Impact of FDI inflows on water temperature in Indian rivers

By Riya Jain & Priya Chetty on October 24, 2019

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.

Figure 1: Average water temperature of Indian rivers for the period of 2002-2017

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.

a) Stationarity

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
Lntemp -4.905            -3.000            0.0000
LnFDI -1.511            -3.000            0.5283
LnFDI with drift -1.511            -1.771            0.0774
LnFDI with trend -1.259            -3.600            0.8976
DiffLnFDI -3.584        -3.000            0.0061

Table 1: ADF results

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 intercept and deterministic trend (Gujarati & Porter, 2009). However, still the null hypothesis is not rejected and the FDI inflows are non-stationary. Thus, the first-order differentiation for the variable was done. DifflnFDI series is generated which represents the stationary form of FDI inflows.

In the case of DifflnFDI, stationarity is derived as the absolute value of test statistic is greater than critical value and the p-value is also small i.e. 3.584 > 3.000 and 0.0061 < 0.05. Thus, DifflnFDI is a stationary variable.

b) Cointegration

Cointegration test studies the nature of the relationship between the variables. It determines whether there is any relationship or linkage between the variables or not. The existence of cointegration between the variables ensures that there is a linkage between the variables and no spurious or non-sense relationship is studied (Gujarati & Porter, 2009). Johansen’s cointegration results for testing the long-run relationship between FDI inflows and water temperature of Indian rivers are given below.

Max. Ranks Trace Statistic 5% Critical Value Max Statistic 5% Critical Value
0 20.3730    15.41 17.6082    14.07
1 2.7649* 3.76 2.7649*   3.76

*represent significant at 5%level

Table 2: Johansen cointegration test results

The null hypothesis of no cointegration is rejected for 0 ranks as the values are greater than critical value i.e. 20.3730 > 15.41, and 17.6082 > 14.07. Furthermore, for 1 rank the null hypothesis is not rejected i.e. 2.7649 < 3.76.

Hence, at most 1 cointegrating vector exists between FDI inflows and water temperature Kumar & Chander, (2016). Thus, the results of trace statistic and max statistic shown in Table 2 depicts that long-run and short-run relationship exists between FDI inflows and water temperature.

c) Normality

A dataset is said to be normally distributed if the values are symmetrically distributed. The existence of normality in the value of the variables is the assumption of the classical linear regression (Casson & Farmer, 2014). Thus, to depict the impact of FDI inflows on water temperature, the normality of the data is tested. Shapiro-Wilk test was used and the result of the test is represented below.

Variable P-value
Lntemp 0.21856
Difflnfdi 0.50621

Table 3: Shapiro-Wilk test results

The p-value for lntemp and Difflnfdi is greater than the significance level of the study i.e. 0.21856 and 0.50621 > 0.05. Therefore, the null hypothesis of the normal distribution is not rejected and both variables are normally distributed. Hence, the regression test can be performed.

Step 2: Regression

The stationary, cointegrated, and normally distributed variables were used to form the regression model.


Variables Nature of variable Description
  Dependent Stationary form of Natural Log-transformation for average water temperature of Indian rivers at the t-time period
Independent Stationary form of Natural log-transformation for net FDI Inflows at the t-time period
Coefficients Intercept, Slope Coefficient
Error Term Residual
t   Time

Table 4: Variables used for regression

The results for the regression of the above model is shown below

LnTemp Coefficient t-value p-value R2 value Adjusted R2 value
Difflnfdi -.0459771   -2.51   0.026    0.3272 0.2755
Constant 3.200333   453.47   0.000        

Table 5: Regression results

The results of the regression model show an R2 value of 0.3272 and an adjusted R2 value of 0.2755. However, it is important to test the heteroscedasticity and autocorrelation presence in the model (Casson & Farmer, 2014) to derive unbiased and efficient results.

Step 3: Diagnostic tests


Autocorrelation means the existence of interdependence between the error terms. The presence of autocorrelation in the model lead to decreasing the validity and precision of the results of the model. No autocorrelation is also the assumption of classical linear regression (Huitema & Laraway, 2006). Durbin Watson test determines whether the residuals are interrelated. The result is presented below.

The Durbin Watson test for the above model has shown the following result.

D-statistic DL DU 4-DU 4-DL
2.147329 0.946 1.543 2.457 3.054

Table 6: Durbin-Watson result

As the D-statistic is between DU and 4-DU i.e. 1.543<2.147329<2.457, thus there is no autocorrelation present in the model. Okumoko, Akarara, & Opuofoni, (2018) stated that the value of the Durbin Watson statistic should be close to 2 to derive reliable results. As the value of D-statistic is close to 2, thus, the residuals are not related to each other, which makes the results of the regression reliable.


Heteroscedasticity refers to the difference in the variance value for the predicted values of the regression. As classical linear regression stated that the model should be homoscedastic for deriving effective results, thus heteroscedasticity presence is tested (Casson & Farmer, 2014; Salkind, 2007). Bartlett’s periodogram based white noise heteroscedasticity test results are represented in the below table.

Bartlett’s (B)- Statistic P-value
0.5384 0.9340

Table 7: Heteroscedasticity test results

The p-value of the test is greater than the significance level of the study i.e. 0.9340 > 0.05, thus the null hypothesis of no heteroscedasticity is not rejected. Kostakis et al., (2016) in their study stated that the model needs to be homoscedastic. Herein, Table 7 represents a high p-value, thus, the dataset for FDI inflows and water temperature is homoscedastic, making the result acceptable.

