193 © 2020 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Economics and Empirical Research Vol. 7, No. 2, 193-206, 2020 ISSN(E) 2409-2622 / ISSN(P) 2518-010X DOI: 10.20448/journal.501.2020.72.193.206 © 2020 by the authors; licensee Asian Online Journal Publishing Group Influence of Environmental Degradation and Economic Growth on CO2 Emissions: Evidence from Developing Countries Rabnawaz Khan1 YuSheng Kong2 Jin Xinxin3 ( Corresponding Author) 1School of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu, Zhenjiang, People’s Republic of China. 2Professor, Dean in the School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China. 3School of Literature and Language, Jiangsu University, Zhenjiang, Jiangsu, Zhenjiang, People’s Republic of China. Abstract China is working to revive the ancient on the Silk road trade routes from Asia to Europe and promote the corporation of energy production with trade under its transactional megaproject the Belt and Road (BRI) initiative. We investigate the relationship between energy consumption and economic growth of regions in this paper along with BRI using panel data for 55 countries during the period of 1970-2015. By assessing Vector Error Correction Model (VECM), fully changed Ordinary Least Squares (FMOLS) and dynamic Ordinary Least Squares regression (DOLS) with first generation test. The statistical finding there is evidence from long-run bidirectional causalities among CO2 emissions, energy use per capita, GDP per capita, manufacturing industries and fossil fuel energy consumption. Hence, there is unidirectional short-run causality running from GDP to renewable energy in South Asia BRI listed countries and bidirectional causality between consumption of energy and GDP per capita in the long run East Asia (Australia, Brunei, Indonesia, Malaysia, Myanmar, Vietnam), Europe & Central Asia (Azerbaijan, Kazakhstan. Russian, Turkmenistan, Uzbekistan) and Middle East & North Africa (Bahrain. Egypt, Iran, Iraq, Kuwait, Oman, Qatar, UAE, Yemen). The results confirm the renewable energy and fossil energy consumption and manufacturing industries. These outcomes suggest significant provision in the economies and trade of China with different countries in the Belt and Road regions. Keywords: CO2 emission, Economic growth, Regional classification, Belt road initiative (BRI). JEL Classification: F43, F64, O11, Q32. Citation | Rabnawaz Khan; YuSheng Kong; Jin Xinxin (2020). Influence of Environmental Degradation and Economic Growth on CO2 Emissions: Evidence from Developing Countries. Asian Journal of Economics and Empirical Research, 7(2): 193-206. History: Received: 28 May 2020 Revised: 3 July 2020 Accepted: 5 August 2020 Published: 17 August 2020 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Acknowledgement: All authors contributed to the conception and design of the study. Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no conflict of interests. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction .................................................................................................................................................................................... 194 2. Literature Review .......................................................................................................................................................................... 195 3. Data and Methodology ................................................................................................................................................................. 195 4. Result ................................................................................................................................................................................................ 198 5. Conclusions ..................................................................................................................................................................................... 205 References ............................................................................................................................................................................................ 205 http://crossmark.crossref.org/dialog/?doi=10.20448/journal.501.2020.72.193.206&domain=pdf&date_stamp=2017-01-14 http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ http://asianonlinejournals.com/index.php/AJEER/article/view/2029 http://asianonlinejournals.com/index.php/AJEER/article/view/2029 Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 194 © 2020 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study is unique based on economic growth, likewise the prior research is not investigated the dynamic link between developing countries Energy-consumption and economic growth. Investigating economic growth and CO2 emissions by manufacturing industries. Moreover, the innovation of this study is indicated that there is evidence from long−run bidirectional causalities among CO2 emission, Energy-use per capita, Per-capita of GDP, Industrialization and remnant fuel Energy-consumption. 