Environmental Proxies and Methodological Biases in the Environmental Kuznets Curve. Critiques of the Use of Temperature in the Model Proposed by Erazo-Camacho et al. (2024)
DOI:
https://doi.org/10.18800/kawsaypacha.202501.D008Keywords:
Environmental Kuznets Curve, Climate change, Econometric methodology, Environmental economics, Environmental public policyAbstract
The Environmental Kuznets Curve (EKC) is widely used to examine the relationship between economic growth and environmental degradation. However, its empirical application critically depends on the selection of appropriate indicators. This article presents a methodological critique of the article «Peru's Environmental Kuznets Curve 2010-2020: A Departmental View» written by Erazo-Camacho et al. (2024), which employs average temperature as a proxy for environmental degradation in an econometric model applied to Peru. It is argued that this choice introduces methodological biases that undermine the validity of its conclusions, particularly the claim that an increase in per capita GDP reduces regional temperatures. This conclusion contradicts established scientific literature (IPCC, 2023), which links fossil fuel-based economic growth and extractive expansion to increased greenhouse gas (GHG) emissions. Through a literature review, data analysis, and conceptual discussion, the article proposes that more suitable indicators for the EKC in extractive economies include sectoral CO? emissions, deforestation, and water pollution. The study concludes that the use of inadequate proxies not only distorts empirical analysis but also risks leading to flawed environmental policy decisions, misrepresenting the true effects of economic growth on the environment.
Downloads
References
Alvarez-Berríos, N.; Aide, T. M. & López-Marrero, T. (2021). Adapting the Environmental Kuznets Curve to island ecosystems: Hyperlocal indicators for Puerto Rico. Ecological Economics, 189, 107171. https://doi.org/10.1016/j.ecolecon.2021.107171
Anselin, L. (2005). Spatial econometrics. En T. C. Mills & K. Patterson (Eds.). Palgrave Handbook of Econometrics (pp. 901-939). Palgrave Macmillan.
Bebbington, A. (2007). Minería y Desarrollo en el Perú, Con Especial Referencia al Proyecto Río Blanco, Piura. Oxfam Internacional.
Bebbington, A. & Bury, J. (2013). Subterranean Struggles: New Dynamics of Mining, Oil, and Gas in Latin America. University of Texas Press.
Bhattarai, M. & Hammig, M. (2001). Institutions and the Environmental Kuznets Curve for deforestation: A cross-country analysis for Latin America, Africa, and Asia. World Development, 29(6), pp. 995-1010. https://doi.org/10.1016/S0305-750X(01)00019-5
Cai, W.; Borlace, S.; Lengaigne, M.; van Rensch, P. & Collins, M. (2014). Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4(2), pp. 111-116. https://doi.org/10.1038/nclimate2100
CEPAL (2022). La transición energética y la resiliencia climática: Catalizadores del crecimiento y la inclusión. https://www.cepal.org/es/articulos/2022-la-transicion-energetica-la-resiliencia-climatica-catalizadores-crecimiento-la
CERES-NASA (2023). Dataset de radiación solar para América del Sur. https://ceres.larc.nasa.gov/data/
Defensoría del Pueblo del Perú (2023). Reporte mensual de conflictos sociales N° 234. https://www.defensoria.gob.pe/conflictos-sociales/
Erazo-Camacho, M. R.; Cubos-Sifuentes, U.; Mejia-Avalos, M. R. & Ramirez-Santana, A. L. (2024). La Curva de Kuznets Ambiental de Perú 2010-2020: una visión departamental. Revista Kawsaypacha: Sociedad Y Medio Ambiente, (14), D-005. https://doi.org/10.18800/kawsaypacha.202402.D005
Estrada, Y.; Guerrero, L.; Sisniegas, P.; Valdivia, G.; Vega, C.; Fernandez, L. E. & Moreno Brush, M. (2023). Distribución y transporte del mercurio en la cuenca Madre de Dios, Amazonía peruana: Influencia de los sedimentos y la hidrodinámica. Proyecto River Mining (PEER 8-235). https://www.planetgold.org/sites/default/files/CINCIA.%202023.%20Distribucion%20y%20transporte%20del%20mercurio%20en%20la%20cuenca%20Madre%20de%20Dios%2C%20Amazonia%20peruana.pdf
