(705d) An Analysis of Socioeconomic Impacts of Aviation Biofuel Development in Brazil
A scenarios-based input-output (IO) analysis is used to evaluate socioeconomic effects including employment, GDP and trade balance (represented by imports) of aviation biofuel supply chains. This approach is exemplified for the local production of aviation biofuel in Brazil. Given that the commercialization of aviation biofuel is still at an early stage, available knowledge and data on the deployment of aviation biofuel are limited. A scenario analysis is useful to explore how possible futures of aviation biofuel would develop. The timeframe of the designed scenarios is set for 2050. The storyline of each scenario is elaborated on the market of aviation biofuel, conversion technologies, selection of feedstocks, and potential competition for biomass. Furthermore, the future demand of aviation biofuel under each scenario is estimated, which can subsequently be used in IO analysis to further determine the effects on employment, GDP and imports. In addition, we propose a stochastic simulation to address the uncertainty of IO analysis, by performing a Monte Carlo (MC) simulation for the technical coefficients in the IO matrix. The uncertainty analysis provides insights into the robustness and reliability of the assessment results.
Four scenarios are developed based on the diverging trends of two key driving forces: i) biofuel policies (conservative or proactive) and ii) technological advancement (gradual or breakthrough). Different demands of aviation biofuel are projected for each scenario, ranging from 3% to 15% of the total fuel demand. Three combinations of technologies and feedstocks are considered for producing aviation biofuel in Brazil: alcohol to jet (ATJ) with sugarcane, hydro-processed esters and fatty acids (HEFA) with macauba, and Fischer-Tropsch (FT) with eucalyptus.
Results of IO analysis show that the scales of socioeconomic effects on employment, GDP and imports are proportional to the estimated demand of aviation biofuel in each scenario. Specially, Scenario 3 (where proactive biofuel policies go hand in hand with advanced technology) leads to the highest employment, GDP and imports potential, whereas the lowest socioeconomic effects are expected in Scenario 1 (in which biofuel policies are conservative and biofuel technologies progress slowly). Furthermore, under the same scenario, the macauba-HEFA chain performs best in terms of employment creation, followed by the sugarcane-ATJ chain and at last the eucalyptus-FT chain. With regard to GDP, the macauba-HEFA chain leads to higher effects than the other two chains. On the other hand, the most significant effects on imports are estimated in the eucalyptus-FT chain, followed by sugarcane-ATJ chain, while the macauba-HEFA chain sees the lowest imports. Additionally, the uncertainty analysis suggests that socioeconomic effects calculated with deterministic method are generally higher than the stochastically simulated results. The variances range from 1% to 28%.
To conclude, it is clear that enabling policy environment, high level of technology readiness, as well as availability of sustainable biomass play a key role in maximizing socioeconomic benefits. The feedstock sectors are the key sectors responsible for employment and GDP generation. While this reveals substantial potential of job opportunities and social development, the cultivation of feedstocks is associated with many uncertainties such as climate, land availability, and agricultural practices. Only when the production of aviation biofuel are developed in a way without threatening food security or deteriorating land conditions, the positive socioeconomic impacts do not contradict sustainability. In addition, the proposed stochastic simulation approach has been useful to address uncertainty in IO analysis. This contributes to an informed decision-making process regarding aviation biofuel development, from the perspective of social sustainability. To the best of the authorsâ knowledge, this is an innovative attempt to address uncertainty of IO analysis in the context of ex-ante socioeconomic assessment by using stochastic simulation to capture the uncertainty in the input-output matrix.