Empirical evidence of sustainable development: Causality relationship between economic growth and environmental degradation in Ecuador and Latin America and The Caribbean
Bibliographic record
Abstract
This article is an inductive argumentation and an empirical-analytical paradigm that evaluates the actual relationship between Gross Domestic Product (GDP) per capita and the Carbon Dioxide (CO2) in the case of Ecuador and to compare it with Latin America and the Caribbean within a period of analysis from 1960 to 2011. It was developed an Augmented Dickey-Fuller unit root (ADF), a Granger Causality Test and a Johansen Cointegration test. It was obtained a VAR model with two variables with a number of 14 lags – VAR2(14) which were tested for which were tested for causality by demonstrating a bidirectionality for Latin America and the Caribbean and a unidirectionality of GDP per capita to CO2 for the Ecuador. Keywords: economic growth, sustainable development, environmental economics. References [1]E. Urteaga, «Las teorías económicas del desarrollo sostenible,» Cuadernos de Economía, vol. 32, nº 89, pp.113-162, 2009. [2]G. Brundtland, «Our Common Future,» de Report of the World Commission on Environment and Development, 1987. [3]R. Bermejo, Del desarrollo sostenible según Brundtland a la sostenibilidad como biomimesis, Bilbao: Hegoa, 2014. [4]W. Beckerman, «Economists, scientists, and environmental catastrophe,» Oxford Economic Papers, vol. 24, nº 3, 1972. [5]G. Grossman and A. Krueger, «Economic Growth and the Environment,» The Quarterly Journal of Economics, vol. 110, nº 2, pp. 353-377, 1995. [6]J. y. A. Medina, «Ingreso y desigualdad: la Hipótesis de Kuznets en el caso boliviano,» Espacios, vol. 38, nº31, p. 23, 2017. [7]M. Ahluwalia, «Inequality, poverty and development, » Journal of Development Economics, nº 3, pp. 307-342, 1976. [8]A. y. R. D. Alesina, «Distributive politics and economic growth,» Quarterly Journal of Economics, vol. 109, nº 2, pp. 465-490, 1994. [9]R. Barro, «Inequality and growth in a panel of countries, » Journal of Economic Growth, vol. 5, nº 1, pp. 5-32, 2000. [10]M. A. Galindo, «Distribución de la renta y crecimiento económico,» de Anuario jurídico y económico escurialense, 2002, pp. 473-502. [11]A. Álvarez, «Distribución de la renta y crecimiento económico, Información Comercial Española, ICE,» Revista de economía, nº 835, pp. 95-100, 2007. [12]J. C. Núñez, «Crecimiento económico y distribución del ingreso: una perspectiva del Paraguay,» Población y Desarrollo, nº 43, pp. 54-61, 2016. [13]S. Kuznets, «Economic Growth and Income Inequality, » American Economic Review, nº 45, pp. 1-28, 1955. [14]J. A. y. C. J. Araujo, «Relación entre la desigualdad de la renta y el crecimiento económico en Brasil: 1995-2012.,» Problemas del desarrollo, vol. 46, nº 180, pp.129-150, 2015. [15]F. Correa, A. Vasco and C. Pérez, «La Curva Medioambiental de Kuznets: Evidencia Empírica para Colombia Grupo de Economía Ambiental (GEA),» Semestre Económico, vol. 8, nº 15, pp. 13-30, 2005. [16]M. Heil and T. Selden, «Carbon emissions and economic development: future trajectories based on historical experience,» Environment and Development Economics, vol. 6, nº 1, pp. 63-83, 2001. [17]D. Holtz-Eakin and T. Selden, «Stoking the fires? CO2 emissions and economic growth,» Journal of Public Economics, pp. 85-101, 1995. [18]D. STERN, «Progress on the environmental Kuznets curve?,» Environment and Development Economics, vol. 3, nº 2, pp. 173-196, 1998. [19]P. Ekins, «The Kuznets curve for the environment and economic growth: examining the evidence,» Environment and Planning, vol. 29, pp. 805-830, 1997. [20]W. Moomaw and G. Unruh, «Are Environmental Kuznets Curves Misleading us?,» de Fletcher School of Law & Diplomacy, 1997. [21]S. M. Bruyn, J. Van- Den- Bergh and J. Opschoor, «Economic growth and emissions: reconsidering the empirical basis of environmental Kuznets curves,» Ecological Economics, pp. 161-175, 1998. [22]B. Friedl and M. Getzner, «Determinants of CO2 Emissions in a small open Economy,» Ecological Economics, vol. 45, nº 1, pp. 133-148, 2003. [23]T. Sheldon, «Carbon emissions and economic growth: A replication and extension,» Energy Economics, vol. 82, pp. 85-88, 2007. [24]B. Huang, M. Hwang and C. Yang, «Causal relationship between energy consumption and GDP growth revisited: A dynamic panel data approach,» Ecological Economics, vol. 67, nº 1, pp. 41-54, 2008. [25]J. He and P. Richard, «Environmental Kuznets curve for CO2 in Canada,» Ecological Economics, vol. 69, nº5, pp. 1083-1093, 2010. [26]S. Dinda, «Environmental Kuznets Curve Hypothesis: A Survey,» Ecological Economics, vol. 49, nº 4, pp. 431-455, 2004. [27]J. M. B. and T. T. Fosten, «Dynamic misspecification in the environmental Kuznets curve: Evidence from CO2 and SO2 emissions in the United Kingdom,» Ecological Economics, vol. 76, pp. 25-33, 2012. [28]K. Ahmed, M. Shahbaz, A. Qasing and W. Long, «The linkages between deforestation, energy and growth for environmental degradation in Pakistan,» Ecological Indicators, vol. 49, pp. 95-103, 2014. [29]J. Wooldridge, Introducción a la Econometría Un Enfoque Moderno. 4ª ed., Mexico D.F.: Cengage Learning, 2010.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".