arXiv subjects

Michal Brzezinski

Publications and source records attributed to Michal Brzezinski.

4 recordsLinked to original sources

arXiv2021

The impact of past pandemics on CO$_2$ emissions and transition to renewable energy

We estimate the short- to medium term impact of six major past pandemic crises on the CO2 emissions and energy transition to renewable electricity. The results show that the previous pandemics led on average to a 3.4-3.7% fall in the CO2 emissions in the short-run (1-2 years since the start of the pandemic). The effect is present only in the rich countries, as well as in countries with the highest pandemic death toll (where it disappears only after 8 years) and in countries that were hit by the pandemic during economic recessions. We found that the past pandemics increased the share of electricity generated from renewable sources within the fiveyear horizon by 1.9-2.3 percentage points in the OECD countries and by 3.2-3.9 percentage points in countries experiencing economic recessions. We discuss the implications of our findings in the context of CO2 emissions and the transition to renewable energy in the post-COVID-19 era.

econ.GN↗

arXiv2014

Power laws in citation distributions: Evidence from Scopus

Modeling distributions of citations to scientific papers is crucial for understanding how science develops. However, there is a considerable empirical controversy on which statistical model fits the citation distributions best. This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers. We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 drawn from Scopus. The power-law model is compared with a number of alternative models using a likelihood ratio test. We have found that the power-law hypothesis is rejected for around half of the Scopus fields of science. For these fields of science, the Yule, power-law with exponential cut-off and log-normal distributions seem to fit the data better than the pure power-law model. On the other hand, when the power-law hypothesis is not rejected, it is usually empirically indistinguishable from most of the alternative models. The pure power-law model seems to be the best model only for the most highly cited papers in "Physics and Astronomy". Overall, our results seem to support theories implying that the most highly cited scientific papers follow the Yule, power-law with exponential cut-off or log-normal distribution. Our findings suggest also that power laws in citation distributions, when present, account only for a very small fraction of the published papers (less than 1% for most of science fields) and that the power-law scaling parameter (exponent) is substantially higher (from around 3.2 to around 4.7) than found in the older literature.

cs.DL↗

arXiv2013

Empirical modeling of the impact factor distribution

The distribution of impact factors has been modeled in the recent informetric literature using two-exponent law proposed by Mansilla et al. (2007). This paper shows that two distributions widely-used in economics, namely the Dagum and Singh-Maddala models, possess several advantages over the two-exponent model. Compared to the latter, the former give as good as or slightly better fit to data on impact factors in eight important scientific fields. In contrast to the two-exponent model, both proposed distributions have closed-from probability density functions and cumulative distribution functions, which facilitates fitting these distributions to data and deriving their statistical properties.

cs.DL↗

arXiv2013

Do wealth distributions follow power laws? Evidence from "rich lists"

We use data on wealth of the richest persons taken from the "rich lists" provided by business magazines like Forbes to verify if upper tails of wealth distributions follow, as often claimed, a power-law behaviour. The data sets used cover the world's richest persons over 1996-2012, the richest Americans over 1988-2012, the richest Chinese over 2006-2012 and the richest Russians over 2004-2011. Using a recently introduced comprehensive empirical methodology for detecting power laws, which allows for testing goodness of fit as well as for comparing the power-law model with rival distributions, we find that a power-law model is consistent with data only in 35% of the analysed data sets. Moreover, even if wealth data are consistent with the power-law model, usually they are also consistent with some rivals like the log-normal or stretched exponential distributions.

q-fin.ST↗