Empirical Model for Estimating the Ecological Footprint in Ecuador Based on Demographic, Economic and Environmental Indicators

• Autorzy: Ilbay Mercy , Ruiz Joseline , Cueva Evelyn , Ortiz Vladimir , Morales Diego

Identyfikatory

DOI

10.12911/22998993/135868

• Języki publikacji: EN

Abstrakty

EN

In this study, the existence of long-term trends in ecological footprint (EF), biocapacity, GDP, population and CO₂ emissions for the period of 1961–2016, and their effect on the demographic, economic and biocapacity indicators on Ecuador’s EF were identified. The long-term trend analysis was performed by means of a Mann-Kendall, nonparametric test. The development of a multiple linear regression model of the EF considers the population, GDP, biocapacity and its logarithmic transformations as returners. A backward removal method was used, in conjunction with the Akaike criterion (AIC) to validate the most suitable model in terms of adjusted-R², NSE, BIAS and RMSE, respectively. The results show significant changes (p<0.01) of the annual EF increase (0.015 hag), total population (216.375 inhabitants), GDP ($1.2 billion) and CO₂ emissions (718.6 kt). However, the biocapacity has been declining (0.086 hag) at a faster rate than the ecological footprint. In other words, in a few years, the country will be facing ecological deficits. As for the empirical model of EF, it can be observed that for every increase of inhabitant’s units, the natural logarithm of biocapacity and GDP will increase EF by 1.68x10^-7, 4.84 and 0.905 gha, respectively. Moreover, EF will be decreased by 0.6 gha each time the biocapacity increases by one gha unit. Finally, this robust and easy-to-interpret model allows accurate EF predictions that can be a tool to better forecast the environmental trends, allowing the development of sustainable projects in Ecuador.

Słowa kluczowe

EN

ecological footprint; trends; environmental degradation; Ecuador

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2021
• Tom: Vol. 22, nr 5
• Strony: 59--67
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Ilbay Mercy

• Universidad Técnica de Cotopaxi, Ingeniería Ambiental, Salache, 050108, Latacunga, Ecuador

• https://orcid.org/0000-0001-9503-2686

autor

Ruiz Joseline

• Universidad Técnica de Cotopaxi, Ingeniería Ambiental, Salache, 050108, Latacunga, Ecuador

• https://orcid.org/0000-0002-9936-5215

autor

Cueva Evelyn

• Escuela Politécnica Nacional, Research Center on Mathematical Modeling (MODEMAT), 170525, Quito, Ecuador

• https://orcid.org/0000-0001-7495-9482

autor

Ortiz Vladimir

• Universidad Técnica de Cotopaxi, Ingeniería Ambiental, Salache, 050108, Latacunga, Ecuador

• https://orcid.org/0000-0003-3512-9122

autor

Morales Diego

• Universidad Técnica de Cotopaxi, Ingeniería Ambiental, Salache, 050108, Latacunga, Ecuador

