2019. Study on the Risk Information Seeking and Processing of Public Policy : A Case Study of Disaster Resettlement Policy in Southern Shaanxi. J. Intell. 38, 72-180. https://doi.org/10.3969/j.issn.1002-1965.2019.02.025.
- Huang, S.K., Lindell, M.K., Prater, C.S., Wu, H.C., Siebeneck, L.K., 2010. Household Evacuation Decision Making in Response to Hurricane Ike. College Station, TX: Texas A&M University Hazard Reduction & Recovery Center.
- Huanqiu, 2016. The original office building of Beijing municipal government moved eastward to Tongzhou, which was favored by many ministries and commissions. Available online: https://china.huanqiu.com/article/9CaKrnJVvgl (accessed 21 April 2023).
- Iravani, M.R., 2012. A social work study high-risk behavior among teenagers. Management Science Letters. 2, 313-320. https://doi.org/10.5267/j.msl.2011.08.007.
- Iyengar, S., Simon, A., 1993. News coverage of the Gulf crisis and public opinion: A study of agenda-setting, priming, and framing. Commun. Res. 20, 365-383. https://doi.org/10.1177/009365093020003002.
- Kaida, N., Miah, T.M., 2015. Rural-urban perspectives on impoverishment risks in development-induced involuntary resettlement in Bangladesh. Habitat Int. 50, 73-79. https://doi.org/10.1016/j.habitatint.2015.08.008.
- Kemper, T.D., 1993. Social models in the explanation of emotions. Handbook of Emotions. 41-51.
- Kiousis, S., Shields, A., 2008. Intercandidate agenda-setting in presidential elections: Issue and attribute agendas in the 2004 campaign. Public Relat. Rev. 34, 325-330. https://doi.org/10.1016/j.pubrev.2008.07.004.
- Lazarus, R.S., Folkman, S., 1984. Stress, Appraisal, and Coping, Springer Publishing Company: Berlin, Germany.
- Le Feuvre, M., Medway, D., Warnaby, G., Ward, K., Goatman, A., 2016. Understanding stakeholder interactions in urban partnerships. Cities. 52, 55-65. https://doi.org/10.1016/j.cities.2015.10.017.
- Lei, T., Yang, N.D., 2016. A multifactorial study on the influence of personal emotions and behavioral decisions in the context of sudden disasters. Seeker. 114-118. https://doi.org/10.16059/j.cnki.cn431008/c.2016.03.022.
- Li, T., Liu, Y., Wang, C., Olsson,G., Wang, Z., Wang, H., 2019. Decentralization of the non-capital functions of Beijing: Industrial relocation and its environmental effects. J. Clean Prod. 224, 545-556. https://doi.org/10.1016/j.jclepro.2019.03.247.
- Lindell, M.K., Perry, R.W., (2011). The Protective Action Decision Model: Theoretical Modifications and Additional Evidence. Risk Analysis. 32(4), 616–632. https://doi.org/10.1111/j.1539-6924.2011.01647.x
- Lippmann, W., Curtis, M., 2017. Public opinion, Routledge: New York.
- Liu, B.Q., Niu, Y., 2015. Multi level emotional analysis of Chinese micro blog. Computer Technology and Development. 25, 23-26. https://doi.org/10.3969/j.issn.1673-629X.2015.11.005.
- Liu, H., Li, J., Li, H., Li, H., Mao, P., Yuan, J., 2021. Risk Perception and Coping Behavior of Construction Workers on Occupational Health Risks—A Case Study of Nanjing, China. IJERPH. 18. https://doi.org/10.3390/ijerph18137040.
- Liu, L., 2016. Research on Emotion Recognition of College Students Based on Forum Posts. Doctoral dissertation, Central China Normal University.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., 2011. Big data: The next frontier for innovation, competition, and productivity. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation (accessed 21 April 2023).
- McCarthy, J.D., Smith, J., Zald, M.N., 1996. Accessing public, media, electoral, and governmental agendas. Comparative perspectives on social movements. 291-311. https://doi.org/10.1017/CBO9780511803987.015.
- McCombs, M.E., Shaw, D.L., 1972. The agenda-setting function of mass media. Public Opin. Q. 36, 176-187. https://doi.org/10.1086/267990.
- Neblett, R.C., Davey-Rothwell, M., Chander, G., Latkin, C.A., 2011. Social Network Characteristics and HIV Sexual Risk Behavior among Urban African American Women. J. Urban Health. 88, 54-65. https://doi.org/10.1007/s11524-010-9513-x.
- NetEase, 2016. Super strict! Beijing Tongzhou issued a document overnight to restrict the purchase of apartments (Social Security for 5 years + single people with houses are not allowed to buy apartments). https://www.163.com/dy/article/BMBDQ00D05159CE7.html (accessed 21 April 2023).
- O'reilly, T., 2007. What is Web 2.0: Design patterns and business models for the next generation of software. Communications & strategies. 17.
- Ortega, A.A.C., 2020. Exposing Necroburbia: Suburban Relocation, Necropolitics, and Violent Geographies in ManilaMga susing salita. Antipode. 52, 1175-1195. https://doi.org/10.1111/anti.12629.
