Review of Economics and Development Studies Vol. 5, No 4, 2019 721 Volume and Issues Obtainable at Center for Sustainability Research and Consultancy Review of Economics and Development Studies ISSN:2519-9692 ISSN (E): 2519-9706 Volume 5: No. 4, 2019 Journal homepage: www.publishing.globalcsrc.org/reads The Causal Nexus of Urbanization, Industrialization, Economic Growth and Environmental Degradation: Evidence from Pakistan 1 Shabana Parveen, 2 Abdul Qayyum Khan, 3 Sohail Farooq 1 PhD Scholar , Department of Economics, Hazara University Mansehra, Pakistan. shabana_economist@yahoo.com 2 Associate Professor, Management Sciences Department, COMSATS University, Islamabad Wah Campus, Pakistan. qayyum72@ciitwah.edu.pk 3 Assistant Professor, Department of Economics, Hazara University Mansehra, Pakistan. thesohailfarooq@hotmail.com ARTICLE DETAILS ABSTRACT History Revised format: 30 Nov 2019 Available Online: 31 Dec 2019 The paper analyzes the causal relation between eeconomic growth, urbanization, industrialization and environmental degradation of Pakistan. The study used time series data for the sample span of 1975-2017, retrived from World Bank Development Indicators (WDI, 2017). Vector Auto Regressive (VAR) model is used for analyzing the causal link amongst the variables, namely economic growth, urbanization, industrialization and environmental degradation. The Granger causality test is used for identifying the order of the causal association. Before estimating VAR, Augmented Dickey Fuller (ADF) as well as Phillips Perron (PP) tests are used for confirming the stationarity characteristic of all variables, first with intercept and then, with intercept along with a linear deterministic trend. Akaike Information Criterion (AIC) is used for selection of optimum lag. The Johansen Cointegration test is adopted for identifying long run associations. The result of the VAR model reveals, If any innovation of one standard deviation from outside the model occurred, it will take about 13 years for CO2, 19 years for urbanization,16 years for industrialization and about 12 years for economic growth in adjustment. These results further indicate that most of the variation in all variables is explained in their own. The study confirmed two unilateral causalities, that is runs from CO2 to urbanization as well as economic growth. The findings of the research work propose that policy makers required to develop policy helpful to the environment which will encourage verifiable economic growth in Pakistan. The policy makers need to plan for environmental issue while making policies regarding urbanization, industrialization and economic growth. © 2019 The authors, under a Creative Commons Attribution-Non Commercial 4.0 Keywords Economic Growth, Urbanization, Industrialization,CO2 Emissions JEL Classification: R11, R19 Corresponding author’s email address: shabana_economist@yahoo.com Recommended citation: Parveen, S., Khan, A. Q. and Farooq.S. (2019). The Causal Nexus of Urbanization, Industrialization, Economic Growth and Environmental Degradation: Evidence from Pakistan, 5 (4), 721-730 DOI: 10.26710/reads.v5i4.883 1. Introduction Carbon Dioxide (CO2) emission is a major component of Green House Gas (GHGs) emissions that is a major factor behind global warming and degradation of natural environment. Environmental degradation http://www.publishing.globalcsrc.org/reads mailto:shabana_economist@yahoo.com mailto:qayyum72@ciitwah.edu.pk mailto:thesohailfarooq@hotmail.com mailto:shabana_economist@yahoo.com Review of Economics and Development Studies Vol. 5, No 4, 2019 722 increases since the 19 th century, with the increasing trend of urbanization and industrialization so the issue of environmental degradation and its relationship with urbanization and industrialization has got much attention from researchers both in developed and developing countries. Pakistan is also facing a higher trend of urbanization with 207.77million population, it has become the sixth most populous country in the world. The major reason behind the trend is the increase growth rate of population as well as migration. The rate of urbanization in Pakistan is 36.38%, which is projected to reach at 50% in the upcoming 15 years (Afzal et al., 2018). As much as industrial growth is concerned, it remains poor throughout the history. The government wants to achieve high growth rate of industrialization which is not satisfactory at present due to political instability, high tax burden and energy crisis. The economic growth of Pakistan remains volatile throughout the history (Pakistan Economic Survey, 2016-17). The main objective of the study is to analyze empirically causal link in economic growth, urbanization, industrialization with environmental degradation. The rest of the paper is organized into five sections. Section 2 consists of the previous literature. Section 3 is about the data along with methodology. Section 4 presents the empirical results whereas Section 5 concludes the study and presents some policy implications. 