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Recently, Joo, Aguinis and Bradley (2017), using a novel distribution pitting technique, have found that the exponential tail distributions-- exponential and power law with an exponential cut-off -- and their generative mechanism - namely, incremental differentiation -, are the most frequent distribution in many individual outputs across different organizations, sectors, jobs and activities. However, this may not be totally accurate in nascent entrepreneurship processes: the first section of this research shows that the lognormal distribution in entrepreneurial outcomes seems predominant throughout the different panels - i.e., longitudinal studies - in different countries. We have studied those in which the datasets are in the public domain: Australia, Sweden, US PSED I & II (Reynolds, 2017b). The power law distribution with an exponential cut-off may also be a plausible fit in some particular panel outcomes variables. A definitive conclusion regarding which of these two distributions may be the better fit will require the analysis of the rest of 14 still ongoing longitudinal projects around the world. The pervasiveness of lognormality offers relevant clues to understand nascent entrepreneurial processes, their generative mechanism, and it will offer strategies to allocate resources to foster and promote new entrepreneurial ventures. The second section of this research is the design and implementation of a baseline agent-based model as a research tool, "A nascent entrepreneurial agent-based model". Inspired by previous simpler entrepreneurial models, our model introduces new layers of complexity, making possible parametrization and calibration. This baseline model, initially with parameters similar to the public available panel datasets --Australia, Sweden, US PSED --, is able to generate the patterns that were found in the empirical results: the heavy-tailed distributions. Although PSED-type of longitudinal panels have been performed in more than a dozen countries, their results and datasets are not publicly available yet. This base model is, therefore, flexible in order to be easily adapted to each of the empirical dataset under study. The model, at this initial stage, has not been fully parametrized and calibrated for any specific country. The baseline model takes the main parameters from the datasets available as examples, in order to show that multiplicative processes --as main generative mechanism-- are able to simulate the empirical patterns. The baseline model is designed as a research tool to experiment and to help entrepreneurship researchers to test their theories, and for exploring in more detail the mechanisms involved in the emergence of new ventures. The baseline model and its background documentation will be openly available to the research community in two major agent-based repositories. Taking this baseline model as a "backbone", researchers can change parameters, agents, behaviours, schedules or global variables for their own theory building or calibration of their specific country's simulation.

CO2 and CH4 current levels are the highest recorded during the Anthropocene epoch due, mostly, to emissions from anthropogenic activities. These emissions have modified the biochemical cycle of C and earth's thermal equilibrium. In contrast, CO emissions have decreased since 2000 following the strict controls on vehicle emissions. Atmospheric observations can be used to detect trends and changes in CO2, CH4 and CO concentrations, and also, to locate and characterize natural and anthropogenic sources. In situ measurements of atmospheric CO2, CH4 and CO have been performed at Royal Holloway University of London since 2000 to 2012 and standardised using NOAA calibration gases. CO2, CH4 and CO varied on time scales ranging from minutes to inter-annual and annual cycles. Diurnal cycles were observed and varied with the length daylight which influences the net soil uptake of CO2, the concentration of OH radicals and, the degree of vertical mixing. There is a greater influence of CO2 and CO anthropogenic emissions during weekdays when fossil fuel use and combustion processes are higher than at weekends not observed for CH4. CO2 showed an increasing trend of 2.45 ppm yr-1 data for the whole dataset; a higher rate of increase than the observed global trend due to higher regional combustion emissions such as airplanes, houses and cars on highly loaded motorways. By contrast, CO showed a progressive decline not linear, but if linearized the decline rate was 15.3 ppb CO yr-1, while CH4 concentration remained steady. The data set was split into 8 categories (45°) using wind sector analysis. This shows that the greatest concentrations for the three carbon gas are recorded from the NE, E and SE sectors. The lowest concentrations were observed for air from the S and SW sectors. Back trajectory and meteorological analysis of the data confirmed that the dominant sources of CO2, CH4 and CO are anthropogenic emissions from Greater London area and the dense local road network to the East of the measurement site. Nowadays, compared with Mace Head (West Ireland) data, the CO2 measured at Egham is slightly higher than at Mace Head while CO and CH4 is not far above Atlantic background levels during large periods of the year when prevailing S-SW winds reach the site. The Egham record implies that controls on CO emissions subsequent to legislation have been extremely successful the UK.