The positive impact of FDI inflows on water temperature

The data is free from the problem of heteroscedasticity and autocorrelation. R2 value shows that about 32% of the variation in water temperature is represented by the FDI inflows. Faraway, (2014) stated that the value above 0 of R2 shows that the model correctly links the variables and a value close to 0.5 or 0.6 depicts a good model. The value shown in Table 5 is above 0 but is less than 0.5. The p-value presented for Difflnfdi is 0.026 < 0.05, the significance level. Thus, there is no significant impact of FDI inflows on water pollution is the null hypothesis is rejected.

Coefficient value shows that with a 1% increase in FDI inflows, water temperature decreases by 0.0459771% i.e. increase in FDI inflows in India is beneficial for the environment.

Figure 2: Impact of FDI inflows on the water temperature of Indian Rivers

The above graph shows that there is a negative relationship between FDI Inflows and water temperature as the fitted line is downward sloping. Furthermore, the scatter plot of temperature is close to the fitted line thus depicting that there is a significant impact of FDI Inflows on temperature. Hence, water temperature tends to decrease with increasing FDI inflows.

Studies supporting findings of FDI inflows’ impact on water pollution

A study by Ding, Tang, & He, (2019) supported the existence of a negative relationship between FDI inflows and water pollution by stating that FDI inflows increase water utilization efficiency. However, the intensity of the effect of FDI inflows on water pollution is not very high. He further added that the existence of environmental regulation is the major reason for this relationship. Apart from environmental regulation, technological upgradations, and innovation to contribute to improving the efficiency of water treatment technologies which in turn increase the utilization efficiency of water. Hence, growth does not always contribute to the degradation of the environment. With the enhancement in technological innovations and environmental regulations, a positive relationship between growth and environment could be seen.

The analysis suggests that an increase in FDI inflows in India for the period of 2002-2017 lead to an improvement in water efficiency. With the presence of environmental regulations, more technological upgradations, and other factors, FDI inflows contributed to reducing the water pollution level in the country by reducing the water temperature level. Though still, the water temperature level is very high i.e. close to 24°C as compared to an optimal level of 15°C.


  • Adhikari K., R., & R.K., A. (2013). An Introductory Study on Time Series Modeling and Forecasting Ratnadip Adhikari R. K. Agrawal.
  • Casson, R. J., & Farmer, L. D. M. (2014). Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clinical and Experimental Ophthalmology, 42(6), 590–596.
  • Ding, X., Tang, N., & He, J. (2019). The threshold effect of environmental regulation, FDI agglomeration, and water utilization efficiency under “double control actions”-An empirical test based on Yangtze River Economic Belt. Water (Switzerland), 11(3), 1–13.
  • Faraway, J. J. (2014). Texts in Statistical Science Series, Linear Models with R (M. Tanner, J. Zidek, & C. Chatfield, eds.). Taylor & Francis.
  • Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). In Basic Econometrics.
  • Huitema, B., & Laraway, S. (2006). Autocorrelation. Encyclopedia of Measurement and Statistics.
  • Kale, V. (2016). Consequence of Temperature, pH, Turbidity and Dissolved Oxygen Water Quality Parameters. International Advanced Research Journal in Science, Engineering and Technology ISO, 3297(8), 186–190.
  • Kostakis, Sardianou, S. and, Lolos, I. and, & Eleni. (2016). Foreign direct investment and environmental degradation : Further evidence from Brazil and Singapore . MPRA, (75643).
  • Kumar, V., & Chander, R. (2016). Foreign Direct Investment And Air Pollution : Granger Causality Analysis. IOSR Journal of Business and Management (IOSR-JBM), 12–17.
  • Naresh, A., & Shaik, R. (2017). Modeling Stream Water Temperature using Regression Analysis with Air Temperature and Streamflow over Krishna River. International Journal of Engineering Technology Science and Research, 4(11), 2394 – 3386.
  • Naseema, K., Masihur, R., & Husain, K. A. (2013). Study of seasonal variation in the water quality among different ghats of river Ganga, Kanpur, India. Journal of Environmental Research And Development, 8(1).
  • Nason, G. P. (2018). Stationary and non-stationary time series. Statistics in Volcanology, (1994), 129–142.
  • Okumoko, T. P., Akarara, E. A., & Opuofoni, C. A. (2018). Impact of Foreign Direct Investment on Economic Growth in Nigeria. International Journal of Humanities and Social Science, 8(1).
  • Pandey, R., Raghuvanshi, D., & Shukla, D. N. (2014). Water quality of river Ganga along ghats in Allahabad city, U. P., India. Advances in Applied Science Research, 5(4), 181–186.
  • Salkind, N. (2007). Heteroscedasticity and Homoscedasticity. Encyclopedia of Measurement and Statistics, 8–10.
  • Seltman, H. J. (2018). Experimental Design and Analysis.

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

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I am a master's in Economics from Amity University. Having a keen interest in Econometrics and data analysis, I was a part of the Innovation Project of Daulat Ram College, Delhi University. My core expertise and interest are in environment-related issues. Apart from academics, I love music and exploring new places.