![](data:image/png;base64,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)
El papel del consumo de energías renovables sobre los gases...
Vol.9-N°1, Enero - Junio 2021
p-ISSN:2602-8204 |e-ISSN 2737-6257
[8] Baah-Acheamfour, M., Carlyle, CN, Lim, SS, Bork, EW &
Chang, SX (2016). Los tipos de cobertura forestal y de pas-
tizales reducen las emisiones netas de gases de efecto inver-
nadero de los suelos agrícolas. Ciencia del Medio Ambiente To-
tal, 571, 1115-1127.
[9] Benedek, J., Sebestyén, T. T., & Bartók, B. (2018). Evaluation
of renewable energy sources in peripheral areas and renew-
able energy-based rural development. Renewable and Sustain-
able Energy Reviews, 90, 516-535.
[10] Boontome, P., Therdyothin, A., & Chontanawat, J. (2017). In-
vestigating the causal relationship between non-renewable
and renewable energy consumption, CO 2 emissions and eco-
nomic growth in Thailand. Energy Procedia, 138, 925-930.
[11] Breitung, J. (2002). Nonparametric tests for unit roots and
cointegración. Journal of Econometrics, 108(2), 343-363.
[12] Cherni, A., & Jouini, S. E. (2017). An ARDL approach to
the CO2 emissions, renewable energy and economic growth
nexus: Tunisian evidence. International Journal of Hydrogen En-
ergy, 42(48), 29056-29066.
[13] Chuang, J., Lien, H. L., Den, W., Iskandar, L., & Liao, P. H.
(2018). The Relationship Between Electricity Emission Factor
and Renewable Energy Certificate: The Free Rider and Out-
sider Effect. Sustainable Environment Research, 29, 138-147.
[14] Dickey, D., Fuller, W. A., 1981. Likelihood ratio statistics for
autoregressive time series with a unit root. Econometrica, 49,
1057-1072.
[15] Dong, K., Sun, R., & Hochman, G. (2017). Do natural gas and
renewable energy consumption lead to less CO2 emission?
Empirical evidence from a panel of BRICS countries. Energy,
141, 1466-1478.
[16] Farhani, S., Chaibi, A., & Rault, C. (2014). CO 2 emissions, out-
put, energy consumption, and trade in Tunisia. Economic Mod-
elling, 38, 426-434.
[17] Hausman, J. A. (1978). Specification tests in econometrics.
Econometrica: Journal of the Econometric Society, 1251-1271.
[18] Hosseinzadeh-Bandbafha, H., Nabavi-Pelesaraei, A., Khanali,
M., Ghahderijani, M., & Chau, K. W. (2018). Application of
data envelopment analysis approach for optimization of en-
ergy use and reduction of greenhouse gas emission in peanut
production of Iran. Journal of Cleaner Production, 172, 1327-
1335.
[19] Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for
unit roots in heterogeneous panels. Journal of Econometrics,
115(1), 53-74.
[20] 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.
[21] Jebli, M. B., & Youssef, S. B. (2017). The role of renewable en-
ergy and agriculture in reducing CO2 emissions: Evidence for
North Africa countries. Ecological indicators, 74, 295-301.
[22] Jin, T., & Kim, J. (2018). What is better for mitigating car-
bon emissions–Renewable energy or nuclear energy? A panel
data analysis. Renewable and Sustainable Energy Reviews, 91,
464 471.
[23] Juana de Sardón, J. M. (2003). Energías renovables para el de-
sarrollo. Editorial Paraninfo
[24] Kais, S., & Sami, H. (2016). An econometric study of the im-
pact of economic growth and energy use on carbon emissions:
panel data evidence from fifty eight countries. Renewable and
Sustainable Energy Reviews, 59, 1101-1110.
[25] 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.
[26] Kim, J., Park, S. Y., & Lee, J. (2018). Do people really want re-
newable energy? Who wants renewable energy?: Discrete
choice model of reference-dependent preference in South
Korea. Energy Policy.
[27] Kuznets, S. (1955). Economic growth and income inequality.
The American economic review, 45(1), 1-28.
[28] Khondaker, A. N., Hasan, M. A., Rahman, S. M., Malik, K.,
Shafiullah, M., & Muhyedeen, M. A. (2016). Greenhouse
gas emissions from energy sector in the United Arab Emi-
rates–An overview. Renewable and Sustainable Energy Reviews,
59, 1317-1325.
[29] 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.
[30] Li, K., & Lin, B. (2015). Impacts of urbanization and industri-
alization on energy consumption/CO 2 emissions: Does the
level of development matter? Renewable and Sustainable En-
ergy Reviews, 52, 1107-1122.
[31] Liobikien˙
e, G., & Butkus, M. (2017). Environmental Kuznets
Curve of greenhouse gas emissions including technological
progress and substitution effects. Energy, 135, 237-248.
[32] Mittal, S., Dai, H., Fujimori, S., & Masui, T. (2016). Bridging
greenhouse gas emissions and renewable energy deployment
target: comparative assessment of China and India. Applied
energy, , 166, 301-313.
[33] Mirza, F. M., & Kanwal, A. (2017). Energy consumption, car-
bon emissions and economic growth in Pakistan: Dynamic
causality analysis. Renewable and Sustainable Energy Reviews,
72, 1233.
[34] Mohlin, K., Camuzeaux, J. R., Muller, A., Schneider, M., & Wag-
ner, G. (2018). Factoring in the forgotten role of renewables
in CO 2 emission trends using decomposition analysis. Energy
Policy, 116, 290-296.
[35] Moreno, J. R., Ortiz, J. D. C., de Vega, R. G., & Caro, G. V.
(2006). Estimación de la emisión de contaminantes debida al
tráfico urbano mediante modelos de asignación de tráfico. In
X Congreso de Ingeniería de Organización.
[36] Moutinho, V., & Robaina, M. (2016). Is the share of renew-
able energy sources determining the CO2 kWh and income
relation in electricity generation? Renewable and Sustainable
Energy Reviews, 65, 902-914.
[37] Nikzad, R., & Sedigh, G. (2017). Greenhouse gas emissions
and green technologies in Canada. Environmental Develop-
ment, 24, 99-108.