The role of financial openness and China’s income on fossil fuels consumption: fresh evidence from Latin American countries
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The impact of China’s financial openness and economic growth on consumption of fossil fuels was analyzed for a panel of ten Latin American countries, throughout 34 years (from 1980 to 2014). An autoregressive distributed lag in the form of unrestricted error correction model was used as the chosen econometric methodology. The results showed that the financial openness, as well as the economic growth of China, increase the fossil fuels consumption in the long-run, while the economic growth of Latin American countries increases the energy use in the short- and the long-run. Moreover, the empirical findings of this research have significant consequences to the local government’s appraisal of the relationship between financial openness, China’s economic growth and consumption of fossil fuels.
KeywordsFossil fuels consumption Financial openness Economic growth China
This study was funded by NECE-Research Unit in Business Science and Economics, sponsored by the FCT-Portuguese Foundation for the Development of Science and Technology, Ministry of Science, Technology and Higher Education, Project UID/GES/04630/2019.
Compliance with ethical standards
Conflict of interest
All the authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
- Baltagi, B. H. (2008). Econometric analysis of panel data (4th ed.). Chichester: Wiley.Google Scholar
- Balza, L. H., Espinasa, R., & Serebrisky, T. (2015). Lights on: Energy needs in Latin America and the Caribbean to 2040 (pp. 1–39). Inter-American Development Bank (IDB). https://publications.iadb.org/handle/11319/7361. Accessed 21 Sept 2018.
- Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: Wiley.Google Scholar
- Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239–253.Google Scholar
- CJA (The Center for Justice & Accountability). (2017). Peru: The struggle for accountability: Civil war atrocities. http://cja.org/where-we-work/peru/. Accessed 26 Sept 2018.
- ECLAC (Economic Commission for Latin America and the Caribbean report). (2008). Latin America risks reverting progress in poverty reduction. United Nations (UN), 59, 1–8. http://hdl.handle.net/10986/26686. Accessed 10 Aug 2018.
- Engle, R., & Granger, G. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55(25), 251–276.Google Scholar
- Granger, C. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 28(1), 121–130.Google Scholar
- Greene, W. (2002). Econometric analysis. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
- Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.Google Scholar
- IEA (International Energy Agency). (2018). Oil market report (p. 1.26). Paris: International Energy Agency.Google Scholar
- ILO (International Labour Organization). (2013). Facing the crisis in Europe: Reflections on the case of Peru (pp. 1–4). http://www.ilo.org/americas/publicaciones/observatorio-de-la-crisis/WCMS_206263/langen/index.htm. Accessed 17 Aug 2018.
- INDEC (Instituto Nacional de Estadística y Censos). (2017). Dirección Nacional de Cuentas Nacionales. http://www.indec.gob.ar/nivel3_default.asp?id_tema_1=3&id_tema_2=9. Accessed 28 Sept 2018.
- IRENA (International Renewable Energy Agency). (2016). Renewable energy market analysis: Latin America (pp. 1–260). New York: IRENA.Google Scholar
- Johansen, J., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.Google Scholar
- Jurado, M. (2018). Latin America’s oil trade flow. Energia16. https://www.energia16.com/latin-americas-oil-trade-flow/?lang=en. Accessed 12 June 2018.
- Koengkan, M. (2017). Is the globalization influencing the primary energy consumption? The case of Latin America and the Caribbean countries. Cadernos UniFOA, Volta Redonda, 12(33), 59–69. e-ISSN: 1982-1816.Google Scholar
- Nkoro, E., & Uko, A. K. (2016). Autoregressive distributed lag (ARDL) cointegration technique: Application and interpretation. Journal of Statistical and Econometric Methods, 5(4), 63–91. ISSN: 1792-6602.Google Scholar
- OVER (Office of Evaluation and Oversight). (2012). Country program evaluation: Nicaragua 2008–2012 (pp. 1–69). Inter-American Development Bank (IDB). https://publications.iadb.org/handle/11319/5812. Accessed 18 Jan 2018.
- Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge working papers in economics, n. 0435. The University of Cambridge, Faculty of Economics. https://doi.org/10.17863/CAM.5113.
- Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of American Statistical Association, 94(446), 621–634. https://www.jstor.org/stable/2670182. Accessed 10 Feb 2018.
- Rafindadi, A. A., & Ozturk, I. (2016). Dynamic effects of financial development, trade openness and economic growth on energy consumption: Evidence from South Africa. International Journal of Energy Economics and Policy, 7(3), 74–85. ISSN: 2146-4553.Google Scholar
- Tissot, R. (2012). Latin America’s energy future (pp. 1–46). Inter-American Development Bank (IDB). https://publications.iadb.org/handle/11319/5709?locale-attribute=en&locale-attribute=es&. Accessed 24 Apr 2018.
- UCDP (Uppsala Conflict Data Program). (2017). http://www.pcr.uu.se/research/ucdp/program_overview. Accessed 23 July 2018.
- Verbeek, M. A. (2008). Guide to modern econometrics (3rd ed.). New York: Wiley.Google Scholar
- Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: The MIT Press.Google Scholar