C02 emissions, urbanization, consumption of electricity and human capital, a cointegration analysis for panel data worldwide from 1986 to 2016
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References
Acheampong, A. O. (2018). Economic growth, CO2 emissions and energy consumption: What causes what and where?. Energy Economics, 74, 677-692. [2] Adom, P. K., Kwakwa, P. A., & Amankwaa, A. (2018). The long-run effects of economic, demographic, and political indices on actual and potential CO 2 emissions. Journal of environmental management, 218, 516-526. [3] Ahmed, K., & Long, W. (2012). Environmental Kuznets curve and Pakistan: an empirical analysis. Procedia Economics and Finance, 1, 4-13. [4] Ahmed, K., Rehman,M. U., & Ozturk, I. (2017). What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian Countries. Renewable and Sustainable Energy Reviews, 70, 1142-1153. [5] Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. [6] Alam, M. M., Murad, M. W., Noman, A. H. M., & Ozturk, I. (2016). Relationships among carbon emissions, economic growth, energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia. Ecological Indicators, 70, 466-479. [7] Al-mulali, U. (2012). Factors affecting CO2 emission in the Middle East: A panel data analysis. Energy, 44(1), 564-569. [8] Alvarado, M. & Ortiz, C. (2018). El rol del capital humano en los ingresosde las provincias de Ecuador. Revista Vista Económica, Vol.4, 120-129. [9] Alvarado, R., & Atienza, M. (2014). The role of market access and human capital in regional wage disparities: Empirical evidence for Ecuador (No. 50). Universidad Católica del Norte, Chile, Department of Economics. [10] Alvarado, R., Ponce, P., Criollo, A., Córdova, K., & Khan, M. K. (2018). Environmental degradation and real per capita output: New evidence at the global level grouping countries by income levels. Journal of Cleaner Production, 189, 13-20. [11] Alvarado, R., Ponce, P., Alvarado, R., Ponce, K., Huachizaca, V., &Toledo, E. (2019). Sustainable and non-sustainable energy and output in Latin America: A cointegration and causality approach withpanel data. Energy Strategy Reviews, 26, 100369. [12] Balaguer, J., & Cantavella, M. (2018). The role of education in the Environmental Kuznets Curve. Evidence from Australian data. Energy Economics, 70, 289-296. [13] Banco Mundial (2018). World Development Indicators. Washington D:C. Disponible en línea. [14] Behera, S. R., & Dash, D. P. (2017). The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region. Renewable and Sustainable Energy Reviews, 70, 96-106. [15] Boamah, K. B., Du, J., Boamah, A. J., & Appiah, K. (2018). A study on the causal effect of urban population growth and international trade on environmental pollution: evidence from China. Environmental Science and Pollution Research, 25(6), 5862-5874. [16] Breitung, J. (2001). The local power of some unit root tests for panel data. In Nonstationary panels, panel cointegration, and dynamic panels (pp. 161-177). Emerald Group Publishing Limited. [17] Cai, Y., Sam, C. Y., & Chang, T. (2018). Nexus between clean energy consumption, economic growth and CO2 emissions. Journal of Cleaner Production, 182, 1001-1011. [18] Cruz, J., & Maldonado, L. (2017). Incidencia del ingreso familiar y la educación en el acceso a la canasta básica familiar en Ecuador. Revista Vista Económica, Vol.3, 19-31. [19] Cumbicus,M., & Tillaguango, B. (2017). Efecto del capital humano en la desigualdad: evidencia empírica para 17 países de América Latina. Revista Vista Económica, Vol.3, 53-62. [20] Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057-1072. [21] Ding, Y., & Li, F. (2017). Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China’s provincial regions. Energy, 125, 533-542. [22] Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger noncausality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460. [23] Enders, W. (2008). Applied econometric time series. John Wiley Sons. [24] Franco, S., Mandla, V. R., & Rao, K. R. M. (2017). Urbanization, energy consumption and emissions in the Indian context A review. Renewable and Sustainable Energy Reviews, 71, 898-907. [25] Godoy, J. (2017). Dinero electrónico y su afección en el capital humano: visión regional en Ecuador. Revista Vista Económica, Vol.3, 74-86. [26] Godoy, J. (2018). Urbanización e industrialización en Ecuador. Revista Vista Económica, Vol.4, 46-57. [27] Granger, C. W. (1988). Causality, cointegration, and control. Journal of Economic Dynamics and Control, 12(2-3), 551-559. [28] Guarnizo, S. (2018). Relación entre capital humano y crecimiento económico de Colombia. Revista Vista Económica, Vol.4, 19-31. [29] Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the econometric society, 1251-1271. [30] Ito, K. (2017). CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. International Economics, 151, 1-6. [31] Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 1551-1580. [32] 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, 97-103. [33] Klein-Banai, C., & Theis, T. L. (2013). Quantitative analysis of factors affecting greenhouse gas emissions at institutions of higher education. Journal of Cleaner Production, 48, 29-38. [34] Lee, J. W., & Lee, H. (2016). Human capital in the long run. Journalof Development Economics, 122, 147-169. 