plm: Implication of irregularly lagged time series

I have an irregularly lagged and unbalanced time series. I want to run a regression fixed effect regression on my data but I'm not totally sure of the implication of the irregular intervals (the unbalancedness is taken into account by plm()). The output of the regression seems not to consider the issue.

Please consider the following

# Sample data
require(WDI)
df <- WDI(country = c("BR","US", "CA"), start = 2000, end = 2010,
indicator = c('EN.ATM.CO2E.PC', 'NY.GDP.PCAP.CD'))

# Remove random years
df <- df[-which(df$year==2005),] df <- df[-which(df$year==2003),]
df <- df[-which(df$year==2006 & df$country == "Brazil"),]

require(plm)
summary(plm(EN.ATM.CO2E.PC ~ NY.GDP.PCAP.CD, data = df, index = c('country', 'year'),
method = "within", balanced = FALSE))

which outputs

Oneway (individual) effect Within Model

Call:
plm(formula = EN.ATM.CO2E.PC ~ NY.GDP.PCAP.CD, data = df, index = c("country",
"year"), method = "within", balanced = FALSE)

Unbalanced Panel: n=3, T=8-9, N=26

Residuals :
Min. 1st Qu.  Median 3rd Qu.    Max.
-1.540  -0.289   0.110   0.543   1.170

Coefficients :
Estimate  Std. Error t-value Pr(>|t|)
NY.GDP.PCAP.CD -6.8273e-05  2.1762e-05 -3.1373 0.004788 **
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    16.404
Residual Sum of Squares: 11.333
R-Squared      :  0.3091
Adj. R-Squared :  0.26155
F-statistic: 9.84253 on 1 and 22 DF, p-value: 0.0047884