2. Literature Review Rich empirical work has been done on analyzing the causal link of many variables with CO2 emissions like, Liu and Bae (2017) analyzed the causal association between industrialization, urbanization, per capita real GDP, intensity of energy with CO2 emissions, and confirmed the long-term bidirectional causalities in industrialization, per capita real GDP with CO2 emissions. Sarkodie and Owusu (2017) studied the causal link between industrialization, population, per capita GDP along with CO2 emissions through the Granger causality test, and confirmed a unidirectional causal association of industrialization to per capita GDP, from population to industrialization as well as per capita GDP, from population towards CO2 emissions. Al-Mulali and Ozturk (2015) confirmed the causal link in industrial development, urbanization and energyuse both in the short and long time period. Kasman and Duman (2015) used data of new European Union member countries and confirmed a unidirectional causal association of urbanization with CO2 emissions. Likewise, Liddle and Lung (2014) found the same association in CO2 emissions and urbanization for 105 countries, but they were unable to found granger causality in case of urbanization and electricity consumption. Another group of researchers studied the causal link in economic growth, urbanization, with CO2 emissions like, Xuemei et al. (2012) found a close relationship between these variables as confirmed, economic growth promotes urbanization and vice versa. Yansui et al. (2016) used data of China for the period of 1997 to 2010, studied the link between CO2 emissions with economic growth as well as urbanization. The work was based on Panel co-integration test along with granger causality. The result showed the studied variables increase CO2 emissions there. The results also suggested a two-way long term association in the variables, meaning that urbanization has causal effect over economy growth in the long period and these have a causal association with CO2 emissions too. Jebli et al. (2015) found two- way causal association for economic growth with CO2 emissions for 24 economies in Sub Saharan Africa, in the span of 1980 to 2010. The analysis was based on panel co-integration technique. Mingxing et al. (2014) presented a two-way causality of urbanization with economic growth. The conclusion of Xuemei et al., (2012) were also the same. Most of the studies are conducted on panel data for analyzing the causal association between urbanization, economic growth with CO2 emissions like, Al Mulali et al. (2015) used heterogeneous panel data of 129 states for the span of 1980 to 2011. The researchers used economic growth, financial growth, urbanization, as well as CO2 emissions in analysis. Interestingly, the result of Granger causality showed that due to financial development, all the variables have a direct impact on the environment, in the short and long run meaning that these variables does not increase CO2 emissions. Al Mulali and Ozturk (2015) worked for 14 MENA states for the span of 1996 to 2012. The results of Granger causality confirmed short term and long term causal link among urbanization, industrial Review of Economics and Development Studies Vol. 5, No 4, 2019 723 development and environmental degradation. Literature also analyzed a causal link of energy use with CO2 emissions based on the idea that economic growth increases energy use that results to CO2 emissions increase . Wang et al. (2011) used data of 28 provinces of China and presented bidirectional causality in economic growth, energy use with CO2 emissions. Li and Cheng (2006) confirmed two-way causality for urbanization and economic growth whereas a Shahbaz et al. (2014) confirmed, urbanization along with economic growth causes increase