Climate Stress, Behavioral Adaptation and Subjective Well Being in Coastal Cities of India

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1.3. Climate Stress

Climate stress has been found to be generated by changes in the environment, as opposed to the regular stresses caused by activities within that environment. Climate stressors are typically not evident in day to day life but do have indirect impacts upon one’s physical and mental capacities. Studies on the effects of climate stressors indicate that they do have impacts upon one’s behavior, moods, lifestyle, cognitive function, physical health and/or psychological well being. Climate change in coastal areas is associated much with flooding, SLR, land inundation, storms, cyclones etc. In present times, Indian coasts are confronted with problems of increasing human pressures which is causing overexploitation of coastal resources. Human activities such as dumping of industrial and toxic wastes, oil spills and leaks have all caused substantial damage to coastal ecosystems of many of the Indian coastal zones. Coping with and adapting to climate stress are therefore issues of concern for experts worldwide.

1.4. Behavioral Adaptation

Behavioral adaptation is an adjustment in individual, group and institutional behavior in order to reduce society’s vulnerabilities to climate (Pielke, 1998). It involves adjustments to decrease the vulnerability of communities, regions, and nations to climate variability and change and in promoting sustainable development (IPCC 2001). As a result of anthropogenic/Socio-economic factors, Indian Coastal population is currently facing great pressure in the form of cumulative environment degradation driven by population expansion, urbanization, industrial development, environmental unfriendly life style, attitudinal changes and unsustainable growth. Coastal population can better deal with such a situation by way of behavioral adaptation which would enable them to bring certain changes in processes, practices and structures so as to moderate potential damages or to benefit from opportunities associated with climate change.

1.5.Subjective Well Being

SWB is ‘a broad category of phenomena that includes people’s emotional responses, domain satisfactions, and global judgments of life satisfaction’ (Diener et al., 1999). Specifically, reported SWB consists of two distinctive components (Diener, 1994): an affective part, which refers to both the presence of positive affect (PA) and the absence of negative affect (NA), and a cognitive part. The affective part is a hedonic evaluation guided by emotions and feelings, while the cognitive part is an information-based appraisal of one’s life for which people judge the extent to which their life so far measures up to their expectations and resembles their envisioned ‘ideal’ life. Climate change induced coastal stress ought to have negative impacts on the SWB of people in view of the present ongoing changes taking place along with the perceived threats in future. As a result, this would definitely alter the affective and cognitive dimensions of one’s personality. There is much probability that the positive affect gets replaced with negative affect. The cognitive part too can not remain unaltered in changing climate scenario. People’s information processing with regard to climate change gets influenced with the numerous information they encounter through internet, books, media and so on. As a result, it is a matter to be studied that how the subjective well being gets altered and in what ways when confronted to climate change issues.

1.6. Climate Change and Human Behavior

There is predominant evidence that causes of global climate change are anthropogenic: the result of human behavior. However, it is less widely recognized that the solutions are also rooted in human behavior. Effective solutions must draw on a broader understanding of social systems and human behavior (Karen Ehrhardt-Martinez, 2007). Human behavior (B) and his interaction with environment (E) can best be explained by the following equation (Lewin, 1951): B = f (P X E). The equation suggests that behavior is a function of person, environment and interaction between the two. Hence, any change in the environment brings changes in a person’s behavior as well. Albrecht et. al (2007) predicts that physical and mental illness related to the environment will increase dramatically.

Previous global studies have tended to either consider (i) the potential impacts of SLR (e.g. number of people affected) (Hoozemans et al. 1993; Nicholls 2004) or (ii) the economic implications of SLR (e.g. impact and adaptation costs) (Fankhauser 1994; Tol 2004) . A workshop convened on 12 November 2004 at Princeton University was motivated by the notion that environmental policies that target human behavior should incorporate insights about behavioral change and decision-making, topics central in behavioral sciences. The objective of the workshop was to inform climate scientists about the current state of understanding of how people think about global warming and, hopefully, initiate a collaborative dialogue between climate and behavioral scientists. As a part of amended New Delhi work program (2007) to implement article 6 of the convention, under public awareness, a special effort to foster psychological/ behavioral change has been stressed. Keeping in view that most of the work, if not all, on climate change focuses on the technicalities of the subject, this study is purported to address the psychological aspects underlying or concerned with climate change as psychological assessment of human-climate interface, behavioral adaptation and subjective well being of people residing particularly in Indian coastal cities are crucial topics of concern.

2. Methodology

2.1. Study Sites

The study was conducted mainly in four coastal cities of India namely Mumbai, Chennai, Daman and Pondicherry. The coastal metropolitan cities of Mumbai and Chennai have been specifically chosen as the study sites for this purpose since these cities are listed under vulnerable categories and also keeping in view the coastal hazards and vulnerability issues associated with climate change in these cities (TERI, 1996).

