Exploring the environmental Kuznets curve for CO2 and SO2 for Southeast Asia in the 21st century context
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DOIhttp://dx.doi.org/10.21511/ee.09(1).2018.01
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Article InfoVolume 9 2018, Issue #1, pp. 7-21
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This study aims to investigate the relationships between economic development and environmental degradation regarding the emissions of CO2 and SO2 in Southeast Asia (SEA). The pooling data consist of 10 countries, Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Singapore, the Philippines, Thailand, and Vietnam, in the period 2003-2012. Furthermore, income elasticity of CO2 and SO2 emissions is computed for each country to observe the sensitivity of environmental degradation through the emissions of CO2 and SO2 brought by economic development.
The results indicate that CO2 displays an inverted U-shape pattern, whereas SO2 has decreased at an increasing rate since 2003. It is expected that SO2 will increase as the SEA economies further develop. The turning points for both CO2 and SO2, indicate that the current SEA income level has not reached the turning point. The income elasticities show that income elasticities for CO2 are positive for all 10 countries. Both Singapore and Malaysia are classified as countries with high income. However, Singapore, with 0.64%, has the highest income elasticity, and Malaysia, with 0.15%, has the second lowest. There is no indication that wealthy countries have a significant impact on CO2 through economic development. Income elasticities for SO2 of each country are all negative. This suggests that SO2 is an inferior good. Brunei, with 8.41%, has the most sensitivity toward change in SO2 emissions, whereas Myanmar, with only 0.58%, is the least sensitive to SO2 emissions.
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JEL Classification (Paper profile tab)O13, O53, Q56
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References42
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Tables8
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Figures4
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- Fig. 1. The map of SEA region
- Fig. 2. Different scenarios of environmental Kuznets curve
- Fig. 3. Comparison of CO2 emissions for high- and low-income countries in SEA
- Fig. 4. Comparison of high- and low-income countries for SO2 emissions in SEA
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- Table 1. General economic indicators of each country in SEA
- Table 2. Definition and descriptive statistics of the variable for estimation
- Table 3. Comparison of fixed- and random-effect models for CO2 emissions and GDP
- Table 4. Comparison of fixed- and random-effect models for SO2 emissions and GDP
- Table 5. Comparison of income differentiation for CO2 in SEA
- Table 6. Comparison of income differentiation for SO2 emissions in SEA
- Table 7. Income elasticity of CO2 emissions for individual countries in SEA
- Table 8. Income elasticity of SO2 emissions for individual countries in SEA
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- Abella, J. A. & Bayacag, P. (2013). Environment, health, and economic growth in the Philippines. Philippines Agricultural Economics and Developmental Association. University of Southeastern Philippines, Bo. Obrero, Davao City.
- Anand, N. (2014). An overview of Indian economy (1991-2013). Journal of Economics and Finance, 3(3), 19-24.
- Azam, M., & Khan, A. Q. (2016). Testing the environmental Kuznets curve hypothesis: A comparative empirical study for small, lower, middle, upper middle and high-income countries. Renewable and Sustainable Energy Reviews, 63(1), 556-567.
- Bo, S. (2011). A literature survey on environmental Kuznets curve. Energy Procedia, 5(1), 1322-1325.
- Bouzanis, A. (2017). Economic snapshot for ASEAN. Focus Economics.
- Carson, R.T. (2009). The environmental Kuznets curve: Seeking empirical regularity and theoretical structure. Review of Environmental Economics and Policy, 4(1), 3-23.
- Chakravarty, S.K., Ghosh, C.P., Suresh, A.N, Dey and Gopa Shukla, okia, C. A. (2012). Deforestation: causes, effects, and control strategies, global perspectives on sustainable forest management.
- Dasgupta, S., Laplante, B., & Wheeler, D. (2002). Confronting the environmental Kuznets curve. The Journal of Economic Perspective, 16(1), 147-168.
- Disli, M., Ng, A., & Askari, H. (2016). Culture, income, and CO2 emission. Renewable and Sustainable Energy Review, 62(1), 418-428.