in CO2 emissions. Likewise, Yazdi and Shakouri (2014) used data of Iran for the period from 1975 to 2011 and worked on the association in energy consumption, economy growth, urbanization with CO2 emissions. The study found a one-way causal linkage from urbanization towards CO2 emissions. Vidyarthi (2014) worked on the data of five states of South Asian for the span of 1972 to 2009 and found a two-way association in economic growth with energy use, whereas a one-way causal association of CO2 emissions with energy use. Omri (2013) used simultaneous equations model for studying the same association in MENA states, confirmed a two-way causal association for economic growth with energy use, whereas a one-way causal association of economic growth with CO2 emissions. Likewise, Ang (2009) concluded that economic growth along with energy use contributes CO2 emissions in China, Zhang and Cheng (2009) conducted a multivariate causal study in China and concluded a unidirectional causal association for energy use towards CO2 emissions but not contributed towards economic growth. In addition, Hwang and Yoo (2014) concluded in Indonesia a two-way causal association in energy use with CO2 emissions. For Saudi Arabia, Alshehry and Belloumi (2015) whereas for French, Ang (2007), confirmed a causal association in energy usage, economic growth with CO2 emissions. Apergis and Payne (2010) found this association in ASEAN economies. Lotfalipour et al. (2010) presented a one-way causal association in energy use , gross domestic product with CO2 emissions. Interestingly, Samuel and Abu (2017) found a trade-off for economic growth with CO2 emissions for Nigeria. They found that whenever GDP per capita increases, it also increases CO2 emissions while when CO2 emissions increase, it did not contribute to economic growth. In Pakistan, studies like Mukhopadhyay and chakraborty, (2005); Bukhari, (2012) has done on the impact of macroeconomic variables such as trade openness, population growth, urbanization on environmental degradation. Asjad and Aqeel (2014) found a one-way causal association among GDP, population growth, energy usage with CO2 emissions. In table 1 the summary of the previous research work done about the causality in economic growth, urbanization, industrialization with CO2 emissions for developed as well as developing countries is presented. The purpose of the present work is to analyze the causal link in CO2 emissions, urbanization, economic growth and industrialization in case of Pakistan. Table: 1 Summary of research work done about causality in economic growth, urbanization, industrialization, and environmental degradation Authors Sample and time period Variables Methodology Results Zhang and Cheng (2009) China (1960- 2007) CO2 emissions, GDP, energy use multivariate model, Granger causality test Unidirectional causal association of GDP with energy use, of energy use with CO2 emissions Hossain (2011) Newely industrialized countries (1971-2007) CO2 emissions, energy use, Economic growth, urbanization. Fisher panel cointegration test, Granger causality test Unidirectional relationship of urbanization with economic growth. Unidirectional relationship found of economic growth with CO2 emissions, urbanization, as well as energy consumption Review of Economics and Development Studies Vol. 5, No 4, 2019 724 Omri (2013) Fourteen MENA Countries (1990-2011) CO2 emissions, GDP, energy use. Simultaneous equations model Bidirectional causal association of energy use with GDP. Unidirectional causal association of CO2 emissions with GDP. Liddle and Lung, (2014). 105 countries(1971- 2009) CO2 emissions Urbanization, electricity use Cointegraton, Granger causality test Granger causality from urbanization to electricity usage. Vidyarthi (2014) Five Asian countries (1972-2009) Energy use, CO2 emissions, Economic growth. Granger causality test Bidirectional causality for energy usage with economic growth. Unidirectional causal association in energy use with CO2 emissions in long term. Alshehry and Belloumi (2015) Saudi Arabia CO2 emissions, Economic growth, energy prices, energy use Granger causality test Unidirectional relationship exists from emissions of CO2 to price of energy and economic growth in short period. Unidirectional causal association in energy use, emissions of CO2 emissions and GDP in long period. Asjad and Aqeel (2014) Pakistan CO2 emissions, GDP per capita, energy