1. Introduction China’s Belt and Road Initiative (BRI), start emergent strategies and provides coincidental to build trade between South Asian countries and Europe, aims to build trade and infrastructure by one silk road and coordinate with African countries, the trade road has it a roadblock in seven countries and later join other countries. In Pakistan, the China is developing Baluchistan province, and an estimated $63 billion will invest in China-Pakistan Economic Corridor (CPEC), developing airports, highway and Beijing aims to link landlocked with a western region to Gwadar. This game changer does not influence; it will change the level of consumption and need for goods. Although environment effects on BRI listed countries and developing strategies will boost economy but influence on environment by pollution. The wide variety of Chinese products capture a major part of the Pakistani local market and resulted in closing down many industries while others are struggling to survive. Hence the fuel emission indicated by the fuel import, percentage of merchandise export employs that highest fuel rate was 17.40% in 2013 (Bank, 2013) in China and 35.14% in Pakistan same year. Figure 1. The problem of energy security and climate have drawn increasing attention in worldwide and International Energy Agency (IEA) have declared through global energy demand. The growth is projected to slow until 2040, and total energy need will increase 30% by China and India. The transport sector, as a main energy consumer sector and sources of greenhouse (GHG) emission, its concern for sustainable development, in 2015 there was 24% CO2 emission occurred by fuel consumption (Liu., Zhao, Liu, & Hao, 2018); (Mirza & Kanwal, 2017). The 79% developed countries are responsible for enormous CO2 emission and two fifth pollution (60% CO2) emission responsible countries are China and USA. These top polluters countries are accountable for a huge global warming, and 11 billion tons CO2 it records emission in the China with 1.36 billion population in the period of 2013. The USA is one of the top developed. Figure-1. Fuel consumption. Source: TM.VAL.FUEL.ZS. UN. Country and CO2 emission from only gaseous fuel consumption is only 1.43 million kt, that account for 21.72% of all over the world CO2 emission in 2014 and China, Russia, Iran and Japan were 48.97% with 6.6 millions of fuel gaseous. The export and infrastructure of China is promoting economic trade and great important to climate change and energy consumption (Zhang, Liao, & Hao, 2018). I vow the president of China to keep resolving climate projects. The global warming has already showed a massive change in climate cause of huge production, infrastructure, cement and steel extensive fossil energy consumption and turn to engender of CO2 emission (Zhao, Zhang, & Shao, 2016). As a result, the environment quality will improve and we accept alternative development as a real solution for lower carbon emissions. Some developing countries have enormous renewable resources and there is a large gap in the development level of energy between developing to developed countries (Schwerhoff & Sy, 2017). Noteworthy that, China-Pakistan Economic Corridor (CPEC) could serve as affective and slushy platform of economic development not only Pakistan and China, other relevant countries their economy in a different level. The more significant aim is to share CO2 emissions and reduce it by different projects. This study is unique because of economic growth, likewise the prior research papers have investigated the dynamic link between developing countries’ energy consumption and economic growth (Ito, 2017; Kong & Khan, 2019; Liu. et al., 2018; Tian, Bai, Jia, Liu, & Shi, 2019). The prior investigations have not showed the regional effects by states likes, 12 East Asia (EA), 4 South Asia (SA), 26 South Asia countries (SEC) and 13 Middle East and North Africa (MENA). The logical structure of this paper highlights the relationship between energy and economic growth of regions and causes of CO2 emissions by development change. This study promotes the use of renewable vitalities that constantly replenished not directly diminish. Environmental degradation and economic growth cause air contamination in the regions and produce a huge flow of CO2 emissions for developing countries. When countries burn fossil fuels like petroleum gas, coal, carbon dioxide and other gases released into environments so these emission trap heat to the earth and the sea https://economictimes.indiatimes.com/news/international/world-news/chinas-bri-initiative-hits-roadblock-in-7-countries-report/articleshow/63771550.cms https://economictimes.indiatimes.com/news/international/world-news/chinas-bri-initiative-hits-roadblock-in-7-countries-report/articleshow/63771550.cms https://www.cgdev.org/media/who-caused-climate-change-historically https://www.businessinsider.com/these-6-countries-are-responsible-for-60-of-co2-emissions-2014-12 https://www.businessinsider.com/these-6-countries-are-responsible-for-60-of-co2-emissions-2014-12 https://data.worldbank.org/indicator/TM.VAL.FUEL.ZS.UN https://knoema.com/atlas/topics/Environment/Emissions/CO2-emissions-from-gaseous-fuel-consumption?action=export&gadget=tranking-container. https://knoema.com/atlas/topics/Environment/Emissions/CO2-emissions-from-gaseous-fuel-consumption?action=export&gadget=tranking-container. http://fortune.com/2017/05/15/china-xi-jinping-belt-road-summit-protectionism/ http://fortune.com/2017/05/15/china-xi-jinping-belt-road-summit-protectionism/ https://www.sciencedirect.com/topics/earth-and-planetary-sciences/global-warming https://www.sciencedirect.com/topics/earth-and-planetary-sciences/global-warming https://www.sciencedirect.com/topics/earth-and-planetary-sciences/energy-consumption