• Energy Research

PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); inland navigation (SHP); international navigation (SHP-INT); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).

Short-term and mid-term projections of energy consumption and carbon emissions raise significant concern about the availability of the necessary energy resources to meet the growing demand and about the impact of emissions on global change. Different macroeconomic models address this issue through global variables, such as gross domestic product, production of goods and services, total population and natural resources extraction. However, the relations among these variables are neither linear nor simple. In an attempt to base said relations on a “bottom-up” perspective, the individual behavior of representative agents of economy, in terms of energy consumption and related carbon emissions, was studied, with particular emphasis on their investment in human capital. It was found that a higher investment in human capital (e.g., education, research) was translated into a better distribution of consumption, with a higher level of energy efficiency and a slight improvement in carbon emissions intensity.

This study describes a model to calculate energy consumption and CO 2 emissions into the atmosphere by the road transport sector in Argentina from 1960 to 2050. The model analyses different mitigation scenarios to determine effective options to produce a sensitive decrease in carbon emissions. The results of this study show that by reducing the use of private vehicles while increasing the use of public transport, combined with a high rate of conversion of gasoline vehicles into hybrid electric vehicles, CO 2 emissions could be reduced up to 9% (or 7.7 Tg). However, without further technological improvements and higher modal transfer to low carbon/less energy-intensive modes, it will be difficult to obtain emission stabilisation of transport emissions under 80 Tg by 2050, which represent approximately twice the current values.

Abstract We studied the role of cold waves and heat waves on major power outages in the metropolitan area of Buenos Aires. Impacts of events occurring in the tails of distributions were assessed estimating deviations of minimum temperature, maximum temperature and hourly electricity consumption with respect to statistically derived thresholds and using three sets of data: temperature observations (1911–2013); major power outages reported in a disaster database (1971–2013) and hourly electricity consumption (2006–2013). These deviations (exceedances) proved to be adequate indicators of the stress posed by extreme temperature events to the electricity distribution system leading to major blackouts. Based on these indicators, we found that the electricity distribution system was under similar stress during cold waves or heat waves, but it was much more vulnerable to heat waves (three blackouts under cold waves against 20 under heat waves between 2006 and 2013). For heat waves, the results of a binomial regression logistic model provided an adequate description of the probability of disastrous supply interruptions in terms of exceedances in extreme temperatures and electricity consumption stress. This approach may be of use for other cities wishing to evaluate the effects of extreme temperature events on the electricity distribution infrastructure.

With the purpose of studying the influence of population dynamics and economic growth on energy consumption and carbon emissions, an endogenous economic growth model is proposed incorporating physical and human capital and using an Agent-Based Model. The model can test different development strategies by identifying the key factors existing at the agent level that may speed up or slow down a given path, and therefore it is an interesting tool to develop and to test mitigation and/or adaptation measures. Favorable scenarios may be possible in societies that encourage investment in human capital through education and technological development, provided that this is accompanied by a reduction in consumption rates and the creation of physical capital by the population. Moreover, this model shows that human capital resulting from education not only raises productivity, but also plays a key role in the development and adoption of new technologies that drive long-term growth.