consumption, population growth. Granger causality test Unidirectional causality found in the variables Saidi and Hammami (2015) six oil- exporting countries (1990-2012) CO2 emissions, GDP, energy usage. GMM model Bootstrap panel Granger causality test, Two way granger causality for UAE for economic growth and CO2. Al-Mulali and Ozturk (2015) Fourteen MENA states (1962-2012) Urbanization, energy use, industrial development. fully modified OLS, Granger causality test All the variables have short and long term causalities. Sarkodie & Owusu (2017) Rwanda (1965- 2011) CO2 emissions, GDP per capita, population, industrialization. ARDL, Granger causality test Unidirectional causality found for industrialization to per capita GDP, population towards GDP per capita, population towards industrialization, population towards CO2 emissions. Liu and Bae (2018) China (1970- 2015) CO2 emissions, real GDP, industrialization, urbanization, energy consumption. ARDL, VECM All variable have positive impact on CO2 emissions. Granger causality exists in Industrialization, energy consumption and CO2 3. Data and Empirical Method Review of Economics and Development Studies Vol. 5, No 4, 2019 725 3.1 Data Source and Variables The research study is based upon time series data for the span of 1975 to 2017 that is retrived from World Bank Development Indicators (WDI, 2017). The main variables that are employed in the study are economic growth, which is represented by a percentage growth in real GDP, urbanization represented by urban population as a percentage of the total population, industrialization represented by industry including construction value added whereas for environmental degradation, CO2 emissions is used as a proxy. VAR model is used for identifying causalities among the macroeconomic variables, namely economic growth, urbanization, industrialization, CO2 emissions with granger causality test for identifying the directions of causalities in the studied variables. 3.2 Model Specification The causal link between CO2 emissions with macroeconomic variables has been analyzed by different econometric techniques. The present study follows the analytical techniques used by Zhao and Wang (2015). Prior to conducting econometric techniques, the data are analyzed for stationarity through Augmented Dickey- Fuller (1979) along with Phillips and Perron(1988) tests, both with intercept and with a linear deterministic trend. Stationarity of the variables allow us to use co-integration test for identifying long run association in the variables. For this purpose, Johansen co-integration (1991, 1995) test is used. The Impulse Response Function (IRF) and variance decomposition is used to examine the vibrant impact of the errors on the variable’s system. Granger causality test is used for identifying the direction of causality amongst the variables. The paper deals with the empirical investigation of the causal relationship between economic growth, urbanization, industrialization and environmental degradation using Pakistan data. We hypothesis our model for empirical analysis pursuing Zhao and Wang (2015), Liddle, B., & Lung, S. (2014). More specifically, the general functional form the model is: ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ Where CO2 is representing Carbon Dioxide Emissions, Ur represents urbanization, Ind stands for industrialization, Eg represents economic growth, k represents lag length and represents error term. 3.3 Empirical Results  Result of ADF and Phillips- perron (PP) unit root tests For stationarity analysis, we use Augmented Dickey-Fuller (ADF) 1979 and Phillips and Peron (1988) tests. The mathematical form of ADF test is ́ Where  = ρ-1 -1 ≤ ρ ≤ 1, with hypothesis as under: Phillips- Perron (PP) test is used to adjust the coefficiat (t-ratio) of the ADF test, when test statistic distribution got affected by any serial correlation. The PP test is presented as Review of Economics and Development Studies Vol. 5, No 4, 2019 726 ́ ( ) ( ( ̂)) Where is the appraisal of error variance while is the zero occurrence of error. Table 2 represents the results of the above mentioned tests. The table shows that economic growth is stationary at level whereas urbanization, industrialization as well as CO2 emissions were non stationary that are converted into stationary after taking the first difference in both tests. Table. 