https://www.sciencedirect.com/topics/earth-and-planetary-sciences/energy-consumption https://www.sciencedirect.com/topics/earth-and-planetary-sciences/environmental-quality https://www.sciencedirect.com/topics/earth-and-planetary-sciences/environmental-quality https://www.sciencedirect.com/topics/earth-and-planetary-sciences/carbon-emission https://www.sciencedirect.com/topics/earth-and-planetary-sciences/carbon-emission https://www.sciencedirect.com/topics/earth-and-planetary-sciences/renewable-resource https://www.sciencedirect.com/topics/earth-and-planetary-sciences/renewable-resource http://www.cpecinfo.com/cpec-news-detail?id=MTc4Nw== Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 195 © 2020 by the authors; licensee Asian Online Journal Publishing Group levels have led to a higher cause of the earth’s rising temperature, extreme storms that stem from a changing climate. The literature, dataset estimation, results and conclusion and recommendations in sections 2, 3, 4 and 5. 2. Literature Review In 1970, the oil and energy crisis has examined and energy consumption count as a paired factor of labor, capital production in the economic growth (Korppoo & Kokorin, 2017; Ozcan, 2013; Solow., 1974). The empirical analysis emphasis by Granger causality test between energy and economic growth and classifies the econometric method based on variable selection and data sets. The early mainstream study has been classified and determined the facts of energy and industrial development regarding GDP and per capita of GDP (Soytas, Sari, & Ewing, 2007). Table-1. literature review of Economic growth and CO2 emission. Study Datasets Econometric techniques Period Outcomes Liu and Hao (2018) 69 countries vector error correction model (VECM), fully modified OLS (FMOLS) and dynamic OLS (DOLS) approaches, 1970 and 2013. the nexuses of the energy consumption and economic developments vary across different subgroups. For the entire group, there is evidence of long−run bidirectional causalities among carbon emissions, energy use, industry value added and GDP per capita Cheng et al. (2017) G6 countries error-correction- based Granger causality 1990– 2012. that economic growth can indeed cause increases in energy consumption Hao, Wang, Zhu, and Ye (2018) OECD countries The vector errors correction model (VECM) 1995- 2010 causal relationship from rural GDP to rural investment and bilateral causal relationship between rural GDP and rural energy consumption in the short run. Zhang et al. (2018) 30 individual Chinese provinces ARDL 1970 Empirical evidence suggests the existence of energy consumption per capita in some provinces. Kahia, Ben Aïssa, and Charfeddine (2016) data of 429 observations for 13 MENA NOECs. panel error- correction model 1980 to 2012 long run causality for the two samples. Mirza and Kanwal (2017) Pakistan ARDL approach 2014 Dynamic causality between energy consumption, economic growth and CO2 emission. Kohler (2013) South African ARDL approach, Engel Granger method. 1960- 2009 Per capita has significant long positively effect in level of CO2. Find bidirectional causality between in income per capita and foreign trade. Pao and Fu (2013) The annual data for Brazil’s real GDP, capital, labour and different types of energy consumptions the vector error correction models reveal a unidirectional causality 1980– 2010 that Brazil is an energy-independent economy and that economic growth is crucial in providing the necessary resources for sustainable development. Countries have the different economic characteristic that may have the different direction of causality with different policies, here the study explore the relationship between emission and the growth in individual countries Table 1 and how the fuel import (% merchandise import) effects this project in different angles. The infrastructure of Pakistan divers on modern strengthen and transportation networks, with many and determined projects. CPEC project becomes operational when Chinese cargo transported overland of Gwader Port for onward Africa and West Asia by maritime shipment. 3. Data and Methodology 3.1. Sample and Variables The data period covers 1970-2015 for a panel comprising the 55 countries. The One belt One road showed seven components of economic growth (6 economics and 1 merchant silk road), the economic corridor was consisting with 6 countries with the name of CPEC in 2013. Table 2 is an explanatory variable definition with a source of data used for analysis. We use henceforth part of preceding studies in the pragmatic analysis Figure 2. we conduct The sources of the database from the Base (2015) and Databases (2015) data. In the terms of total primary energy supply (TPES), RE represent Renewable Energy (RE) including the primary energy equivalent of hydro (excluding pumped storage), solar, geo-thermic, tide and wave sources. https://www.sciencedirect.com/topics/engineering/energy-utilization https://www.sciencedirect.com/topics/engineering/energy-utilization https://www.sciencedirect.com/topics/engineering/causality https://www.sciencedirect.com/topics/engineering/sustainable-development http://www.gwadarport.gov.pk/ http://www.gwadarport.gov.pk/ https://en.wikipedia.org/wiki/China%E2%80%93Pakistan_Economic_Corridor https://en.wikipedia.org/wiki/China%E2%80%93Pakistan_Economic_Corridor Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 196 © 2020 by the authors; licensee Asian Online Journal Publishing Group While CMIC and FFE influence