- Food and Agricultural Organizations of the United Nations (2010). Global forest resources assessment. Rep. No. 163. Rome: Food and Agriculture Organization of the United Nations.
- Frankel, J., & Orszag, P. (2001). Retrospective on American economic policy in the 1990s. Cambridge, Massachusetts: MIT Press.
- Free World Maps (2005). South East Asia political map.
- Egli, H. (2001). Are cross-country studies of the environmental Kuznets curve misleading? New evidence from time series data for Germany. Ernst-Moritz-Arndt University of Greifswald. Discussion Paper 10/2001. University of Greifswald, Greifswald, Germany.
- International Energy Agency (2015). Southeast Asia Energy Outlook of Southeast Asia 2015: World energy outlook special report. France: International Energy Agency.
- Grossman, G., & Krueger, A. (1991). Environmental impacts of a North American free trade agreement. National Bureau of Economic Research (Working Paper No. 3914). Cambridge, Massachusetts: MIT Press.
- Grossman, G., & Kruger, A. (1995). Economic growth and environment. The Quarterly Journals of Economics, 110(2), 353-377.
- Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy, 37(12), 5167-5172.
- Katircioglu, S. T., Feridun, M., & Kilinc, C. (2014). Estimating tourism-induced energy consumption and CO2 emissions: The case of Cyprus. Renewable and Sustainable Energy Reviews, 29(1), 634-640.
- Millimet, D. L., List, J. A., & Stengos, T. (2003). The environmental Kuznets curve: Real progress or misspecified models? Review of Economics and Statistics, 85(4), 1038-1047.
- Napoli, C. (2013). Growing green: A comparison across Southeast Asia. (Working Paper Prepared for the Nottingham GEP). University of Nottingham, Kuala Lumpur, Malaysia.
- Nourdhaus, W. D. (2007). Economic theory of technological change theory of innovation. The American Economic Review, 59(2), 18-28.
- Ranveer, A. C., & Latake, P. T. (2015). The greenhouse effect and its impacts on environment. International Journal of Innovative Research and Creative Technology, 1(3), 333-337.
- Saboori, B., Sulaiman, J., & Mohd, S. (2012). Economic growth and CO2 emissions in Malaysia: A cointegration analysis of the evironmental Kuznets curve. Energy Policy, 51(1), 184-191.
- Sulemana, I., Harvey, S., & Rikoon, S. (2017). Environmental Kuznets curves for air pollution in African and developed countries: Exploring turning point incomes and the role of democracy. Journal of Environmental Economics and Policy, 6(2), 134-152.
- Sugiawan, Y., & Managi, S. (2016). The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy, 98(1), 187-198.
- Sinha, A., & Bhatt, M. (2017). Environmental Kuznets curve for CO2 and NO2 emissions: A case study of India. European Journal of Sustainable Development, 6(1), 267-276.
- Stern, D .I. (2004a). Economic growth and energy. Encyclopedia of Energy, 2(1), 35-51.
- Stern, D. I. (2004b). The rise and fall of the environmental Kuznets curve. World Development, 32(8), 1419-1439.
- Taguchi, H. (2012). The environmental Kuznets curve in Asia: The case of sulphur and carbon emissions. Asia-Pacific Development Journal, 19(2), 77-92.
- Torras, M., & Boyce, J. K. (1998). Income, inequality, and pollution: A reassessment of the environmental Kuznets curve. Ecological Economics, 25(1), 147-160.
- The World Bank, World Development Indicators (2003‒2012a). CO2 emissions (metric tons per capita). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012b). SO2 emissions (metric tons per capita). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012c). GDP per capita (US$ current Thousand). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012d). GDP deflator (base year varies by country). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012e). Population, total. Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012f). Trade (% of GDP). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- The World Bank, World Development Indicators (2003‒2012g). Energy intensity level of primary energy (MJ/$2011 PPP GDP). Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam. The World Bank Development Data Group.
- Wayman, E. (2012). The oldest human fossils in Southeast Asia. Smithsonian Magazine.
- World Health Organization (2004). The World Health Report 2004. Switzerland: World Health Organization.
