  Carbon dioxide (CO2) emissions have emerged as a critical issue with profound
impacts on the environment, human health, and the global economy. The steady
increase in atmospheric CO2 levels, largely due to human activities such as
burning fossil fuels and deforestation, has become a major contributor to
climate change and its associated catastrophic effects. To tackle this pressing
challenge, a coordinated global effort is needed, which necessitates a deep
understanding of emissions patterns and trends. In this paper, we explore the
use of statistical modelling, specifically the lognormal distribution, as a
framework for comprehending and predicting CO2 emissions. We build on prior
research that suggests a complex distribution of emissions and seek to test the
hypothesis that a simpler distribution can still offer meaningful insights for
policy-makers. We utilize data from three comprehensive databases and analyse
six candidate distributions (exponential, Fisk, gamma, lognormal, Lomax,
Weibull) to identify a suitable model for global fossil CO2 emissions. Our
findings highlight the adequacy of the lognormal distribution in characterizing
emissions across all countries and years studied. Furthermore, to provide
additional support for this distribution, we provide statistical evidence
supporting the applicability of Gibrat's law to those CO2 emissions. Finally,
we employ the lognormal model to predict emission parameters for the coming
years and propose two policies for reducing total fossil CO2 emissions. Our
research aims to provide policy-makers with accurate and detailed information
to support effective climate change mitigation strategies.
