CO2 emissions and economic activity: Heterogeneity across countries and non-stationary series

Nonlinear regressions with nonstationary time series

“…Several works investigated the appropriateness of restricting all countries adhering to the same values of ( 1 , 2 ). For example, List and Gallet (1999), Dijkgraaf et al (2005), Piaggio and Padilla (2012). Particularly, Piaggio and Padilla (2012) tested this assumption by checking whether the confidence intervals of ( 1 , 2 ) across different countries overlap.…”

“…For example, List and Gallet (1999), Dijkgraaf et al (2005), Piaggio and Padilla (2012). Particularly, Piaggio and Padilla (2012) tested this assumption by checking whether the confidence intervals of ( 1 , 2 ) across different countries overlap. They rejected the homogeneity assumption of parameters across di↵erent countries, based on the result that they could not find any possible group with more than 4 countries whose confidence intervals overlap with each other.…”

How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis

“…Similarly, Piaggio and Padilla (2012) [34] study the relationship between CO 2 emissions and economic activity for 31 countries for the period from 1950 to 2006 using a co-integration analysis. They suggest considering the differences among countries in the relationship between CO 2 emissions and economic activity to avoid faulty estimations and conclusions.…”

“…They state that differences would depend on the real determinants of the relationship, which would be shaped by energy and environmental policies. As inferred by Piaggio and Padilla (2012) [34] and Jaunky (2011) [19]; our model captures the effects of these policies by including energy efficiency and renewable energy resources in reference to nonrenewable resources in a multivariate setting.…”

Analyzing the effects of Energy Action Plans on electricity consumption in Covenant of Mayors signatory municipalities in Andalusia

“…where E is an energy consumption indicator in per capita terms expressed in logarithms, Y is the income per capita expressed also in logarithms, A is the sum of the time effect and individual effect and Z is a set of control variables that impact energy consumption which is included to control for heterogeneity among individuals to avoid incorrect estimates (Piaggio and Padilla, 2012). Finally, e is a random error term.…”