Persistence of nitrogen oxides emissions using historical time series data: evidence from 37 countries

ABSTRACT

Two features in time series data: the existence of time trends and the degree of persistence, are examined in this work on the nitrogen oxides emissions from 37 OECD countries. Updated techniques in time series are used that allow for fractional degrees of differentiation in the data. Thus, if the number of differences required is one, nitrogen oxides emissions are not mean reverting in the sense that if there is an exogenous shock (resulting from a technological advancement to change nitrogen oxides emissions), the effect of such shock on nitrogen oxides emissions will be permanent. Time trends are observed in half of the series. For these countries the trend coefficient is found to be positive in all cases. This is an indication that continuous technological progress is needed in taming NO_x emissions. In addition to developing their own local technologies, less technologically endowed OECD countries should engage in collaboration with the more technologically endowed countries in order to facilitate increase in trans-border transfer of technology. The technologically advanced countries should also strive to continue to introduce better technologies in a bid to reduce NO_x emissions. Most of the results show evidence for persistence of nitrogen oxides emissions.

KEYWORDS:

• NO_x emissions
• time trends
• long memory
• fractional integration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In plain words, nonstationarity means that the series is not stable across time, with the mean and the variance among other things changing with time. Environmental Kuznets curve hypothesis suggests nations will generate less pollution as they grow. The hypothesis is based on the assumption that as countries experience economic expansion, they will develop technologies that will ensure lower pollution levels are generated from economic activities.

2 A white noise process is a pure random process, that is characterised by having a zero mean, a constant variance and zero values for the autocorrelations.

3 The spectral density function is the counterpart of the autocorrelations (time dependence) in the frequency domain.