the infrastructure of this mega project, high manufacturing and export development creating negative aspects on environments by CO2. per capita increase the wealth also increases CO2 emissions in different development sectors. It shows Table 3 the mean of indicators with a standard deviation. Figure-2. Region distribution of countries. Sources: East Asia (EA), South Asia countries (SEC), Europe & Central Asia (E&CA), Middle East & North Africa (MENA). East Asia (EA), South Asia countries (SEC), Europe & Central Asia (E&CA), Middle East & North Africa (MENA) In this empirical method, in what we follow, we test all explanatory variables in panel data, in case of non- stationary, we investigate the long run prevailing cointegration relationship and investigate their magnitude by long-run stationary. The panel cointegration test with panel unit root test applied on all variables, which allow the serial correlation among a cross section i.e. the so-called 2nd generation test. Therefore, it is essential to estimate the stationary of each explanatory variable by a unit root test. The reliability of variables ensure by four unit root test (Levin, Lin, & Chu, 2002) which include IPS Augmented test used by cross-sectional (Im, 2003; Levin et al., 2002; Pesaran, 2007) used for panel unit root test and so far panel co-integration estimated error-correction by Westerlund (2007). Table-2. Variables description for the analysis. Variables Definition Unit measurement Time frame availability Data sources RE Renewable energy 1000 toe (tonne of oil equivalent) 1960-2017 OECD C CO2 emission Metric tons per capita 1960-2014 World Bank (EN.ATM.CO2E. PC) EP Energy use per capita Kilograms of oil equivalent per capita 1960-2015 World Bank (EG.USE.PCAP.KG.OE) IVA Industry value added % of GDP 1994-2016 World Bank (NV.IND.TOTL. ZS) GDPC GDP Per capita growth Constant 2010 US$ 1960-2017 World Bank (NY.GDP.PCAP. KD) CMIC Co emissions from manufacturing industries and construction % of total fuel combustion 1960-2014 World Bank (EN.CO2.MANF. ZS) FFE Fossil fuel energy consumption % of total 1960-2015 World Bank (EG.USE.COMM.FO.ZS) Table 4 shows unit root test on a level. In the level case, we cannot reject the null hypothesis, except for the GDP per capita growth, CO2 emission, arm import trend, commercial service export, and inflation GDP deflator. Table 5 shows Padroni Residual Co-Integration test with Newey-West automatic bandwidth. 3.2. Econometric Methods Whether or not the panel data processes are stationary specifies in the regression's selection model, if the panel data process is non-stationary, the conformist OLS estimation method would lead and solve a spurious problem in regression. show the dependent variable where regressed on to get . Equation. [1] is shows the dependent variable where regressed on to get and effect showed by null hypothesis and deterministic trend is . ∑ (2) The Equation. [2] shows a dependent variable in 1st order lag, also i s a vector that contains all explanatory variable. Is the autoregressive coefficient and variable is equal to − . Meanwhile, the null hypothesis , is equal to zero, and there is a parameter of a unit root. When an is negative, so it shows that we reject the null hypothesis, implying a stationary in panel data? The panel data requires stationary or cointegration in regression. The residual of spurious regression of non-stationary examine by co-integration test in https://data.oecd.org/energy/renewable-energy.htm Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 197 © 2020 by the authors; licensee Asian Online Journal Publishing Group explanatory variables. Equation. [3] show the dependent variable where regressed on to get . The individual effect showed by null hypothesis and deterministic trend is . The null hypothesis , is not co- integration, the residual value will be l(1) process, if the variables are cointegrated, the residual value of the alternative hypothesis is l(0) process. (3) Renewable energy, we followed the approach of Cai, Che, Zhu, Zhao, and Xie (2018); Hussain, Arif, and Aslam (2017); Liu and Hao (2018); Waite (2017); Younas et al. (2016); (Yu & Lu, 2018) The long-run relationship between renewable energy, CO2 emission, Energy use per capita, Industry value added, GDP Per capita growth, Co emissions from manufacturing industries and construction and Fossil we give fuel energy consumption: Where RE shows the renewable energy and i=1,….,55 and t=1970,….,2015 reveal the country and time, respectively whereas renewable energy, which we take from the source of energy per capita, industry value added, GDP growth level, manufacturing industries, and fossil fuel consumption. Shows the fixed effects of country and − are the long-run parameter of elasticities, related with each explanatory variable of this panel data. Kao (1999) and Pedroni (2001) tests based on two-step method implemented for data analysis in this paper. This theoretical test based on the vector error correction model, the Kao test approach showed with the Padroni test, includes cross section intercepts and homogenous coefficient of variables. The Johansen Fisher panel (Wu, Zhu, & Zhu, 2018). The long-run dynamic estimation exist among the variable after the cointegration test. The relationship leads to long-run correlation in cointegration and variables endogeneity problems, which may cause OLS estimation in asymptotic bias. This paper adopts the dynamic ordinary