2 Results of Unit root test Variables Result ofADF test Result of PP- test Intercept Intercept and Trend Intercept Intercept and Trend Eg -11.136* -11.910* -9.283* -11.271* Ur 0.379 -7.281* -0.818 -7.306* 0.264 -7.277* -1.383 -7.247* Ind -2.511 -7.210* -2.705 -7.174* -2.322 -8.021* -2.537 -8.492* CO2 -2.235 -7.627* -2.149 -8.259* -4.043 -7.627* -1.741 -17.127* *Significant at 1% significance level 3.4 Cointegration Test For identifying the presence of long term association in the used variables, Johansen(1988) presented two likelihood ratio tests that are maximum Eigen value and trace statistics. These tests are represented in two equations: ( ̂) ∑ ̂ Where in both equations represent the size of the sample, λˆi is the ith largest known associations. Table 3 shows the results of cointegration test. The results show that for all 4 variables, the null hypothesis of no cointegration is rejected at 1% significance level. Table. 3 Results of Cointegration test Levels of significance: *p < 0.01 3.5 Impulse Response Function (IRF) Results IRF is used to know about the response of dependent variables to any change or innovation in error term. Figure (1) presents the estimation of 4 variables that are, CO2 emissions, urbanization, industrialization, economic growth in IRF terms to unitary innovation or shock from outside. The graphs show that if one standard deviation innovation or shock occurs from outside, the CO2 will takes 13 years, urbanization will takes 19 years, industrialization will take 16 years and economic growth will takes 12 years to N.Hypothesis A. Hypothesis Trace Statistics Statistic Critical Value r  = 0 r ≤ 1 r = 1 r = 2 73.92* 31.84* 47.86 29.80 15.06 3.84 r ≤ 2 r ≤ 3 r = 3 r = 4 17.74* 6.74* Review of Economics and Development Studies Vol. 5, No 4, 2019 727 absorb the shocks. Figure 1. Response of Variables to impulses of 1 standard deviation innovation -.02 .00 .02 .04 .06 .08 2 4 6 8 10 12 14 16 18 20 CO2 UR IND EG Response of CO2 to Cholesky One S.D. Innovations -.001 .000 .001 .002 .003 2 4 6 8 10 12 14 16 18 20 CO2 UR IND EG Response of UR to Cholesky One S.D. Innovations -.04 -.02 .00 .02 .04 .06 2 4 6 8 10 12 14 16 18 20 CO2 UR IND EG Response of IND to Cholesky One S.D. Innovations -.2 -.1 .0 .1 .2 .3 .4 2 4 6 8 10 12 14 16 18 20 CO2 UR IND EG Response of EG to Cholesky One S.D. Innovations 3.6 Variance Decomposition Results Variance decomposition analysis is used to identify that how much of the variations in dependent variable are lagged by there own variance and by other variables. Table 4 shows the variance decomposition of the employed variables. The first group referred to the values of variance decomposition of CO2.The values of standard error (S.E) values which is explained by CO2 itself ranging from 100% to 82%. Economic growth is also explaining much of variations in CO2, ranging from 4.09% to 8.52%. Similarly, the variation in CO2 explained by industrialization and urbanization are ranging from 0.62% to 7.62% and 0.02% to 1.74% respectively. The second group represents the values of variance decomposition of urbanization. The values of standard error explained by urbanization itself, ranging from 99% to 94%. The second variable that explains most of the variation in urbanization is economic growth that explains 3.57% to 3.84% variation. Similarly, CO2 explains 1.04% to 0.95% variation and industrialization explained 0.06% to 0.89% variation in urbanization. In a similar way the values of the variance decomposition for industrialization and economic growth can be interpreted. Table 4. Values of Variance Decomposition Variance Decomposition of CO2 Period S.E. CO2 Ur Ind Eg 1 0.0687 100.0000 0.0000 0.0000 0.0000 2 0.0713 95.2936 0.0012 0.6159 4.0894 3 0.0725 92.4464 0.0224 0.6217 6.9096 4 0.0751 86.4072 1.1595 3.9799 8.4535 5 0.0757 85.6066 1.4681 4.1339 8.7914 6 0.0769 83.2002 1.5832 6.6833 8.5333 7 0.0761 83.0070 1.6455 6.7951 8.5524 8 0.0772 82.4011 1.7333 7.3760 8.4896 9 0.0774 82.2217 1.7296 7.5311 8.5168 10 0.0774 82.1319 1.7361 7.6154 8.5158 Review of Economics and Development Studies Vol. 5, No 4, 2019 728 Variance Decomposition of Ur Period S.E. CO2 Ur Ind Eg 1 0.0026 1.0421 98.9578 0.0000 0.0000 2 0.0027 0.9951 95.3750 0.0642 3.5657 3 0.0030 0.7886 96.1344 0.0702 3.0068 4 0.0031 0.9367 94.6304 0.7297 3.7032 5 0.0032 0.8984 94.7619 0.8279 3.5117 6 0.0032 0.9254 94.5247 0.8244 3.7255 7 0.0032 0.9150 94.5502 0.8258 3.7090 8 0.0032 0.9383 94.3863 0.8475 3.8278 9 0.0032 