35] León, J. (2018). Relación entre el capital humano y el crecimientoeconómico en Bolivia, mediante técnicas de cointegración. Revista Vista Económica, Vol.4, 94-106. [36] Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24. [37] Li, X., Tan, H., & Rackes, A. (2015). Carbon footprint analysis of student behavior for a sustainable university campus in China. Journal of Cleaner Production, 106, 97-108. [38] Meng, J., Liu, J., Guo, S., Li, J., Li, Z., & Tao, S. (2016). Trend and driving forces of Beijing’s black carbon emissions from sectoral perspectives. Journal of Cleaner Production, 112, 1272-1281. [39] Meng, M., Jing, K., & Mander, S. (2017). Scenario analysis of CO2 emissions from China’s electric power industry. Journal of cleaner production, 142, 3101-3108. [40] Mirzaei, M., & Bekri, M. (2017). Energy consumption and CO2 emissions in Iran, 2025. Environmental research, 154, 345-351. [41] Mora, E. (2017). ¿Es importante el gasto público para aumentar el capital humano a nivel global mediante la aplicación de datos de panel? Revista Vista Económica, Vol.3, 42-52. [42] Olaya, E. (2017). Efectos del gasto en investigación y desarrollo en el ingreso de los establecimientos de Ecuador. Revista Vista Económica, Vol.3, 7-18.[43] Ouyang, X., & Lin, B. (2017). Carbon dioxide (CO2) emissions during urbanization: a comparative study between China and Japan. Journal of cleaner production, 143, 356-368. [44] Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670. [45] Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary panels, panel cointegration, and dynamic panels (pp. 93-130). Emerald Group Publishing Limited. [46] Pesaran,M. H., & Shin, Y. (1998). An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31, 371-413. [47] Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326. [48] Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. [49] Ponce, P., & Alvarado, R. (2019). Air pollution, output, FDI, trade openness, and urbanization: evidence using DOLS and PDOLS cointegration techniques and causality. Environmental Science and Pollution Research, 26(19), 19843-19858. [50] Poumanyvong, P., & Kaneko, S. (2010). Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics, 70(2), 434-444. [51] Rafiq, S., Salim, R., & Nielsen, I. (2016). Urbanization, openness, emissions, and energy intensity: a study of increasingly urbanized emerging economies. Energy Economics, 56, 20-28. [52] Raggad, B. (2018). Carbon dioxide emissions, economic growth, energy use, and urbanization in Saudi Arabia: evidence from the ARDL approach and impulse saturation break tests. Environmental Science and Pollution Research, 25(15), 14882-14898. [53] Robinson, O., Kemp, S., &Williams, I. (2015). Carbon management at universities: a reality check. Journal of Cleaner Production, 106, 109-118. [54] Salahuddin,M., Alam, K., Ozturk, I., & Sohag, K. (2017). The effects of electricity consumption, economic growth, financial development and foreign direct investment on CO2 emissions in Kuwait. Renewable and Sustainable Energy Reviews. [55] Sarango, D. (2018). Análisis de la relación entre el consumo de energía y las emisiones de carbono en Ecuador. Revista Vista Económica, Vol.4, 32-45. [56] Shahbaz,M., Loganathan, N.,Muzaffar, A. T., Ahmed, K., & Jabran, M. A. (2016). How urbanization affects CO2 emissions inMalaysia? The application of STIRPAT model. Renewable and Sustainable Energy Reviews, 57, 83-93. [57] Versteijlen, M., Salgado, F. P., Groesbeek, M. J., & Counotte, A. (2017). Pros and cons of online education as a measure to reduce carbon emissions in higher education in the Netherlands. Current opinion in environmental sustainability, 28, 80-89. [58] Wang, P., Wu, W., Zhu, B., & Wei, Y. (2013). Examining the impactfactors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China. Applied Energy, 106, 65-71. [59] Wang, Q., Su, M., Li, R., & Ponce, P. (2019). The effects of energy prices, urbanization and economic growth on energy consumption per capita in 186 countries. Journal of cleaner production, 225, 1017-1032. [60] Wang, Q., Zeng, Y. E., &Wu, B.W. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and sustainable energy reviews, 54, 1563-1579. [61] Wang, S., Li, G., & Fang, C. (2017). Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renewable and Sustainable Energy Reviews. [62] Martínez-Zarzoso, I., & Maruotti, A. (2011). The impact of urbanization on CO2 emissions: evidence from developing countries. Ecological Economics, 70(7), 1344-1353. [63] Wang, Y., Chen, L., & Kubota, J. (2016). The relationship between urbanization, energy use and carbon emissions: evidence from a panel of Association of Southeast Asian Nations (ASEAN) countries. Journal of Cleaner Production, 112, 1368-1374. [64] Wang, Y., Chen,W., Kang, Y., Li,W., & Guo, F. (2018). Spatial correlation of factors affecting CO 2 emission at provincial level in China: A geographically weighted regression approach. Journal of Cleaner Production, 184, 929-937. [65] Wang, Y., & Zhao, T. (2018). Impacts of urbanization-related factors on CO2 emissions: Evidence fromChina’s three regionswith varied urbanization levels. Atmospheric Pollution Research, 9(1), 15-26. [66] Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and statistics, 69(6), 709-748. [67] Wooldridge, J. M. (1991). On the application of robust, regressionbased diagnostics tomodels of conditional means and conditional variances. Journal of econometrics, 47(1), 5-46. [68] Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press. [69] Wu, Y., Shen, J., Zhang, X., Skitmore, M., & Lu, W. (2016). The impact of urbanization on carbon emissions in developing countries: a Chinese study based on the U-Kaya method. Journal of Cleaner Production, 135, 589-603. [70] Xu, S. C., He, Z. X., & Long, R. Y. (2014). Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI. Applied Energy, 127, 182-193. [71] Zhang, N., Yu, K., & Chen, Z. (2017). How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis. Energy Policy, 107, 678-687. [72] Zhang, Q., Yang, J., Sun, Z., & Wu, F. (2017). Analyzing the impact factors of energy-related CO2 emissions in China: What can spatial panel regressions tell us?. Journal of Cleaner Production, 161, 1085-1093.