least square (DOLS) and panel fully changed ordinary least squares (FMOLS), which are unbiased asymptotic estimators developed by Pedroni (2001) The long-run correlation problems and endogeneity solve by FMOLS estimator a non-parametric term of correlation. The associated t-statistics constructed with panel FMOLS in Equation 4 where indicate the conventional (FMOLS) estimator is indicating in Equation (5a and 5b): ∑ ∑ The DOLS panel method augments the lead and lagged difference of the repressors in the cointegration equation, which helps to eliminate serial correlation and the asymptotic endogeneity from the individual model. Equation 6 shows panel DOLS regression to get the DOLS estimator. The long-run dynamic involves lead (Ki) and lags (Ki). Equation 7 shows DOLS regression with long run and Equation 8 shows the dynamic link of co- integration. If the long-run cointegration relationship exists among the variables, we can establish the vector error correction model to investigate by the two-step procedure of Engle and Granger (1987) causalities. ∑ ∑ ∑ ∑ ∑ ∑ ∑ As a bounded VAR with cointegration relationship, the VEC model handling the endogenous variable to the long-run equilibrium while allowing the short-run variable. The dynamic panel VEC model by the following Equation [9] to [16] where the first difference operator showed with , the intercept of individual effects denoted with , the optimal lag length denoted with q by Schwarz information criterion (SC), the error correction term derived from the long-run correlation and specify serial uncorrelated error term. In the panel vector error correction model (VCM), short-and long-run causality among the variable treated by Granger causalities. The direction of causality shows the significance of the coefficient in the equation. The direction of causality in the equation shows the significance of the coefficient. It represents the dynamic error correction model below: ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ https://pdfs.semanticscholar.org/99c2/fdecbced56a09ace2950da5cd7d0bdd617b7.pdf https://pdfs.semanticscholar.org/99c2/fdecbced56a09ace2950da5cd7d0bdd617b7.pdf Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 198 © 2020 by the authors; licensee Asian Online Journal Publishing Group ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ The direction of causality by the significance of the coefficient in the equation. The δ denotes the short-run casualty of the change coefficient. The long-run causality. 4. Result 4.1. Descriptive Statistics and Unit Root Figure 3 explored the value of a box plot element, the median is 95% of confidence with a fixed width, lower and upper whisker. RE lower whisker shows minimum far outliers in lower whisker FFE, EP and GDPC, register the highest mean value with nearest neighbor fit line and it concluded that all predictor with renewable energy integrated with the countries along Belt and Road. Figure 4 states dispersal has computed in renewable energy (RE), the Middle East and North Africa are embarking, that will require more than $200 billion in investment on a massive program to develop renewable energy and sustain socio-economic development of the region, with ripple effects of renewables development throughout society by economic growth. Figure 5 showed the residual sign of renewable energy in different countries by regions. Figure-3. Categorical means of countries by boxplot. http://helioscsp.com/mena-plans-200bn-investment-in-renewable-energy/ https://www.irena.org/mena https://www.mei.edu/publications/middle-east-hub-chinas-modern-silk-road Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 199 © 2020 by the authors; licensee Asian Online Journal Publishing Group Table 4 shows the panel unit root test of the LLC, IPS, ADF, PP, and Hadri in individual and trend intercept, both in level and 1st difference, and an order to remove inconvenience, stationary test rendering to cross-sectional in 1st generation unit root test Table 5 with a common root, afterward, Table 6 we computed the cointegration test by Pedroni, Kao from Engle-Granger based and Fisher performed before empirical valuation of FMOLS and DOLS. Figure-4. Dispersal of States. Table-3. Descriptive statistics. Variables Mean Median Max Min Sta.Dev. Obs RE 8.198 2.050 89.596 0.000 12.455 1551 C 6.428 4.403 67.106 0.016 7.712 1551 EP 2307.542 1571.034 21959.440 86.879 2495.740 1551 IVA 33.776 30.555 213.690 6.064 15.729 1551 GDPC 11387.490 4781.851 113682.000 237.814 15294.990 1551 CMIC 19.659 18.639 56.604 0.000 10.476 1551 FFE 78.096 86.045 100.000 1.654 23.276 1551 4.2. Panel Regression Analysis The endogenous explanatory variables do not contain any past value of variables in the static model, the term of error serially and mutual independent, therefore need to construct the dynamic model in form of endogenous lagged variables and serially correlated error (Gourieroux & Monfort, 1997) . The dynamic model can construct for the endogenous variables in form of lagged and DOLS and VECM estimations the lag of dependent variables are present as explanatory variables of the regression equation; therefore, the dynamic relationship shows the dynamic model. Figure-5. Residual indication of Renewable energy. In fact, these methods have become mainstream dynamic approaches of relevant studies (Emirmahmutoglu & Kose, 2011; Herrerias, Joyeux, & Girardin, 2013; Liddle & Lung, 2013; Nazlioglu & Karul, 2017). It should account for the dynamic relationship between the variables for when analyzing different individual variables. 