Water temperature is identified as an important factor that influences the physical and chemical properties of water. The temperature of water influences the level of diseases occurred in water sources. A water temperature of 15°C or less is an acceptable level. A high degree of temperature leads to chemical reactions like dissolving of minerals from rocks or having higher electrical conductivity (Kale, 2016). Discharge of pollutants in water streams is the main contributor to high water temperature. The aim of this study is to examine the impact of Foreign Direct Investment (FDI) inflows on environmental pollution in India.
Studies by Naresh & Shaik (2017), Pandey, Raghuvanshi, & Shukla (2014) have shown that there has been variation in the water temperature of major Indian rivers due to water pollution. For instance, in Kanpur, the Ganga river is mostly polluted by industrial effluents, domestic sewage, tannery effluents, and cremation of dead bodies. These pollutants influenced the water temperature so much that the average water temperature for Kanpur Ghats was 26-27 °C in summer, 17-18°C in winter, and 24-26°C in spring (Naseema, Masihur, & Husain, 2013).
The figure below depicts the mean value of the water temperature of 15 major Indian rivers for the time period 2002-2017. It shows that the average temperature is much higher than the optimal level required. The water temperature has decreased with time, from above 25°C in 2002 to below 24°C in 2017. There have been various fluctuations and in 2006, the water temperature reached a minimum of approx. 23°C.
A number of factors are responsible for the increase in the water temperature of Indian rivers. This article presents the findings with respect to water temperature. For this purpose, data from 15 major Indian rivers were collected for the time period 2007-2017. The data on water pollution indicators were obtained from the official websites of the National Water Mission and Ministry of Statistics and Programme Implementation. STATA software was used for analysis.
Impact of FDI inflows on water temperature in the rivers during 2007-2017
The first step in a time series analysis is to check the data for variability. According to Lütkepohl & Xu, (2009), any dataset should be stable with minimum variation. The variability in this dataset was found to be high. Therefore using natural log transformation in MS Excel, the dataset was first stabilized. This stabilized dataset was then used for further analysis.
In order to test the impact of FDI inflows on water temperature in Indian rivers, the following hypothesis was framed.
H0: There is no significant impact of FDI inflows on water temperature in Indian rivers.
HA: There is a significant impact of FDI Inflows on water temperature in Indian rivers.
Step 1: Pre-condition tests
The dataset should first be tested for stationarity, normality, and cointegration of the variables. Augmented Dickey-Fuller (ADF) test, Johansen cointegration test, and Shapiro-Wilk tests were applied respectively in STATA software.
Stationarity is the property of the time series that ensures the mean and variance value of the variable is constant. This assumption helps in determining the relationship reliably and forecasting (Adhikari, 2013; Gujarati & Porter, 2009; Nason, 2018). The below table represents the results of the ADF test for stationarity.
|Variable||Test-Statistic||5% Critical Value||p-value|
|LnFDI with drift||-1.511||-1.771||0.0774|
|LnFDI with trend||-1.259||-3.600||0.8976|
For Lntemp, the absolute test-statistic is greater than the critical value i.e. 4.905 > 3.000. The p-value is also less than the significance level i.e. 0.0000 < 0.05. Thus, the null hypothesis of data being non-stationary is rejected and the natural-log transformation of water temperature is stationary. Furthermore, for LnFDI initially the absolute test-statistic is less than the critical value and p-value is also greater than the significance level. Thus, the further model is considered in case of drift and trend to test the stationarity in the presence of inte