Would Private Sector be Inclined to Take up Initiatives to Address Water Crisis in India?

Water Situation in India

Thus a combined impact of population rise, socio-economic growth, urbanization, and climate change pose challenges towards sustainable water management in India (Jain, 2012). A balanced demand and supply in domestic water use, and adequate availability of water for agriculture, industry, and energy production would be required to overcome these challenges. (Halady & Rao, 2010)

In the coming years, India will be highly vulnerable to the impacts of the rising sea level, rising global temperature, floods and droughts, water pollution, and the associated health hazards. Sea level along the Indian coastline has been steadily rising at the rate of 1.06–1.25 mm/year (MOEF, 2010). By 2100, there may be about 50 cm rise in the sea level, causing a displacement threat to the large number of people living along the 7,517 km coastline.

The rising global temperature is likely to

reduce the size of glaciers in the Himalayan range, severely affecting water flow in the northern river basins in India;

The major Indian rivers get their water from the melting of snow and glacier during the summer season. If the trend of rising temperature continues, the river flow in the Himalayan basins may initially increase. However, with the depletion of snow reservoir, the variability of downstream run-off will increase, subsequently reducing the water flow. This will seriously affect the livelihood of people engaged in fisheries and agriculture in the river basin (Wilk & Wittgren, 2009). To address such water-related threats, coping mechanisms in the form of mitigation as well as adaptation strategies are urgently needed. The mitigation aspects of water-related strategies would refer to reducing the generation of greenhouse gases so as not to increase the global warming (which is one of the main causes of water-related calamities), facilitate judicious use of fresh water, control water demand and improve efficiency, work against contamination of ground water and fresh water bodies, so as to prevent disease and so on. All the same, in view of many water-related calamities which have already destroyed communities, such as Chennai floods in November 2016, we focus here on adaptation strategies (Potarazu, 2015)

Adaptation Initiatives to Water Challenges in India

The human race has to often cope with various kinds of natural hazards such as floods, droughts, forest fires, earthquakes, and spread of diseases. This coping with nature is looked upon as adaptation of the entire society to the natural phenomena. Adaptation to water-related problems refers to changes in the ways society uses water (Jain, 2012). Adaptation strategies have taken different forms: such as seasonal migration of communities in search of water in the face of drought, agricultural population inventing techniques for storing water, relocation of agriculture and irrigation facilities, constructing embankments and dikes, and even lowering lakes to protect cities, agricultural land, and populations from flooding (Wilk & Wittgren, 2009).

Adaptation in the context of water-related concerns should be able to primarily improve resilience of people to the unavoidable variability of water availability in the country. Adaptation strategies should, therefore, include:

Increasing the water supply and improving the ecosystems:

This would involve (a) expansion of rainwater harvesting and improvement of rain-fed cultivation and ground water recharge, (b) adoption of water transfer schemes, (c) restoration of aquatic habitats and ecosystems, (d) building of reservoirs to increase storage capacity, (e) encouraging the use of traditional water harvesting systems to supplement household water supply and irrigation practices.

In addition to the above, there are many other freshwater adaptation measures such as conjunctive management of groundwater, tank restoration, etc. (Amarasinghe, Shah, & McCornick, 2008; Gupta & Deshpande, 2004; Pittock, 2011). Since private sector has provided leadership for implementing certain well-defined projects, they may be roped in to take up this challenge and achieve the identified targets.

Global Initiatives for Shared Water Management

Most business operations either depend on water resources or have significant impacts on them. Some businesses use water for cooling and cleaning; others use water as a central ingredient in the production or for consumption of the product sold by them (World Business Council on Sustainable Development, 2005). Therefore, the future of business depends on the sustainability of water resources which are increasingly coming under pressure. Globally, per capita availability of water is steadily decreasing. This trend is going to continue with the growth in world population, increased water consumption levels of the emerging economies, and climate change becoming a reality (World Business Council on Sustainable Development, 2005).

In recent years, many business initiatives have been developed across the world to support sustainable water management. In fact, the public–private initiative ‘CEO Water Mandate’ was launched at the 2007 UN Global Leadership Forum (CEO Water Mandate, 2007). Some of the world’s largest corporations urged their business peers to take immediate action to address the emerging global water crisis. The project is designed to help companies better manage water use in their direct operations and through their supply chains. More specifically, the endorsers of the initiative pledge to set water-use targets, assist suppliers with water-efficiency practices, and partner with governments, policy makers and community groups to address water shortages and sanitation, and to share experiences with the ultimate aim of advancing best practices in the field (UN Global Compact, 2008). These initiatives are to be taken up totally on a voluntary basis.