0.9429 94.3576 0.8760 3.8234 10 0.0032 0.9501 94.3198 0.8863 3.8439 Variance Decomposition of Ind Period S.E. CO2 Ur Ind Eg 1 0.0561 0.1296 6.7216 93.1488 0.0000 2 0.0576 0.4288 6.9157 90.1731 2.4824 3 0.0635 0.6687 7.8809 89.2175 2.2329 4 0.0650 0.6779 8.3033 88.4470 2.5718 5 0.0657 0.7038 8.2256 88.3215 2.7491 6 0.0663 0.7111 8.0835 88.4619 2.7435 7 0.0663 0.7149 8.1064 88.3589 2.8198 8 0.0666 0.7221 8.1950 88.2840 2.7988 9 0.0666 0.7214 8.1986 88.2390 2.8411 10 0.0667 0.7256 8.1885 88.2436 2.8422 Variance Decomposition of Eg Period S.E. CO2 Ur Ind Eg 1 0.5107 1.6656 25.6034 13.3006 59.4304 2 0.5674 7.9035 23.7860 11.2723 57.0382 3 0.5935 10.5154 24.0396 12.6282 52.8167 4 0.6065 10.6401 23.0406 15.4812 50.8382 5 0.6119 10.5116 24.1891 15.2418 50.0574 6 0.6161 10.4247 24.2526 15.6373 49.6854 7 0.6178 10.4335 24.4099 15.5591 49.5976 8 0.6192 10.4328 24.2954 15.8647 49.4071 9 0.6197 10.4274 24.3282 15.9044 49.3400 10 0.6199 10.4211 24.3461 15.9235 49.3094 Cholesky ordering: CO2 Ur Ind Eg 3.7 Granger Causality Results Granger causality test (1969) is adopted for identifying the directions of causal link in these variables. Once, long run cointegration is confirmed in variables, then the Granger unidirectional or bidirectional causality test can make clear the direction between the used variables Feng et al. (2009). The estimates of granger causality are given in table5. The results identify two unilateral causalities. One is running from CO2 to urbanization and the other is from CO2 to economic growth. Table 5. Results of Granger Causality Null Hypothesis F-ratios Prob. UR ≠ CO2 1.83816 0.1737 CO2 ≠ UR 5.81056 0.0065 IND ≠ CO2 0.39351 0.6776 CO2 ≠ IND 0.25946 0.7728 Review of Economics and Development Studies Vol. 5, No 4, 2019 729 EG ≠ CO2 0.77482 0.4683 CO2 ≠ EG 3.75681 0.0330 IND ≠ UR 0.33006 0.5230 UR ≠ IND 2.09155 0.1382 EG ≠ UR 2.30943 0.1139 UR ≠ EG 1.44316 0.2495 EG ≠ IND 0.16602 0.8477 IND ≠ EG 0.82584 0.4460 Note: ≠ represents null hypothesis i.e., does not Grangers cause 4. Concluding Remarks Economic growth is the desire of every country. The role of urbanization and industrialization cannot be ignored in the growth process of a country. The macroeconomic variables urbanization, industrialization, economic growth are associated with CO2 emissions too. The purpose of this work is to analyze any causal association in urbanization, industrialization, economic growth with CO2 emissions. The results of VAR model indicate that if innovation of 1 standard deviation is given, it takes about 13 years for CO2, 19 years for urbanization, 16 years for industrialization and 12 years for economic growth to adjust. It follows that in Pakistan the policies regarding economic growth, industrialization, urbanization and CO2 emissions are not effective as it takes much longer time to adjust. Furthermore, the case of urbanization is much alarming, therefore special attention is needed in policy formulation for urbanization, and further the policy must be objective oriented and also proper check on its implementation is required. In addition, for all variables, the causality result indicates that the response of every variable to their own shock/ innovation was much better as compare to shock in other variables. Granger causality results identify only two unilateral causalities, that is from CO2 emissions towards economic growth, and urbanization. There found no bidirectional causality and independent type relationships were found in economic growth and urbanizations, economic growth with industrialization, urbanization with industrialization and industrialization with CO2 emissions. The issue of CO2 emissions must not be ignored at the time of framing policy for industrialization. References Afzal, M., Shaoib, S A., & Nawaz, M. (2018). MacroEconomic Determinants of Urbanization in Pakistan. ISSN(E) DOI journal, 5(1): pp. 6-12 . Al-Mulali U., & Ozturk I. (2015). The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy, 84: pp. 382- 389. Al-mulali U, Tang CF & Ozturk I (2015). Does financial development reduce environmental degradation? Evidence from a panel study of 129 countries. Environ Sci Pollut Res: pp. 1–10. Apergis, N., & Payne, J. E. (2010). The emissions, energy consumption, and growth nexus: evidence from the common wealth of independent states. Energy Policy, 38: pp. 650-655. Alshehry, A. S., & Belloumi, M. (2015). Energy consumption, carbon dioxide emissions