able.
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|
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.
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. 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/10.33