- Popkin, S.J., Rich, M.J., Hendey, L., Hayes, C., Parilla, J., Galster, G., 2012. Public housing transformation and crime: Making the case for responsible relocation. Cityscape. 14, 137-160.
- Prechter, R.R., 2003. Pioneering studies in socionomics, New Classics Library: Gainesville.
- Qi, F.C., Yang, C.H., Liu, Z.Y., Dong, Q., Sun, M.S., Dong, Z.D., 2019. Openhownet: An open sememe-based lexical knowledge base. https://doi.org/10.48550/arXiv.1901.09957.
- Qu, W.D., Yan, Z., Zhu, B., 2021. Unpaid commuting stress: Evaluation of the relocation policy of the Beijing Municipal Government. Cities. 113. https://doi.org/10.1016/j.cities.2021.103166.
- Ramli, N., Ghani, N.A., Hatta, Z.A., Hashim, I.H.M., Sulong, J., Mahudin, N.D.M., Abd Rahman, S., Saad, Z.M., 2014.Bayesian Network Model of Crowd Emotion and Negative Behavior. In Proceedings of the 3rd International Conference on Quantitative Sciences and Its Applications (ICOQSIA), Kedah, MALAYSIA, 12-14, pp. 867-874.
- Remzi, G., Çankal, E., 2020. Granger causal relationship between bond yield changes and equity returns through wavelets analysis: The case of Turkey. Ege Acad. Rev. 20, 301-317. https://doi.org/10.21121/eab.618452.
- Russell, J.A., Lewicka, M., Niit, T., 1989. A cross-cultural study of a circumplex model of affect. J. Pers. Soc. Psychol. 57, 848. https://doi.org/10.1037/0033-2909.98.2.219.
- Shepherd, L., Spears, R., Manstead, A.S.R., 2013. 'This will bring shame on our nation': The role of anticipated group-based emotions on collective action. J. Exp. Soc. Psychol. 49, 42-57. https://doi.org/10.1016/j.jesp.2012.07.011.
- Sohu, 2015. Beijing official response to the rumor that the municipal government moved to TongZhou: subject to the authoritative release. https://www.sohu.com/a/18232426_114984 (accessed 21 April 2023).
- Tang, J., Xu, Y., Qiu, H., 2022. Integration of migrants in poverty alleviation resettlement to urban China. Cities. 120, 103501. https://doi.org/10.1016/j.cities.2021.103501.
- Terminski, B., 2013. Development-Induced Displacement and Resettlement: Theoretical Frameworks and Current Challenges. Development. 10:101.
- Tu, C., Mu, X.Z., Wu, Y.F., Gu, Y.F., Hu, G.W., 2021. Heterogenous impacts of components in urban energy metabolism: evidences from gravity model. Environ. Dev. Sustain. 24:10089–10117. https://doi.org/10.1007/s10668-021-01857-4.
- Vasi, I.B., King, B.G., 2012. Social Movements, Risk Perceptions, and Economic Outcomes: The Effect of Primary and Secondary Stakeholder Activism on Firms' Perceived Environmental Risk and Financial Performance. Am. Sociol. Rev. 77, 573-596. https://doi.org/10.1177/0003122412448796.
- Wang, F., Hu, X., Liu, P., 2014. Empirical studies of anxiety, risk perception and NIMBY conflict–landfill sites in Beijing as an example. J. Beijing Instit. Technol. Soc. Sci. Ed. 16, 61-67.
- Wanghe, K.Y., Guo, X.L., Luan, X.F., Li, K., 2019. Assessment of Urban Green Space Based on Bio-Energy Landscape Connectivity: A Case Study on Tongzhou District in Beijing, China. Sustainability. 11, 4943. https://doi.org/10.3390/su11184943.
- Watson, D., Tellegen, A., 1985. Toward a consensual structure of mood. Psychol. Bull. 98, 219-235. https://doi.org/10.1037/0033-2909.98.2.219.
- Weeks, B.E., Ardèvol-Abreu, A., Homero, G., 2015. Online Influence? Social Media Use, Opinion Leadership, and Political Persuasion. Int. J. Public Opin. Res. 29, 214-239. https://doi.org/10.1093/ijpor/edv050
- Wet, C.D., 2006. Development-induced displacement: Problems, policies and people, Berghahn Books: New York.
- Williams, B.L., Brown, S., Greenberg, M., Kahn, M.A., 1999. Risk perception in context: the Savannah River Site Stakeholder Study. Risk. Anal. 19, 1019-1035. https://doi.org/10.1111/j.1539-6924.1999.tb01125.x.
- Xie, Q.H., Peng, Z.C., 2018. An empirical study on Netizens' negative emotions and psychological risk perception -- from the perspective of major decision-making and social stability risk assessment. Scientific Decision Making. 36-53. https://doi.org/10.3773/j.issn.1006-4885.2018.04.036.
- Zhang, Y.Q., Loh, C., Louie, P.K.K., Liu, H., Lau, A.K.H., 2018. The roles of scientific research and stakeholder engagement for evidence-based policy formulation on shipping emissions control in Hong Kong. J. Environ. Manage. 223, 49-56. https://doi.org/10.1016/j.jenvman.2018.06.008.
No competing interests reported.