• https://orcid.org/0000-0001-6603-4500

Bibliografia

• 1. Ahmed Z., Zafar MW., Ali S., Danish. 2020. Linking urbanization, human capital, and the ecological footprint in G7 countries: An empirical analysis. Sustainable Cities and Society, 102064. doi:10.1016/j.scs.2020.102064
• 2. Ahmed Z. & Wang Z. 2019. Investigating the Impact of Human Capital on the Ecological Footprint in India: An Empirical Analysis. Environmental Science and Pollution Research, 26 (26), 26782–26796. doi: 10.1007/s11356–019–05911–7
• 3. Akaike H. 1974. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19 (6), 716–723. doi: 10.1109/TAC.1974.1100705.
• 4. Alola A., Bekun, F., & Sarkodie S. 2019. Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe. Science of The Total Environment, 685, 702–709, doi:10.1016/j.scitotenv.2019.05.139
• 5. Anand S.& Sen A. 2000. Human Development and Economic Sustainability. World Development, 28(12), 2029–2049. doi:10.1016/s0305–750x(00)00071–1
• 6. Badii M. 2008. La Huella Ecológica y Sustentabilidad. Daena: International Journal of Good Conscience, 3 (1), 672–678.
• 7. Borucke M., Moore D., Cranston G., Gracey K., Iha K., Larson J., Lazarus E., Morales JC., Wackernagel M., Galli A. 2013. Accounting for Demand and Supply of the Biosphere’s Regenerative Capacity: The National Footprint Accounts’ Underlying Methodology and Framework. Ecological Indicators, 24, 518–533. doi:10.1016/j.ecolind.2012.08.005
• 8. Büchele D., Chao M., Ostermann M., Leenen M., Bald I. 2019. Multivariate Chemometrics as a Key Tool for Prediction of K and Fe in a Diverse German Agricultural Soil-Set Using EDXRF. Scientific Reports, 9, 17588.
• 9. Burnham K.P., Anderson D.R. 2004. Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304, doi: 10.1177/0049124104268644
• 10. Ćalasan M., Abdel Aleem S.H., Zobaa A.F. 2020. On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Conversion and Management, 210, 112716, doi:10.1016/j.enconman.2020.112716
• 11. Carroll R., Schneider H. 1985. A note on levene’s tests for equality of variances. Statistics & Probability Letters, 3(4), 191–194. doi:10.1016/0167–7152(85)90016–1
• 12. Charfeddine L., Mrabet, Z. 2017. The impact of economic development and social-political factors on ecological footprint: A panel data analysis for 15 MENA countries. Renewable and Sustainable Energy Reviews, 76, 138–154. doi:10.1016/j.rser.2017.03.031
• 13. Chowdhury M. & Turin T. 2020. Variable selection strategies and its importance in clinical prediction modelling. Family medicine and community health, 8(1), e000262, doi:10.1136/fmch-2019–000262
• 14. Danish, Baloch M., Mehmood N., Zhang J. 2019. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries. Science of The Total Environment. doi:10.1016/j.scitotenv.2019.05.028
• 15. Danish, Ulucak R., Ud-Din S. 2020. Determinants of the Ecological Footprint: Role of Renewable Energy, Natural Resources, and Urbanization. Sustainable Cities and Society, 54, 101996, doi: 10.1016/j.scs.2019.101996
• 16. Destek, M.A., Ulucak, R., Dogan, E. 2018. Analyzing the environmental Kuznets curve for the EU countries: the role of ecological footprint. Environmental Science and Pollution Research, 25(29), 29387–29396. doi: 10.1007/s11356018 2911
• 4.Destek M. & Sinha A. 2020. Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Cooperation and development countries. Journal of Cleaner Production, 242, 118537, doi:10.1016/j.jclepro.2019.118537
• 17. Duntenam G. H. 1989. Principal Components Analysis. Quantitative Applications in the Social Sciences. Sage Publications, Newbury Park.
• 18. Efroymson MA. 1960. “Multiple regression analysis,” Mathematical Methods for Digital Computers.Wiley, New York.
• 19. Galli A., Wackernagel M., Iha K., Lazarus E. 2014. Ecological Footprint: Implications for Biodiversity. Biological Conservation, 173, 121–132.
• 20. Güçlü Y.S. 2018. Multiple Şen-innovative trend analyses and partial Mann-Kendall test. Journal of Hydrology, doi:10.1016/j.jhydrol.2018.09.034
• 21. Hawkins D.M. 1973. On the Investigation of Alternative Regressions by Principal Component Analysis. Journal of the Royal Statistical Society. Series C (Applied Statistics), 22(3), 275–286. doi:10.2307/2346776
• 22. Jeffers J.N.R. 1981. Investigation of Alternative Regressions: Some Practical Examples. Journal of the Royal Statistical Society. Series D (The Statistician), 30(2), 79–88. doi:10.2307/2987560
• 23. Kassouri, Y., Altıntaş, H. 2020. Human Well-being Versus Ecological Footprint in MENA Countries: A trade-off?. Journal of environmental management, 263, 110405, doi: 10.1016/j.jenvman.2020.110405