Another initiative called the ‘Water Initiative’ has been launched in 2003 by the World Economic Forum (2008). This programme is intended to promote public–private partnerships on water projects and responsible management of watersheds. The aim is to create multi-stakeholder networks, comprising businesses, NGOs, international organizations, and governments that facilitate cooperation on water projects that are well developed, bankable, with appropriate leadership and financing plans. The initiative has focused on creating water partnerships in India and South Africa (Morrison, Morikawa, Murphy, & Schulte, 2009).

Besides these initiatives, the World Business Council on Sustainable Development (WBCSD) has also encouraged corporations to be concerned about water issues (Moss, Wolff, Gladden, & Guttieriez, 2003; World Business Council on Sustainable Development, 2005). It has introduced the ‘Global Water Tool’ with an emphasis on reporting and risk assessment in the context of water issues (Lambooy, 2011). Guidelines on voluntary environmental reporting are, amongst others, offered by the Global Reporting Initiative (GRI), which has pioneered the development of the world’s most widely used sustainability reporting framework. This framework sets out the principles and performance indicators that organizations can use to measure and report on their economic, environmental, and social performance (GRI, 2008).

Private sector participation in water management has been explored in China’s water market (Choi, Chung, & Lee, 2010), Portuguese water sector (Silvestre, 2012), in Malaysia (Tan, 2012), and Indonesia (Bakker, 2007). In all these studies, the authors have established the tremendous contribution of the private sector towards water management.

Voluntary Participation of Indian Industry in Sustainable Water Management

In India, the industry initiatives in the context of water management have been in the areas of (a) policy formulation, (b) information dissemination, water audits, and water awareness, and (c) water projects which involve promoting private sector participation and watershed management. In this context, the initiatives carried out by many Indian companies in the realm of water efficiency are broadly in the following areas (India Water Portal, n.d.):

Improving cooling tower operations

The Confederation of Indian Industries (CII) partnered with the Global Water for Sustainability (GLOWS) to build local capacity for Indian industry/corporate through Public–Private Community Partnership (PPCP) projects in water management (PPCP Guidebook on Water, n.d.). Indian Business Alliance on Water (IBAW), formed by the World Economic Forum (WEF) & CII-Godrej GBC in 2006, has facilitated PPCP projects in water for information dissemination through seminars and other awareness programmes in schools and other organizations. The Green Building Movement has also been started to optimize the water use in various buildings across the country (CII, 2009; Iyer & Dasgupta, 2011).

Thus, there are many initiatives that have been successfully implemented in India in the context of water management. However, the challenge is huge and full industry participation in this endeavour is still awaited. Also, the awareness of water concerns is still not widespread in this country. It is expected that as the awareness level grows, especially amongst the private sector, their propensity to commit and encourage their organizations towards sustainable water management will become a reality.

To determine if such propensity exists in the private sector and also to determine if awareness of water concerns exists, an empirical academic research was undertaken.

Objectives

The objectives of the empirical research were to:

(1) assess the awareness levels of Indian private sector about the water-related concerns;

Constructs and Associated Indicator Variables

Prior to empirical research, it was necessary to specify the constructs and associated indicators which could be used to measure the level of awareness of the private sector on the existence and impact of water crisis and assess how it can help in addressing this situation. Based on published research on sustainable water management in India (Grail Research, 2009; Iyer & Dasgupta 2011; Jain, 2012; Thakkar, 2012;) and related literature on water-related issues across the world (Kabat & Schaik, 2003; Pittock, 2011; Wilk & Wittgren, 2009), the following constructs and indicators were identified:

Conceptual Framework

To meet the research objectives, the following hypotheses were developed:

Hypothesis 1: Awareness of the existence of water-related challenges would encourage private sector to take up voluntary initiatives, both individual and organizational, towards enabling communities to adapt to water challenges.

Using the constructs as defined above, the conceptual framework for the two hypotheses can be presented as shown in Figures 1 and 2.

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Figure 1:

Source: Authors’ conceptualization.

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Figure 2:

Source: Authors’ conceptualization.

Exploratory Analysis

For analysing the data, first, the mean, standard errors, and associated t-values were determined for each construct. These measures were used to test the significance of each construct corresponding to one-tailed t-test with null hypotheses and alternative hypotheses such as:

H0: Mean < = 2.5 (median of the 4-point scale used in the survey questionnaire)

H1: Mean > 2.5 (median of the 4-point scale)

If the t-value associated with the test is greater than 1.645, the construct is considered significant.

Thus, if Construct 1 has t-value greater than 1.645, it can be inferred that awareness of the existence of water shortage is significant among managers in India.