1st variation of time matters in investigating the Granger causality among variables. 2nd the lagged effects of the policy or past information have a direct impact on changes of variables. 3rd the relationship among variables differ in long-run and short-run, which requires long and short-run estimations dynamic. Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 200 © 2020 by the authors; licensee Asian Online Journal Publishing Group Table-4. Unit root of individual variables (Level). Individual intercept Individual intercept and trend Variable les CR Individual root Hadri CR Individual root Hadri LLC IPS ADF PP LLC Breitung IPS ADF PP RE -45.080*** -14.656*** 277.840*** 319.731*** 8.521*** -41.624*** -1.614*** -11.936*** 474.523*** 262.236*** 8.89*** C 3.794 3.368 112.996 174.445*** 14.834*** -0.059 6.723 0.135 146.777*** 223.552*** 14.865*** EP 0.810 -1.926*** 246.709*** 170.106*** 22.294*** -8.443*** 7.479 -4.857*** 487.042*** 508.001*** 17.986*** IVA -3.237*** -2.356*** 130.172** 148.074*** 16.551*** 1.608 1.533 1.143 93.607 100.323 14.221*** GDPC 7.899 10.047 65.194* 44.285 28.143*** 2.296* 5.101 3.532 105.382* 88.320 12.065*** CMIC -4.590*** -2.781*** 165.828*** 198.960*** 20.745*** -0.893 0.579 -0.661 138.003*** 161.444*** 13.219*** FFE -23.219*** -6.324*** 143.533*** 173.255*** 27.012*** -41.316*** 5.086 -7.893*** 380.635*** 396.642*** 16.267*** Note: *** specifies the significance levels at 1% ** specifies the significance levels at 5% * specifies the significance levels at 10%. Table-5. Padroni Residual Co-Integration Test. Individual intercept Individual intercept and individual trend No intercept or trend Statistic Weighted Statistics Statistic Weighted Statistics Statistic Weighted Statistics Panel v-Statistic 0.008* -3.086* -1.631* -5.501 0.070* -3.046* Panel rho-Statistic 1.96* 2.228* 3.348* 5.065 2.174* 1.214* Panel PP-Statistic -4.021*** -6.899*** -4.824*** -6.202*** -2.898*** -6.858*** Panel ADF-Statistic -0.389* -4.415*** -1.056* -5.302*** -0.424* -3.354*** Alternative hypothesis: individual AR coefs. (between-dimension) Individual intercept Individual intercept and individual trend No intercept or trend roup rho-Statistic 5.809 7.890 4.518 Group rho-Statistic -13.954*** -12.480*** -12.698*** Group rho-Statistic -9.239*** -8.119*** -4.733*** Note: Specified with lag ength 1 with Newey-West automatic bandwidth (Bartlett Kernel). Kao Residual Co-integration (t-Statistics) are -8.199*** (ADF), 16.393 (Residual variance) and 10.111 (HAC Variance). Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 201 © 2020 by the authors; licensee Asian Online Journal Publishing Group Table-6. Johansen fisher panel co-integration test. Note: The variable’s definition stated in Table 2 *** specifies the significance levels at 1% ** specifies the significance levels at 5% * specifies the significance levels at 10%. The cointegration regression: Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS), emphasized by the maximum likelihood approach (Johansen, 1991). The nonstationary estimation specified the basic cointegration method in the triangular system of equation, the linear trend specification by a trend variable assumption. Fully changed OLS uses a semi-parametric correction to eliminate the problem caused by the long run correlation between the stochastic repressors and the integration equation. Hence, asymptotically unbiased by FMOLS and fully efficient mixture normal asymptotic allowing for standard Wald test using asymptotic statistically Chi-Square inference. Table 6 shows panel (FMOLS) liner trend specification, the cointegration regression estimate using by differenced data in an additional trend. The pool panel method specifies the long run variance computed by Bartlett in the Andrews Automatic Bandwidth method. Table 7 showed an Individual coefficient of Fully Modified Ordinary Least Squares (FMOLS) & Dynamic Ordinary Least Squares regression (DOLS) has computed with (@trend) specification. The Bartlett and Andrew automatic in long run covariance individually categorize countries by different regions. The ordinary least squares coefficient covariance using an estimation of the long run variance of FMOLS & DOLS residual in different regions and use a sandwich-style HAV (Newley-West) covariance. The individual coefficient examined the trend coefficient for each cross-section in individual regions. The Vector Error Correction (VEC) model explore seven variables, the cointegration equation (CoinEq), RE statistically significant coefficient related at -6.86 in cointegration term, the coefficient relating to Error Correction Term (ECM), its negative at 2.49% Table 8 implies that previous year errors will be correct in the following year at the change rate of 2.49%, the other difference operator it represents short-run coefficients, the co-integration equation or ECT capture the long-run equilibrium, it signified conversion to long-run equilibrium at -0.024, therefore show convergent to long run equilibrium so it analyzing that that in the short run relationship there is no significant coefficients in RE except EP ( lag -1). The short-run coefficient of is significant showing that short-run will exhibit reduction it’s on by self because its log representation every order relationship and transept are not significant. The contribution of