Bh Khan Non Conventional Energy Resources Pdf
Everardo Frost
This document provides biographical information about the author of the book "Non-Conventional Energy Resources". It states that BH Khan received engineering and PhD degrees from Aligarh Muslim University and IIT Kanpur. He has over 31 years of experience teaching and researching electrical engineering in India and abroad. His areas of interest include non-conventional energy sources, power electronics, and microprocessor applications. The author has received several awards for his work in the energy field.Read less
Conventional energy consumption such as coal, natural gas, and oil is a source of deteriorating environmental sustainability as well as a severe challenge to the green environment. The present paper explores the nexus between CO2 emissions, energy imports, energy intensity, and power generation from renewable and non-renewable energies from 1990 to 2021 in Australia. Based on the ARDL model, the findings reveal that energy imports and power generation from non-renewable energy sources show an adverse effect on the green environment. A 1% increase in conventional energy imports leads to an 11% increase in CO2 emissions. Similarly, a 1% increase in energy generation from conventional sources will increase CO2 emissions by 45%. On the other hand, lower energy intensity and power generation from renewable sources reveal a positive effect on environmental quality. A 1% increase in energy intensity will decrease CO2 emissions by 92% while energy generation from non-conventional sources by 15%. Most interestingly, energy intensity reveals the foremost position among all the selected variables to decarbonize and effectively transform conventional energy to clean and green energy production and utilization. The robustness test outcomes confirm the results of the empirical output. Furthermore, this study suggests that governments and policymakers should focus on the adaptation of lower energy intensity for the purpose to reduce CO2 emissions and promote a clean and green environment. Finally, power generation from renewable energy sources plays an inevitable role which ultimately helps environmentally sustainability in Australia.
The reminding of the paper is arranged as follows: the study has considered the literature review on the linkage between CO2 emissions, energy intensity (EI), energy imports (ET), electricity generation from a renewable source (EPNR), and electricity generation from conventional energy sources (EPR). The methodology section describes the model specification, data source, and econometric analysis. The results and discussions section highlights the empirical outcomes of the present study. Finally, the paper discusses the conclusion, policy recommendations, and limitations of the study.
The present study endeavors to examine the impacts of effects of energy intensity (ET), energy imports (EI), electricity production from renewable energy sources (EPR), and electricity production from non-renewable energy sources (EPNR) on CO2 emissions from 1990 to 2021 and propose policy implications for utilizing the higher energy intensity in Australia. The current study divides the literature review section into four segments: First, the association between energy intensity and CO2 emissions; Second, the relationship between energy imports and CO2 emissions; Third, the impact of electricity production from renewable energy sources on CO2 emissions; and Fourth, the connection between electricity production from non-renewable energy sources and CO2 emissions.
The above studies used various methods to examine energy consumption and CO2 emissions problems based on data from different countries. However, none of them examined changes in Australia using the ARDL approach. Therefore, the contribution of this study to current literature on energy import and energy consumption policy and CO2 emissions is through using the ARDL method to highlight the factors affecting CO2 emissions in Australia. A recent time series method proposed by Jordan and Philips (2018) is employed to get robust and consistent estimation results in the study. This analysis will help policymakers to identify the impact of energy import, and energy consumption on environmental degradation and attach importance to the influence of energy consumption structure optimization on CO2 reduction. They can then take steps toward raising energy efficiency by building an effective strategic plan for sustainable development.
In this paper, we employed a series of econometric methods such as the conventional unit root test, Zovit-Andrew structural break unit root test, panel co-integration, ARDL model, FMOLS, DOLS, CCR, and Granger causality tests. Figure 5 illustrates the econometric strategy of the present study.