Confirmatory Analysis

The linkages between the constructs were determined by structural equation modelling (SEM), where a series of separate but interdependent multiple regression equations measured the causal relationships amongst the constructs. In SEM, there is a unique feature of being able to include variables that are not directly measurable and are thus called unobserved or latent constructs (Joreskog & Sorbom, 1993). The observed variables, gathered from field surveys, are referred to as manifest or indicator variables and are used to measure the latent constructs.

The significance of the overall model was given by the Chi-square value, associated degrees of freedom, and overall model p-value which must be greater than 0.05 for accepting the model. The individual links were determined in terms of individual critical ratios that should be more than 1.96 or less than −1.96. The critical ratios have individual p-values which must be less than 0.05 to be considered significant at 5 per cent level of significance.

In addition to the Chi-square tests and the associated p-values, various other descriptive measures were considered in assessing the overall model performance: Comparative Fit Index (CFI), Goodness-of-fit Index (GFI), Adjusted Fit Index (AGFI), and Normed Fit Index (NFI), etc. All these indicators should be close to 1 for better model fit.

Research Instrument: The Survey Questionnaire

For empirical research, a survey questionnaire was used to measure 11 constructs presented earlier. The individual variables or manifest variables formed the different items in the questionnaire (Jain, 2012). The items under awareness constructs C1–C9 were measured on a 4-point Likert scale:

1= not aware 2 = little aware

The items under the constructs measuring propensity of private sector to take up individual as well as organizational initiatives, C10 and C11, were also measured on a 4-point Likert scale:

1= not keen 2= little keen

In addition, demographic features of the respondents, years of operation of the company the respondent was working for, and the size of the company (in terms of the total number of employees) were obtained.

Sampling Frame and Data Collection

The sampling frame comprised Indian managers working in different organizations in the private sector—professional managers who were Indian alumni of the Asian Institute of Management, Philippines, the Great Lakes Institute of Management, Chennai, and the Indian Institute of Management, Ahmedabad. Data were collected through non-probability purposive sampling. The questionnaire was digitalized and the link sent to a population of junior to middle managers to directors at different organizations in the country.

The final sample size was 166, the associated margin of error being less than 8 per cent. The data collection was carried out during the second half of 2013. Based on the responses obtained, a database was set up and analysed using the methodology explained above.

Some indices of sample profile are given in Figures 3(a) and 3(b):

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Sample Profile by Age Group

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Figure 3(b):

Source: Authors’ analysis.

Exploratory Analysis

An analysis of the database reveals significant awareness on the part of practising managers about the existence of impending water shortage situation in the country and also about the spread of disease due to water crisis. However, awareness about spread of disease due to water crisis was not significant (Table 1).

Further, there is significant awareness that private sector can help in recycling and treatment of waste water as well as water conservation. However, there is no significant awareness about private sector being able to help spread awareness, enabling communities to address water shortage, helping in infrastructure building for agriculture and irrigation and building sanitation facilities (Table 2).

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Table 1:

Awareness of the Existence and Impacts of Water Crisis Situation in India

Construct	Mean	t-value	Significance
Construct 1: Awareness of existence of water shortage situation in India	3.1069	10.891	Significant
Construct 2: Awareness of stress and other impacts due to water shortage	2.1868	−4.709	Not significant
Construct 3: Awareness of spread of disease	2.8176	4.3214	Significant

Source: Authors’ analysis.

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Table 2:

Awareness of the Potential of Industry/Private Sector to Address Water-related Challenges

Construct	Mean	t-value	Significance
Construct 4: Awareness that private sector can help in recycling and treatment of waste water	2.8491	4.217	Significant
Construct 5: Awareness that private sector can help in water conservation and watershed development	2.6415	1.668	Significant
Construct 6: Awareness that private sector can help spread awareness	2.4119	−1.024	Not significant
Construct 7: Awareness that private sector can organize initiatives to enable communities addressing water shortage	2.4434	−0.679	Not significant
Construct 8: Awareness that private sector can organize initiatives to help in agriculture and irrigation infrastructure,	2.3349	−1.98	Not significant
Construct 9: Awareness that private sector can organize initiatives to build water treatment plants and sanitation facilities.	2.4858	−0.165	Not significant

Source: Authors’ analysis.

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Table 3:

Propensity of the Private Sector to Take Up Initiatives

Construct 10: Encourage organization to take up initiatives	2.7736	3.067	Significant
Construct 11: Take up voluntary initiative individually	2.7311	2.419	Significant

Source: Authors’ analysis.

Thus, the private sector has significant propensity to take up voluntary individual initiatives and also to encourage organizations to take up initiatives on water related issues (Table 3).

Out of 11 constructs considered, only 6 constructs, C1, C3, C4, C5, C10, and C11 are found to be significant.

Confirmatory Research with Structural Equation Modelling

Having formulated the two hypotheses, structural equation modelling was applied to check if the data supported the proposed models.