Finer, M. & Mamani, N. (2024). Gold mining in the southern Peruvian Amazon, summary 2021-2024 (208; MAAP). https://www.maapprogram.org/maap-208-gold-mining-in-the-southern-peruvian-amazon-summary-2021-2024/
Grossman, G. M. & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), pp. 353-377. https://doi.org/10.2307/2118443
Hansen, M. C.; Potapov, P. V.; Moore, R.; Hancher, M.; Turubanova, S. A.; Tyukavina, A. ... & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), pp. 850-853. https://doi.org/10.1126/science.1244693
IPCC (2023). Climate Change 2023: Synthesis Report. Grupo Intergubernamental de Expertos sobre el Cambio Climático. https://www.ipcc.ch/report/ar6/syr/
IQAir (2024). World Air Quality Report 2023: Lima. https://www.iqair.com/
Lawrence, D. & Vandecar, K. (2015). Effects of tropical deforestation on climate and agriculture. Nature Climate Change, 5(1), pp. 27-36. https://doi.org/10.1038/nclimate2430
Ministerio del Ambiente del Perú (MINAM) (2019). Inventario nacional de gases de efecto invernadero 2019. https://infocarbono.minam.gob.pe/inventarios-nacionales-gei/inventario-nacional-gases-efecto-invernadero-2019/
Ministerio de Desarrollo Agrario y Riego (MIDAGRI) (2023). Reporte Anual de Gases de Efecto Invernadero del sector Agricultura del año 2019. https://infocarbono.minam.gob.pe/wp-content/uploads/2023/05/Informe-RAGEI-Agricultura-2019_vf.pdf
Nobre, C. A. (2016). El futuro climático de la Amazonía. Revista de la Academia Brasileña de Ciencias, 88(3), pp. 2327-2336. https://doi.org/10.1590/0001-3765201620150387
Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939), pp. 419-422. https://doi.org/10.1126/science.1172133
Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. Organización Internacional del Trabajo.
Programa de las Naciones Unidas para el Medio Ambiente. (2025). Reducción de las emisiones derivadas de la deforestación y la degradación forestal. https://www.unep.org/es/explore-topics/cambio-climatico/redd
Quispe, C.; Tam, J.; Arellano, C.; Chamorro, A. & Espinoza, D. (2023). Informe sobre el pronóstico de efectos de ENOS sobre las condiciones oceanográficas frente a la costa peruana en base a forzantes del Pacífico ecuatorial y sudeste. Instituto del Mar del Perú. https://www.imarpe.gob.pe/imarpe/imagenes/portal/imarpe/informe_enos_diciembre_2023.pdf
Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F. S.; Lambin, E. F. ... & Foley, J. A. (2009). A safe operating space for humanity. Nature, 461(7263), pp. 472-475. https://doi.org/10.1038/461472a
Rudel, T. K.; Defries, R.; Asner, G. P. & Laurance, W. F. (2009). Changing drivers of deforestation and new opportunities for conservation. Conservation Biology, 23(6), pp. 1396-1405. https://doi.org/10.1111/j.1523-1739.2009.01332.x
SENAMHI (Servicio Nacional de Meteorología e Hidrología del Perú) (2021). Impacto del ENSO en la variabilidad climática del Perú. https://www.senamhi.gob.pe/
Stern, D. I. (2004). The rise and fall of the environmental Kuznets curve. World Development, 32(8), pp. 1419-1439. https://doi.org/10.1016/j.worlddev.2004.03.004
Temper, L.; Demaria, F. & Scheidel, A. (2018). The Global Environmental Justice Atlas (EJAtlas): Ecological distribution conflicts as forces for sustainability. Sustain Sci, 13, pp. 573-584.
Valle-Basto, D. F.; Espinosa-Quiñones, T. & Limache-de-la-Fuente, D. (2023). Evaluación de la deforestación (2000-2020) en concesiones forestales peruanas en la provincia de Tambopata (Madre de Dios) usando plataformas de datos abiertos. Revista Kawsaypacha: Sociedad y Medio Ambiente, 12. https://doi.org/10.18800/kawsaypacha.202302.A010
Wang, Q.; Li, Y. & Li, R. (2024). Rethinking the environmental Kuznets curve hypothesis across 214 countries: The impacts of 12 economic, institutional, technological, resource, and social factors. Humanities and Social Sciences Communications, 11(1), pp. 1-19. https://doi.org/10.1057/s41599-024-02736-9







