# Impact of FDI inflows on water temperature in Indian rivers

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.

H

_{0}: There is no significant impact ofFDIinflows on water temperature in Indian rivers.H

_{A}: There is a significant impact ofFDIInflows 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 |

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

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 |

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.

Wherein,

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 |

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

LnTemp | Coefficient | t-value | p-value | R^{2} value | Adjusted R^{2} value |

Difflnfdi | -.0459771 | -2.51 | 0.026 | 0.3272 | 0.2755 |

Constant | 3.200333 | 453.47 | 0.000 |

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

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 | D_{L} | D_{U} | 4-D_{U} | 4-D_{L} |
---|---|---|---|---|

2.147329 | 0.946 | 1.543 | 2.457 | 3.054 |

As the D-statistic is between D_{U} and 4-D_{U} 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

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 |

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. R^{2} 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 R^{2} 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.

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.

#### References

- Adhikari K., R., & R.K., A. (2013).
*An Introductory Study on Time Series Modeling and Forecasting Ratnadip Adhikari R. K. Agrawal*. https://doi.org/10.1210/jc.2006-1327 - 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. https://doi.org/10.1111/ceo.12358 - 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. https://doi.org/10.3390/w11030452 - 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. https://doi.org/10.17148/IARJSET.2016.3834 - 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. https://doi.org/10.1144/iavcei001.11 - 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. https://doi.org/10.4135/9781412952644.n201 - Seltman, H. J. (2018).
*Experimental Design and Analysis*.

- Conditional CAPM analysis for stock-based investing - February 20, 2021
- Short term momentum analysis of growth, income and value stocks - February 9, 2021
- Inferential analysis to compare the performance of stocks listed in the BSE (2011-2020) - January 30, 2021

## Discuss