2.2. Sampling

Figure 2. Vulnerability to One Meter Sea Level Rise (Source: TERI, 1996)

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Multistage sampling technique was used for selecting the sample from selected coastal cities of India. In the first stage, coastal metropolitan cities of Mumbai and Chennai and small coastal cities of Daman and Pondicherry were purposively selected based on their high vulnerability index to SLR (TERI, 1996) to represent the coastal population of South-East and West zone of India. In the second stage, from the selected cities, institutions (educational, corporate, govt. and others) falling within the distance of 2 km from the coast were listed based on Google earth imagery. Out of the generated list of 45 institutes, using systematic sampling technique 30 institutions were selected for the study. Based on the sampling frame of list of employees/students, 150 adult respondents (Male=74; Female=76 ) were systematically selected for the study. Those residing in the city since less than 2 years were excluded from the sample.

Following table describes the demographic features of the sample:

Table 1. Demographic Profile of Respondents

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2.3. Tools and Techniques

CCPI was especially designed to assess CCA, CSEC, coping/adaptation, IA and CSWB of the sample. CCPI was divided in five sections as above to obtain information on 5 point Likert type rating scale. The section wise details are as below:

Table 2. Reliability of Responses Measuring Climate Variables

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Besides rating scale format, there were some open ended questions too, which were included to obtain qualitative information, thus adopting the multiple response format including nominal, ordinal and ratio scales. Principal Component Analysis (PCA) using SPSS was conducted for factor analysis of CCA, CSEC and coping/adaptation. Based on rotated component matrix, the elicited component factors under the study variables and their Eigen values are tabulated as under:

Table 3. Eigen Values Calculated Through Principal Component Analysis (PCA)

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2.4. Research Design

A correlational research design was adopted to study the inter-relationship between categorical variables (age, gender and distance from coast) and other discrete variables (CCA, CSEC, Coping/Adaptation, IA and CSWB).

3. Results

In the context of coping and adaptation against the impact of climate change, it is immensely important for the coastal habitants to have knowledge and awareness about possible climate change impacts.

Figure 3. Section Wise Scale Average

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3.1. Climate Change Awareness

Level of CCA was assessed using 13 item rating scale as a part of CCPI. On the basis of Principal Component Analysis (PCA), CCA was indicated by two components viz. tacit knowledge and awareness about climate change impacts. Tacit knowledge was significantly higher when the distance from coast of the sample’s residence was less than 2 km (F = 4.16, significant at .017 level). Inter-correlations revealed Negative correlation between distance from coast and tacit knowledge which justifies that the near coast population had higher tacit knowledge about changing climate and its impacts being more vulnerable to the potential adverse impacts of SLR. As the distance from coast was increasing, the tacit knowledge was decreasing as populations were less concerned about the direct effect of SLR or changing climate on their lives. However, the individual coping and subjective well being were not affected by it as being in the coastal city, coping at the individual level is indispensable to some extent.

Significant positive correlation (r = .503) was found between tacit knowledge and emotional anxiety which indicated that the more respondents gain knowledge and awareness regarding climate change, the greater are the chances of their becoming more emotionally anxious about the situation. Some scholars have indicated the need for access to information as a means to enhance people’s climate awareness which in turn help them to adapt(Phillips 2003; Kurita et al. 2006; Collins/Kapucu 2008; Leal Filho 2009; Leal Filho/Mannke 2009; Saroar/Routray 2010). Significant positive correlation was also found between tacit knowledge and IA (r = .289) which meant that role of institutions and their accountability are more recognized by people when their tacit knowledge increases. In other words, people expect the institutions to play more effective roles and also believe in their capabilities of bringing about positive change in dealing with climate change when they become more aware about climate change aspects.

Figure 4. Component Wise Scale Average

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Major impact of SLR was perceived to be loss of life and property (M = 1.85), followed by flooding (M = 1.82) and loss of coastal resources (M = 1.81) on a 3-point rating scale as shown in Figure 5 below:

Figure 5. Major Perceived Impacts of SLR

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The prime perceived factors determining vulnerability of coastal population to coastal disasters were insufficient financial and physical resources to cope effectively (M = 1.56) followed by forced choice to live near the coast (M = 1.46) and inefficient disaster management system (M = 1.42).

Figure 6. Perceived Reasons for the Coastal Population being Vulnerable to Coastal Disasters

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3.2. Climate Stress and Emotional Concern

Climate stress was assessed using 18 item rating scale classified in emotional anxiety, dissatisfaction and resource stress/ climate variability based on PCA. A moderate level of climate induced emotional anxiety and resource stress was reported (M = 2.73 and 2.72).

Figure 7. Perception of Coastal Environment of Cities by the Respondents

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However, dissatisfaction with the luxurious lifestyle of developed nations, industrial and domestic pollution and improper management of coastal zones possibly causing climatic adversities was expressed through frustration and anger (M = 3.16) and the need for more stringent environmental laws was expressed. Significant gender differences were found with females reporting higher emotional anxiety than males (F = 8.81 significant at .003 level). In a study by Sundblad et. al (2007), it was found that cognitive risk judgments (of probability) of serious negative consequences and affective risk judgments (worry) were predicted by knowledge of causes and knowledge of consequences of climate change, in particular health consequences. The study also found that women were more worried than men but did not differ from men with respect to the cognitive risk judgments. About 45% of the respondents reported the coastal environment/condition in their city to be stressful while 35% regarded it as not to be much stressful (See Figure 7).