- Wooldridge, J. M. (2009). Introductory Econometrics: A modern approach (5th International Edition). Ohio, US: South-Western, Cengage Learning.
- Utari, G. A, & Cristina, R. (2015). Growth and inequality in Indonesia: Does Kuznets curve hold? Journal of Modern Accounting and Auditing, 11(2), 93-111.
- Zhang, Y. (2011). Scale, technique, and composition effects in trade-related carbon emissions in China. Environtal Resource Economics, 51(1), 371-389.
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The nexus between foreign direct investment and environmental sustainability in North Africa
Environmental Economics Volume 9, 2018 Issue #1 pp. 57-68 Views: 2175 Downloads: 442 TO CITE АНОТАЦІЯThis paper provides a study of the relationship between sustainable development and foreign direct investment (FDI) from an empirical point of view in the case of the North African countries during the period from 1985 to 2005. We use the cointegration test, the FMOLS (Fully Modified Ordinary Least Squares) model and the Granger causality test to examine this relationship. According to the empirical results, we confirm the existence of a cointegration relationship between the different series studied in this paper. Based on the cointegration test we can use the error correction model. Also, to test the effect of FDI on sustainable development in the North African countries, we make an estimate by FMOLS method. We found that the foreign direct investment has a positive impact on CO2 emissions. Also, the Granger Causality test confirms the presence of a bidirectional relationship between FDI and CO2 emissions (Carbon dioxide). That is to say, the FDI can cause CO2 emissions and CO2 emissions can cause FDI based on the Granger causality.
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The impact of Working Capital Management on shareholders’ wealth and profitability: evidence from Colombo Stock Exchange
Wasantha Perera
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Pradeep Priyashantha
doi: http://dx.doi.org/10.21511/imfi.15(2).2018.10
Investment Management and Financial Innovations Volume 15, 2018 Issue #2 pp. 104-115 Views: 2116 Downloads: 424 TO CITE АНОТАЦІЯThe Working Capital Management (WCM) has an important role for the firm’s success or failure, because it directly affects the overall business health of the firm. This study examined the impact of WCM on profitability and shareholders’ wealth using 50 companies listed in different sectors on the Colombo Stock Exchange (CSE) for the period from 2010 to 2015. This sample represents 47% of the selected sectors of CSE. The profitability of the company is measured using gross operating profit (GOP) and shareholders wealth measured by Tobin’s Q (TQ) ratio. The WCM is measured using five independent variables namely stock holding period (SHP), debtors’ collection period (DCP), creditors’ settlement period (CSP), cash conversion circle (CCC) and current assets ratio (CAR). Further, three additional variables such as firm size (SIZE), leverage (LEV) and earning yield (EY) are employed as controlling variables to capture the impact of other performance of the companies.
The data were analyzed using ordinary least square (OLS) and panel data regression models. These regression models reveal that there is a significant negative relationship between CCC and dependent variables (GOP & TQ). Further, this relationship has been confirmed by the major components of CCC such as SHP, DCP. Firm size also positively and significantly effects on the firm GOP while negatively effects on the TQ. Further, they revealed that there is a significant positive relationship between LEV and TQ. The study finds that the shareholders’ wealth and profitability can be increased through the efficiency of WCM. -
The role of foreign direct investment and trade on carbon emissions in Turkey
Gizem Kaya , M. Özgür Kayalica 
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Merve Kumaş ,
Burc Ulengin
doi: http://dx.doi.org/10.21511/ee.08(1).2017.01
Environmental Economics Volume 8, 2017 Issue #1 pp. 8-17 Views: 1912 Downloads: 959 TO CITE АНОТАЦІЯThis study aims to observe the long run and short run effects of gross domestic product, foreign direct investment inflows and trade on CO2 emissions and causality relationships between these factors, using annual data for the period of 1974-2010. The empirical results demonstrate that the inverted U-shaped relationship of environmental Kuznets curve is valid for Turkey. In addition, there are positive long run effects of foreign direct investment and trade openness on CO2 emissions. The authors also find a bidirectional causality relationship between CO2 emission and FDI.