C, EP, IVA, GDPC, CMIC, and FFE is strongly exogenous, and it implies that they have a very weak influence on predicting RE. The long-run period influence of RE on itself is the further remove while the influence of predictor variables is increasing as further remove, it means predictor are exhibiting strong endogenous influence on RE on a remove, strong endogenous influence while RE exhibiting weak endogenous influence on itself. In FFE the short run RE focus arrow variance, while C is 0.71% Figure 6 of focus arrow variance in FFE, both variables RE and C are exhibiting strong exogeneity, and they have a weak influence on predicting FFE short-run, we show that FFE is 98.53% as we go in future the influence of predictor variables (increasing) are very strong while the influence of FFE is decreasing the further removed. Figure 7 East Asia countries except for Australia, Japan, and Newzeland is a positive effect in high economic growth regime but forming negative effect on CO2 emission. These countries with weak infrastructure and limited financial resources will struggle to adapt and sustain their economic growth in the face of a changing climate, like Singapore showed high economic growth with less CO2 emission and Brunei showed emission from the above median line, India and China need to binding emission limit cause of climate catastrophe in Asia also in several decades to occur in developing countries.[1] The United Arab Emirates and Qatar (2nd highest ecological footprint globally) are increasing CO2 emission in the middle East, and Romania gradually decreased by 14.6% from fossil fuel combustion in 2012, Turkmenistan increased CO2 emission from 19 to 100 million from 1997 to 2016 with the 10.20% annual rate, Hungary and Ukraine are slightly going down in Europe and central Asia. Intercept in CE and VAR Intercept and trend in CE-no trend in VAR No. of CE(s) (from trace test) (from max-eigen test) (from trace test) (from max- eigen test) None 890.7*** 857.2*** 985.1*** 542.1*** At most 1 995.8*** 649.4*** 1505*** 2358*** At most 2 697.4*** 608.4*** 820.5*** 600*** At most 3 581.5*** 434.5*** 547.9*** 3481*** At most 4 424*** 309.9*** 498*** 375.6*** At most 5 209.7*** 176.8*** 356.3*** 282.6*** At most 6 155.4*** 155.4*** 148.7*** 148.7*** https://knoema.com/atlas/Australia/CO2-emissions-per-capita https://tradingeconomics.com/japan/gdp-per-capita https://climatepositions.com/climate-change-performance-australia-vs-new-zealand/ https://www.cogentoa.com/article/10.1080/23322039.2017.1379239 https://www.cogentoa.com/article/10.1080/23322039.2017.1379239 https://futureoflife.org/2016/08/05/developing-countries-cant-afford-climate-change/ https://futureoflife.org/2016/08/05/developing-countries-cant-afford-climate-change/ https://www.nccs.gov.sg/climate-change-and-singapore/national-circumstances/singapore's-emissions-profile https://en.actualitix.com/country/brn/brunei-darussalam-co2-emissions.php https://www.masterresource.org/climate-policy/india-co2-emissions-to-triple-by-2030/ https://www.masterresource.org/climate-policy/india-co2-emissions-to-triple-by-2030/ https://countryeconomy.com/energy-and-environment/co2-emissions/united-arab-emirates https://dohanews.co/global-report-warns-qatars-consumption-resources-unsustainble/ https://www.romania-insider.com/romania-sees-14-6-fall-in-co2-emissions-from-energy-use https://knoema.com/atlas/Turkmenistan/topics/Energy/Total-Energy/Carbon-dioxide-emissions https://www.indexmundi.com/facts/hungary/co2-emissions https://knoema.com/atlas/Ukraine/CO2-emissions-per-capita Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 202 © 2020 by the authors; licensee Asian Online Journal Publishing Group Table-7. Individual coefficient by regions. East Asia FMOLS DOLS South. A FMOLS DOLS Europe FMOLS DOLS Middle.E FMOLS DOLS Australia -27.27 (-0.43) -0.21(0.67) Bangladesh 2.79 (.24) 7.22(0.17) Albania 1.03 (-0.20) -14.82(-0.15) Bahrain -177.27 (0.01) Brunei Darussalam -16.83 (-0.06) India 4.70 (.19) 8.72(0.19) Armenia -19.47(0.28) -31.42(0.21) Egypt -136.18 (-0.62) -170.43(-1.09) China 5.73 (-0.27) 13.97(-0.36) Nepal -2.46 (-0.04) -1.57(-0.02) Azerbaijan -6.20 (0.51) 25.01(-1.02) Iran -165.92 (0.28) -216.18(0.20) Indonesia 5.79 (-0.16) 12.98(-0.16) Pakistan 2.36 (0.13) 5.95(0.07) Belarus -6.42 (-0.10) -54.67(0.18) Iraq -171.60 (0.75) -217.02(0.47) Japan -37.16 (-0.15) Bosnia H. -11.65(0.09) Israel -168.00 (0.21) Malaysia 29.12 (-0.88) 33.36(-0.62) Bulgaria -16.63 (0.29) -26.16(0.06) Jordan -164.51 (0.04) -220.36(0.12) Myanmar 34.70 (-0.79) Cyprus -13.90 (0.23) -35.46(-0.33) Kuwait New Zealand -19.59 (-0.28) 5.90(0.07) Czech P -11.47 (0.12) Lebanon -161.52 (0.24) Philippines -1.55 (-0.03) 3.97(-0.10) Estonia -0.11 (-0.00) Oman -164.39(0.19) Singapore -8.41 (-0.86) 7.05(0.13) Georgia -9.74 (0.06) -42.83(0.63) Qatar Thailand -1.41 (-0.13) 1.11(0.09) Greece -12.16 (0.18) Saudi -169.26(0.47) -233.74(0.64) Vietnam -0.35 (0.11) 11.67(-0.79) Hungary -11.44 (0.10) UAE -177.09(0.22) -261.56(1.15) Kazakhstan -17.24 (0.76) 5.54(-0.40) Yemen -116.61(-1.0) -91.83(-2.93) Latvia -6.68 (0.03) Lithuania -4.418 (-0.06) Macedonia, -19.17(0.34) -33.16(0.28) Montenegro -4.880 (-0.00) Poland -12.74(0.11) Romania -8.23 (-0.01) -28.86(0.01) Russian F -10.27 (0.45) -92.43(3.38) Serbia -8.71 (0.39) Slovak R -10.91 (0.09) Slovenia -12.87 (0.20) Turkey -8.82(-0.031) -25.68(-0.24) Turkmenistan Ukraine -17.52(0.37) -32.50(0.30) Uzbekistan -22.57 (0.95) 