3.3. Coping and Adaptation

Figure 8. Respondents’ Coping/Adapting Capacities to Climate Change Related Coastal Adversities

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Perceived coping and adaptation was assessed through a 12-item perceived coping scale which was further clustered in Individual coping, Group/institutional coping and general adaptation effort based on factor analysis (PCA). Group/institutional coping was found to be highest (M = 3.27) followed by individual coping (M = 3.23) and adaptation effort (M=2.19). No significant age differences were found in this section.

Females were found to be putting greater adaptation effort in terms of keeping the food stock for adverse coastal scenario managing coastal upheaval by migration or other possible options (F=22.66 significant at .000 level). It is justifiable by the Indian familial system where females play a dominant role in managing the household and family in case of any coastal or other disaster. The significantly positive correlation (r = .406) indicates that with the increased adaptation effort from people, expectation for IA also increases. It is justifiable with the fact that if coastal populations are exerting effort to adapt with the climate variability and associated adversities, it cannot be successful in isolation if local institutional system is not supplementing their efforts by playing a responsible proactive role in macro level adaptation. Only then the adaptive efficacy of the city to changing climate can increase.

Overall, about 5% of sample was fully capable and 17% capable to cope/adapt to climate change. Adaptive response to a large extent depends on three important elements. These are timely recognition of the need to adapt, an incentive to adapt, and ability to adapt (Fankhauser et. al, 1999; Adger et. al, 2005; Perry, 2007). Smit et al., (2001) maintains that the impacts of climate change are not evenly distributed — the people who will be exposed to the worst of the impacts are the ones least able to cope with the associated risks. This implies that the coastal population has to look for effective ways of increasing their coping capacities.

3.4. Institutional Accountability

It was indicated by perceived efficacy of coastal disaster management system and role of government/NGOs and media in creating awareness and environmentally healthy lifestyle. A moderate level of IA was found (M = 2.48). The reason for respondents not acknowledging high accountability of institutions could be because in India coastal planning and management are constrained largely by a lack of information, data and analysis about the interaction between development activities and the coastal environment. India does not have a specific coastal focus for its development policies. It has on the one hand, policies and plans for sectorial development and on the other, environmental policies and legislation to protect the environment from such development. Some of these are incompatible. So coastal development occurs with no or little management. (Noronha et. al 2003). However, the study registered significant gender differences with females reporting higher level of IA than males (F=3.829 significant at .052 level). Around 48.7% of the sample reported moderate level of IA while 36% reported high level of IA in their coastal city.

3.5. Coastal Subjective Well Being

CSWB, indicated by 4 items, was found to be low moderate (M = 2.25). A trend of gender differences was found with females having a slightly better feeling of CSWB than males. While men and women show little comparative difference in average subjective well-being, more women than men live at the extreme ends of well-being scales (Diener et. al 1999). People living near the coast were having a significantly higher CSWB (F= 3.31 significant at .03 level) and as the distance from the coast was increasing, the CSWB was decreasing (r = -.189 significant at .05 level). The reason may be the pleasant landscape of the sea and its surroundings which gives people a sense of contentment and they feel good living near the coast, especially if they have not personally experienced any coastal disaster in the past. It is further enhanced by the IA as if local management system and institutions are responsive and accountable, it further enhances the CSWB (r = .406 significant at .01 level). Similarly adaptation efficacy increases with the increase of distance of settlement from the coast. People staying more distant from the coastline, possess more adaptation efficacy than those who are staying close to the coast (Saroar and Routray,2009). This finding conforms to the existing body of knowledge of resilient coastal community (Adger et. al, 2007).

3.6. Analysis and Interpretation of Regression

Linear regression analysis was calculated to make the predictions of possible relationship between dependent variables of CSWB, Group/institutional coping, individual level coping and adaptation effort and independent variables like CCA and Climate stress. Unstandardized Coefficients (B) suggest that for one unit increase in emotional anxiety, the CSWB will increase by 2.393 units. It means that emotional anxiety to climate change, though appears to be just opposite to well being, can be attributed to higher CSWB in this case. In spite of being emotionally anxious, respondents are likely to have SWB as an individual defense mechanism to curb the negativity associated with anxiety. On the other hand, for one unit increase in Resource stress and climate variability, SWB will decrease by 0.311. This means adverse impact of climate variability in terms of increasing temperature and humidity, coastal risk and resource stress specifically associated with water shortage can be attributed to reduced level of coastal SWB. However, IA accounts for the increase of 0.392 per unit to coastal SWB. The responsive attitude of coastal management institutions and media contributes to positive increase in SWB.

Table 4. Multiple Regression Model of Effects of Factors on Dependent Variables

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