• 24. Kendall M, 1975. Rank Correlation Methods. Griffin, London, UK.
• 25. Lin D., Hanscom L., Murthy A., Galli A., Evans M., Neill E., Mancini M., Martindill J., Medoua, F.Z., Huang S., Wackernagel M. 2018. Ecological Footprint Accounting for Countries: Updates and Results of the National Footprint Accounts, 2012–2018. Resources, 7(3), 58, doi: 10.3390/resources7030058
• 26. Mancini M., Galli A., Coscieme L., Niccolucci V., Lin D., Pulselli F., Bastianoni S., Marchettini N. 2018. Exploring ecosystem services assessment through Ecological Footprint accounting. Ecosystem Services, 30, 228–235. doi:10.1016/j.ecoser.2018.01.010
• 27. Mann H.B. 1945. Nonparametric Tests Against Trend. Econometrica. 13, 245–259
• 28. Martínez R.S. 2009. La Huella Ecológica del Sistema Económico y Urbano Actual: El Caso de China y del Distrito Federal. Estudios Agrarios, 41, 184.
• 29. Ministerio del Ambiente del Ecuador (MAE). 2013. Reporte de la Huella Ecológica del Ecuador: 2008 y 2009. Primera edición. Quito – Ecuador, pages 6–54
• 30. Ministerio del Ambiente del Ecuador (MAE). 2014. Reporte de la Huella Ecológica del Ecuador: 20082011. Quito – Ecuador, pages 4–68
• 31. Mrabet Z., AlSamara M., & Hezam Jarallah S. 2016. The impact of economic development on environmental degradation in Qatar. Environmental and Ecological Statistics, 24(1), 7–38. doi:10.1007/s10651–016–0359–6
• 32. Nash J., Sutcliffe J. 1970. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology, 10(3), 282–290. doi:10.1016/0022–1694(70)90255–6
• 33. Nathaniel S., Yalçiner K., Bekun F. 2020. Assessing the environmental sustainability corridor: Linking natural resources, renewable energy, human capital, and ecological footprint in BRICS. Resources Policy, 101924. doi:10.1016/j.resourpol.2020.101924
• 34. Rentería V., Toledo E., Bravo D., Ochoa-Jiménez D. 2016. Relación entre emisiones contaminantes, crecimiento económico y consumo de energía. El caso de Ecuador 1971–2010. Revista Politécnica, 38, 7.
• 35. Rodrigues A. de P., de Gois J., Costa M., da Silva C., Xavier V., Luna A. 2020. Exploring multivariate linear regression methods for the prediction of total phenolic content in standard American lager beers using synchronous fluorescence spectroscopy fused data. Chemometrics and Intelligent Laboratory Systems, 104168. doi:10.1016/j.chemolab.2020.104168
• 36. Royston P., Moons K., Altman D. & Vergouwe Y. 2009. Prognosis and prognostic research: Developing a prognostic model. BMJ, 338(mar31 1), b604–b604, doi:10.1136/bmj.b604
• 37. Sanchez M., Ochoa M., Toledo E., & Ordoñez J. 2020. The relevance of Index of Sustainable Economic Wellbeing. Case study of Ecuador. Environmental and Sustainability Indicators, 100037. doi:10.1016/j.indic.2020.100037
• 38. Sen P.K. 1968. Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63, 1379–1389.
• 39. Theil H. 1992. Un Método de Rango Invariante de Análisis de Regresión Lineal y Polinomial. En: Raj B., Koerts J. (eds) Contribuciones de Henri Theil a la Economía y la Econometría. Estudios Avanzados en Econometría Teórica y Aplicada, vol 23. Springer, doi: 10.1007/978–94–011–2546–8
• 40. Ulucak R., Lin D. 2017. Persistence of policy shocks to Ecological Footprint of the USA. Ecological Indicators 80, 337–343. https://doi.org/10.1016/j.ecolind.2017.05.020
• 41. Vasallo J.M. 2015. Estadística Aplicada a las Ciencias de la salud: Capítulo Análisis de Regresión Lineal Simple y Múltiple. Elsevier, Madrid.
• 42. Wackernagel M., Galli A., Hanscom L., Lin D., Mailhes L., Drummond T. 2018. Ecological Footprint Accounts. Routledge Handbooks Online, doi:10.4324/9781315561103–16
• 43. Wang Z., Bui Q., Zhang B., Pham T. 2020. Biomass Energy Production and its Impacts on the Ecological Footprint: An Investigation of the G7 Countries. Science of the Total Environment, 743, 140741, doi:https://doi.org/10.1016/j.scitotenv.2020.140741
• 44. Yu J. Y., Kao H. Y. 2007. Decadal Changes of ENSO Persistence Barrier in SST and Ocean Heat Content Indices: 1958–2001. Journal of Geophysical Research: Atmospheres, 112 (D13), doi:10.1029/2006JD007654
• 45. Zambrano-Monserrate M., Ruano M.A., Ormeño-Candelario V., Sanchez-Loor D.A. 2020. Global Ecological Footprint and Spatial Dependence Between Countries. Journal of Environmental Management, 272, 111069, doi:10.1016/j.jenvman.2020.111069

Uwagi

1. Błędna numeracja w bibliografii - rozdzielono poz. 16.

2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).