86.74(-4.69) Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 203 © 2020 by the authors; licensee Asian Online Journal Publishing Group Table-8. Panel causality results of all countries. Note: *** specifies the significance levels at 1% ** specifies the significance levels at 5% * specifies the significance levels at 10%. Countries Dependent variable Independent variables Long run Short run ECM ContEq All countries 2.949*** -0.287* 12.110*** 2.305** 1.768** -1.185* -6.863*** -0.024 2.692*** -1.256* 3.769*** 4.331*** -0.577* 1.080* 11.885*** -0.924* -2.267*** 0.401* -4.119*** 2.642*** 2.409*** -7.648 10.884*** 2.803*** -1.198* 5.168*** 0.346* 3.499*** -0.3113*** 2.800*** 5.900*** -4.732*** 6.284*** 0.646* 0.293* -0.569 1.805** -0.784* 3.088*** 1.025* 0.729* -0.449* 2.407*** -0.630* 0.858* 2.194*** 3.092*** 0.492* -0.841* 0.310*** East Asia 5.614*** -0.126* 2.174*** 5.482*** 0.761* -0.227* 0.363*** 0.000 6.461*** 0.289* 1.903*** 0.065* -1.755** -0.176* -2.211*** -0.909* -0.304* 1.262* 0.060* -3.087*** 0.213* 4.200*** 3.424*** 1.873** 0.785* 2.219** -1.478* 1.509* 4.430*** 7.265*** -0.841* -0.397* 2.594*** 3.20*** -0.691* 5.551* -0.295* -1.209* -2.692*** -1.350* 3.638*** -0.256* -1.462*** -0.196* -0.195* 0.053* 1.663** -0.780* -0.685* -0.7199*** South Asia -1.359* 1.591* 0.441* -0.181* 1.179* -2.548*** -3.922*** -0.130 -1.233* 5.333*** 1.608* 4.753*** -0.430* 2.262*** 0.129*** 1.815** 5.110*** -2.685*** 3.844*** 1.300* 4.259*** 0.624* 0.416* 1.639* -3.136*** 0.798* -3.082*** 1.277* -1.800*** 0.870* -0.496* 1.062* -3.586*** 0.189* -0.405* 3.533* -2.641*** 1.664** 3.929*** 1.188* -7.829*** -0.561* 3.332* -2.641*** 1.664** 3.929*** 1.188* -7.829 -0.561* -0.358*** Europe 0.036* -0.323* 3.968*** -0.810* -0.580* 2.021*** -0.117*** -0.000 0.249* 33.200*** 3.368*** 2.246*** -1.141* 0.313* 3.602*** -0.571* 32.046*** -1.767** 1.830** 2.974*** -0.128* 1.931* 5.035*** 3.944*** -1.858** 0.226* 1.293* 2.086*** 4.853*** -0.636* 1.853** 2.457*** 0.392* -1.785** 1.772** -1.032 -0.675* -1.595* 2.874*** 1.005* -1.828** 0.816* 0.386*** 1.752** 1.083* 0.353* 2.416*** 1.712** 1.712** 2.086*** Middle east 0.168* 0.876* 8.147*** 0.686* 0.249* 1.534* -5.003*** -0.113 -1.572* -2.652*** 3.723*** 2.766*** -1.233* 0.426* 6.883*** 0.153* -3.915*** -0.930* -2.083*** 2.384*** 0.282* -4.547* 7.107*** 1.840** -1.775** 2.085*** 2.166*** -0.845* -0.757*** 0.064* 3.214*** -1.593* 3.778*** -1.212* 0.103* 0.604 -0.184* 0.378* 3.620*** 3.569*** -0.802* 0.024* 2.100*** 1.365* 0.265* 0.349* -0.880* 0.077* -0.109* 0.351*** Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 204 © 2020 by the authors; licensee Asian Online Journal Publishing Group Figure-6. Variance decomposition (Cholesky). Figure-7. Distribution of region. Asian Journal of Economics and Empirical Research, 2020, 7(2): 193-206 205 © 2020 by the authors; licensee Asian Online Journal Publishing Group 5. Conclusions The mega project Belt and Road is a precisely re-presenting economic corporation, and it is a huge plan of development of 4 regions. In the 21stcentury it connected with the Maritime Silk Road from the China to Europe via Central Asia and then rapidly in-touch with other states as well for pure economic corridor. This paper panel data analysis implies that economic growth of 55 countries and their influence on energy consumption, renewable energy, manufacturing industry and construction with industry value added, fossil fuel and GDP per capita. The regional individualities of countries are different within BRI on behave economic contribution, the policies and recommendation of energy discussed separately in regions. It shows EA countries’ positive and high economic growth, and there is a causality running from GDP per capita, industry value added and renewable energy. In addition, Chinese enterprises have undertaken over 3,100 projects in Belt and road likes nuclear, hydraulic and biomass and also the country region-specific factors should be carefully finding the energy development approach for renewable energy such as hydropower projects in South Asia. Regarding South Asia-countries, the relationship between renewable energy consumption and economic growth is not the signification, there are causality running from GDP and likes India have created a negative impact on global warming. The United Arab Emirates (UAE) and Oman is less causality running from GDP and energy consumption is not significant in Europe and the central east. The Middle East & North Africa (ME&NA) region has undergone a significant transformation because of economic development and increase diversification of energy sources in an economic factor like Belt and Road project. The result implies that most import sources of economic development are traditional fossil energy rather than renewable energy and the global energy demand growing by an average 1% per year from 2010 and through 2040. The most important source of energy meet future demand growth is natural gas expected to largest absolute economic growth, and it became a key fuel in meeting future. BRI project may also reduce transit risk of energy sources by Pakistan via the Iran-Pakistan pipeline in Middle East and import natural gas to Western China through Pakistan and maintaining oil and gas pipelines and other transport routes. Construction of cross-value- added industries, power supply network, power-transmission routes can further promote 55 country’s economy in BRI project. 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