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rease the level of imports of coal, imported coal is the explanatory (independent) variables and response

used majorly for blending and comprises a small portion (dependent) variables. The days of stock of coal with

of days of coal stock. Similarly, rising overdues to gencos power plants in India is the variable of interest and forms

and subsequently to coal suppliers may not have led our dependent variable. The despatch volumes of coal,

to curtailment of coal supplies as long as the gencos the volume of imported coal, overdues to gencos and the

managed to meet their working capital requirements demand of power forms our independent variables. The

through debt or other means. There is also a slew of variables are normalized prior to the regression analysis,

nationalised players of disproportionate size in both so that the mean of each of the resulting normalized

power generation and coal mining and marketing areas. datasets of the corresponding variables is 0 and the

In the subsequent section, we attempt to study the standard deviation is 1. Thus, the regression equation we

relationship between the overall level of coal stock in attempt to solve for is as follows:

thermal power plants across India and the indicators for

each of the reasons listed in the section above. Stock = β0 + β1 Demand + β2 Despatch + β3 Import +

β4 OD + e

METHODOLOGY

where,

To study the eﬀect of the various reasons listed in Stock = Normalized Days of Coal Stock at Power

the previous section, the data of days of coal stock is Plants across India at the end of the month

collected from National Power Portal (NPP). The coal

stock available across power plants in India at the end Demand = Normalized Power Demand for the

of the respective months is taken as the coal stock for month in Million Units

the month. The power demand data (in Units, kWh) is

extracted from the website of Central Electricity Authority Despatch = Normalized Monthly Volume of Coal

(CEA). Data of despatch of coal by Coal India Limited to despatched by Coal India Limited and other captive

the power sector, and volume of coal imports has also mines to power sector

been taken from the website of Ministry of Coal. The data

has been collected for the months of the pandemic and Import = Normalized Monthly Volume of Imported

disruption of economic activity, starting from April 2020 Coal by power sector

to October 2021. As a proxy of overdues to Coal India

from the power sector, data for overdues from discoms OD = Normalized Total outstanding dues to

to gencos has been considered. This data for total generating companies from distribution companies

overdues to gencos has been collected from the PRAAPTI at the end of the month

(Payment Ratiﬁcation And Analysis in Ministry of Power,

Government of India, Power procurement for bringing β0 = Constant term in the model

Transparency in Invoicing of generators) database. βi’s = Slope coeﬃcients of each explanatory variable,

and

To estimate the dependency of the coal stock with each

of the other factors, we use linear regression with multiple e = Error term of the model, also called the residuals

variables. Multiple linear regression (MLR), also known

simply as multiple regression, is a statistical technique A multiple linear regression works on the principle

that uses several explanatory variables to predict the that for the best ﬁt model, the sum of squared errors is

outcome of a response variable. The goal of multiple linear minimum. Statistical MLR software and add-ins helps ﬁnd

regression is to model the linear relationship between all of the ’s in the multiple regression. In these models, the

null hypothesis is that the βi’s = 0, which is to say that the

corresponding independent variable does not inﬂuence

the dependent variable. If the p-value of corresponding

variable in the regression output is less than the level

of signiﬁcance, we can reject the null hypothesis and

conclude that the independent variable is signiﬁcant in

17

used majorly for blending and comprises a small portion (dependent) variables. The days of stock of coal with

of days of coal stock. Similarly, rising overdues to gencos power plants in India is the variable of interest and forms

and subsequently to coal suppliers may not have led our dependent variable. The despatch volumes of coal,

to curtailment of coal supplies as long as the gencos the volume of imported coal, overdues to gencos and the

managed to meet their working capital requirements demand of power forms our independent variables. The

through debt or other means. There is also a slew of variables are normalized prior to the regression analysis,

nationalised players of disproportionate size in both so that the mean of each of the resulting normalized

power generation and coal mining and marketing areas. datasets of the corresponding variables is 0 and the

In the subsequent section, we attempt to study the standard deviation is 1. Thus, the regression equation we

relationship between the overall level of coal stock in attempt to solve for is as follows:

thermal power plants across India and the indicators for

each of the reasons listed in the section above. Stock = β0 + β1 Demand + β2 Despatch + β3 Import +

β4 OD + e

METHODOLOGY

where,

To study the eﬀect of the various reasons listed in Stock = Normalized Days of Coal Stock at Power

the previous section, the data of days of coal stock is Plants across India at the end of the month

collected from National Power Portal (NPP). The coal

stock available across power plants in India at the end Demand = Normalized Power Demand for the

of the respective months is taken as the coal stock for month in Million Units

the month. The power demand data (in Units, kWh) is

extracted from the website of Central Electricity Authority Despatch = Normalized Monthly Volume of Coal

(CEA). Data of despatch of coal by Coal India Limited to despatched by Coal India Limited and other captive

the power sector, and volume of coal imports has also mines to power sector

been taken from the website of Ministry of Coal. The data

has been collected for the months of the pandemic and Import = Normalized Monthly Volume of Imported

disruption of economic activity, starting from April 2020 Coal by power sector

to October 2021. As a proxy of overdues to Coal India

from the power sector, data for overdues from discoms OD = Normalized Total outstanding dues to

to gencos has been considered. This data for total generating companies from distribution companies

overdues to gencos has been collected from the PRAAPTI at the end of the month

(Payment Ratiﬁcation And Analysis in Ministry of Power,

Government of India, Power procurement for bringing β0 = Constant term in the model

Transparency in Invoicing of generators) database. βi’s = Slope coeﬃcients of each explanatory variable,

and

To estimate the dependency of the coal stock with each

of the other factors, we use linear regression with multiple e = Error term of the model, also called the residuals

variables. Multiple linear regression (MLR), also known

simply as multiple regression, is a statistical technique A multiple linear regression works on the principle

that uses several explanatory variables to predict the that for the best ﬁt model, the sum of squared errors is

outcome of a response variable. The goal of multiple linear minimum. Statistical MLR software and add-ins helps ﬁnd

regression is to model the linear relationship between all of the ’s in the multiple regression. In these models, the

null hypothesis is that the βi’s = 0, which is to say that the

corresponding independent variable does not inﬂuence

the dependent variable. If the p-value of corresponding

variable in the regression output is less than the level

of signiﬁcance, we can reject the null hypothesis and

conclude that the independent variable is signiﬁcant in

17