In Eq. 1, CO2 defines carbon emissions, EI represents energy imports, ET describes energy intensity, EPR is the electricity production from renewable sources, and EPNR represents electricity production from non-renewable sources, respectively. The present study empirically probes the multivariate time series method. First, the data series is changed into a natural logarithmic form for the purpose to overcome the issue of heteroscedasticity. Second, the autoregressive distributed lag (ARDL) estimation is used which is a common approach in time-series data. The study of Khan et al. (2022a, b, c, d, e, f) suggested such techniques could assess numerous potentially coherent theories in case the response variable is explored at level 1(0) or first difference 1(1).
The main objective of the unit root test is to differentiate the selected response and explanatory variables to be stationary at the level or first difference. All the selected variables were joined at the standard unit order of 1(0) and 1(1) illustrating that is uniform with the sufficiency of the ARDL bounds estimation is specifically best fitted for our study. Further, all the selected variable connections were examined before utilizing the ARDL bounds model (Dickey and Fuller 1979; Phillips and Perron 1988) utilizing the following equation.
To examine the long-run association between CO2 emissions, energy intensity, energy imports, and energy generation from renewable and non-renewable energy sources, we employ the Johansen co-integration tests introduced by Johansen and Juselius (1990) for all the selected variables. In addition, to confirm the co-integration among the study variables, we used the Johansen co-integration technique of the three different estimations such as (1) the unit root test proposed by Pedroni (1999) and Pedroni (2004), an advanced estimation that is distinguished as the Padroni co-integration method; (2) another co-integration method developed by Kao (1999), the Kao co-integration technique; (3) an error correction based co-integration method, which is an appropriate estimation in the co-integration evaluation Westerlund (2007).
where CO2 defines carbon dioxide emissions, EI represents energy imports, ET describes energy intensity, EPR denotes electricity production from renewable sources, and EPNR represents electricity production from non-renewable sources respectively. Δ represents the variation in the response variable. Time is represented by t, and ε represents the error term.
To explore the Granger causality association, this paper employs the Granger causality tests taking CO2 emissions, energy imports (EI), energy intensity (ET), electricity production from renewable sources (EPR), and electricity production from non-renewable energy (EPNR).
The ARDL technique was first proposed by Pesaran et al. (1999) and Pesaran et al. (2001). It is important to understand that the ARDL method has numerous advantages, in contrast to other co-integration methods. Further, the ARDL co-integration method can be employed with a lag length of response and explanatory variables, whereas the other co-integration techniques require identical lag lengths (Engle and Granger 1987; Johansen and Juselius 1990). Additionally, ARDL co-integration method can use in the case of data series stationarity level of 1(0) or 1(1). The present study designs the following ARDL equations:
In the above equation, \(\sigma _1 to \sigma _5\) represents the long-run variance of explanatory variables. CO2 is carbon dioxide emission, EI is energy imports, ET represents energy intensity, EPR denotes energy production from renewable sources, and EPNR is electricity production from non-renewable sources. The Akaike information criteria (AIC) were employed to examine the appropriate lag length. Moreover, for the ARDL short-run model, the following ECM model was adopted.
The output of the descriptive summary are presented in Table 3. The mean value of CO2 emissions is reported at 1.22, and the standard deviation is 0.02. The energy import mean value is 11.85, and the standard deviation is 0.26. The energy intensity mean value is 0.72, while the standard deviation is 0.05. The mean value of EPNR means the value is reported as 1.86, and its standard deviation is 0.05. In last, the mean value of EPR is 0.28, while the value of the standard deviation is 0.51. The Jerque-Bera statistic shows the data is equally distributed. The total number of observations in this study accounted for 33.
Moreover, the output of the correlation matrix are presented in Table 3. A positive correlation was found between CO2 emissions, energy imports, and electricity generation from non-renewable energy sources, indicating that energy imports and non-renewable energy sources adversely affect CO2 emissions in Australia. In contrast, a negative and significant correlation was found between CO2 emissions, energy intensity, and renewable energy sources, indicating that lower energy intensity and consumption of electricity from renewable energy sources play a crucial role in abating CO2 emissions.
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