Structural Equation Modelling (SEM)

This is an analytical approach to study the linkages between unobserved variables or latent variables/constructs and observed variables or manifest/indicator variables which constitute the unobserved variables. The measurement model connects the unobserved variable to the observed variable. The structural model connects the unobserved variables to the unobserved variables.

SEM estimates a series of separate but interdependent multiple regression equations simultaneously. Linear SEM approach is used to validate the causal relationships between the different latent constructs (Joreskog & Sorbom, 1993). Generally, the fit criteria of a structural equation model indicate to what extent the specified model fits the empirical data. Only one GFI measure, that is, the c2 test statistic, has an associated significance test, while all other measures, such as GFI, CFI, AGFI, and NFI, are descriptive.

For the chi-square GFI test of the model as a whole, the chi-square value should not be significant if there is a good model fit: the higher the chi-square, the more the difference between model-estimated and actual covariance matrices, and hence the worse would be the model fit. If the p-value associated with the c2 value is larger than 0.05, the null hypothesis is accepted and the model is regarded as compatible with the population covariance matrix S. In this case, the test states that the model fits the data. For a good model fit, the ratio c2/df should be as small as possible.

As there exist no absolute standards, Chi square/ degrees of freedom is less than 2.

Critical ratio: After the model is run, each arrow has a regression coefficient. This coefficient divided by its standard error is called critical ratio. This ratio is very much like the t-value but applies even when the normality assumption does not hold.

Consider any arrow/link from one variable to another. For any regression coefficient b, the null hypothesis is H0: β =0, H1: β =\= 0.

Under 5 per cent level of significance, if critical ratio is more than 1.96, the link is considered significant.

Results for SEM on Hypothesis 1

In this model, the significant constructs, C1, C2, and C3 were considered and their impacts investigated on C10 and C11. The overall measure of the model fit, Chi-square/degrees of freedom, overall p-value, GFI, CFI, AGFI, and NFI are given below.

Chi-square/ degrees of freedom = 1.304 ( good fit comprises < 2)

Overall p-value = 0.071 ( good fit comprises p-value > 0.05)

GFI = 0.919 ( acceptable fit > 0.90)

CFI = 0.982 ( good fit > 0.97)

NFI = 0.927 ( acceptable fit > 0.90)

AGFI = 0.855 ( acceptable fit > 0.85)

The regression estimates of the links between the different constructs, their critical ratios and individual p-values are given in Table 4.

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Table 4:

Regression Estimates

			Regression Estimate	S.E.	C.R.	p-value
C10 organization	<—	C1 Water shortage	0.025	0.671	0.037	0.971
C11 individual	<—	C1 Water shortage	1.759	1.173	1.499	0.134
C10 organization	<—	C2 Impacts	0.269	0.531	0.507	0.612
C10 organization	<—	C3 Disease	0.129	0.222	0.582	0.561
C11 individual	<—	C3 Disease	0.705	0.336	2.095	0.036
C11 individual	<—	C2 Impacts	-0.995	0.877	-1.134	0.257

Source: Authors’ analysis.

From the regression estimates on the individual links, one observes that most of the links considered are not significant though the overall model fit is significant and acceptable (Arbuckle, 1997). Awareness of spread of disease was the only construct which led to individual initiatives. Thus, the current analysis confirms that as awareness about spread of disease increases, the propensity to individual initiative will increase.

Next a model for Hypothesis 2 was run, using C4, C5, C6, C7, C8 and C9 as independent variables and C10 and C11 as dependent variables (Figure 2).

However, this model did not converge perhaps because C6, C7, C8, and C9 had not emerged as significant in the exploratory analysis.

SEM Combined Results for Hypotheses 1 and 2

In this analysis, first, a model was proposed considering all the constructs C1–C9 and their causal impacts evaluated on C10 and C11. Using the conceptual framework as given above, with Hypothesis 1 and Hypothesis 2 combined, several runs for structural equation models were taken. The final model did not converge under this scenario.

Thereafter, only the significant constructs as obtained from the exploratory analysis were considered. This time the final model converged with C1, C3, C4, C5, C10, and C11 remaining in the model. The following indicators give the measures of GFI:

Chi-square/ degrees of freedom = 1.152 < 2 implying significant model fit

Overall p-value indicating goodness of fit for the final model = 0.113 (good fit > 0.05).

GFI = 0.885 (acceptable fit > 0.90)

NFI= 0.931 ( acceptable fit > 0.90)

CFI = 0.990 ( good fit > 0.97)

AGFI = 0.885 (acceptable fit > 0.85)

From the results on regression estimates of the different links, considering only the significant constructs, the final combined model for Hypotheses1 and 2, emerged (Figure 4).

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Figure 4:

Source: Authors’ conceptualization.