and economic growth: The case of Saudi Arabia. Renewable and Sustainable Energy Reviews, 41: pp. 237-247. Ang, J, B. (2007). CO2 emissions, energy consumption, and output in France. Journal of Energy Policy, 35: pp. 4772-4778. Ang, J. B. (2009). CO2 emissions, research and technology transfer in China. Ecological Economics, 68(10): pp. 2658-2665. Asjal, B. M., & Aqeel , B. M. (2014). Impact of CO2 emissions: Evidence from Pakistan. Pakistan Business Review, 15(4). Bukhari, N. (2012). Impact of trade openness and energy consumption on the environment of Pakistan (1972-2010). (Doctoral dissertation, Open University Islamabad). Feng, T.W.; Sun, L.Y.; Zhang, Y(2009). The relationship between energy consumption structure, economic structure and energy intensity in China. Energy Policy, 37: pp. 5475–5483. Review of Economics and Development Studies Vol. 5, No 4, 2019 730 Government of Pakistan (GOP), Economic Survey 2016-17, Economic advisors wing, Ministry of Finance, Islamabad. Hossain MS (2011) Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy 39: pp.6991–6999 Jebli, B. M., Youssef, B. S., & Ozturk I. (2015). The role of renewableenergy consumption and trade: environmental Kuznets curve analysis for sub-Saharan Africa countries. African Development Review, 27(3): pp. 288–300. Kasman A., & Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Economic Modelling, 44 : pp. 97–103. Liddle, B., & Lung, S. (2014). Might electricity consumption cause urbanization instead? Evidence from heterogeneous panel long-run causality tests. Global Environmental Change, 24(1): pp. 42-51. Liu, X., & Bae, J.(2018). Urbanization and industrialization impact of CO2 emissions in China. Journal of Cleaner Production, 172 : pp.178-186. Lotfalipour, M. R., Falahi, M. A., & Ashena, M. (2010). Economic growth, CO2 emissions, and fossil fuels consumption in Iran Energy, 35: pp.5115-5120. Mingxing, C., Yongbin, H., & Zhipeng, T. (2014). The provincial pattern of the relationship between urbanization and economic development in China. Journal of Geographical Sciences, 24(1): pp. 33 -45. Mukhopadhyay, K., & Chakraborty, D. (2005). Is Liberalization of Trade good for the Environment? Evidence from India. Asia-Pacific Development Journal, 12(1): pp. 110-136. Omri, A. (2013). CO2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models.Energy economics, 40: pp. 657-664. Sarkodie, S, A., & Owusu, P, A. (2017). Carbon dioxide emissions, GDP per capita, industrialization and population: An evidence from Rwanda. Environ. Eng. Res, 22(1): pp.116-124 Saidi, K., & Hammami, S. (2015). The impact of energy consumption and CO2 emissions on economic growth: Fresh evidence from dynamic simultaneous-equations models. Sustainable Cities and Society, 14: pp. 178-186. Samuel, O. O. D., & Abu, J. (2017). Sustainable Economic Development and Environmental Degradation: Evidence from Nigeria. IIARD International Journal of Economics and Business Management, 3(3): pp. 2489-0065, Shahbaz, M., Khraief, N., Salahuddin, G. S., & Ozturk, I. (2014). Environmental Kuznets curve in an open economy: A bounds testing and causality analysis for Tunisia. Renewable and Sustainable Energy Reviews, 34: pp. 325 – 336. Vidyarthi, H. (2014). An econometric study of energy consumption, carbon emissions and economic growth in South Asia: 1972-2009. World Journal of Science, Technology and Sustainable Development, 11(3): pp. 182-195 Wang, S., Zhou, D., & Zhou, P. (2011). CO2 emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy, 39(9): pp. 4870–4875. Xuemei, B., Jin, C., & Peijun, S. (2012). Landscape urbanization and economic growth in China: Positive feed backs and sustainability dilemmas. Environmental Science and Technology, 46(1): pp.132–139. Yansui, L., Bin, Y., & Yang, Z. (2016). Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis. J. Geogr. Sci. 2016, 26(2): pp.131-152. Yazdi, S. K. & Shakouri, B. (2014). The impact of energy consumption, income, trade, urbanisation and financial development on carbon emissions in Iran. Advances in Environmental Biology, 8(5): pp. 1293-1300. Zhang, X. P., & Cheng, X. M. (2009). Energy consumption, carbon emissions and economic growth in China. Ecological Economics, 68: pp. 2706-2712. Zhao, Y., & Wang, S (2015). The Relationship between Urbanization, Economic Growthand EnergyConsumption in China: An Econometric Perspective Analysis. Sustainability, ISSN 2071- 1050: pp. 5609-5627;