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This study aimed to analyze whether, and how, uncertainty influences the layout and costs of a CO 2 transportation network. The case without uncertainty is modelled with a perfect foresight (PF) model and with uncertainty with the real option approach (ROA). In this study, uncertainties in the CO 2 price, tariff received per tonne of CO 2 transported, the willingness, probability and moment that sources join the CO 2 transportation network are incorporated in the analysis. The results show that uncertainty leads to higher required CO 2 prices before investments in carbon dioxide capture and storage (CCS) are made. With a volatility of 47% in the CO 2 price, the required CO 2 price almost triples in comparison with the net present value approach. Hence, under uncertainty less sources are retrofitted with CCS and less CO 2 is captured and stored over time. For instance, for the analyzed case study 31 Mt and 137 Mt CO 2 is projected to be captured in the base scenario of ROA and PF model, respectively, in the period 2015–2050. If the volatility of the CO 2 price is reduced with 50%, 96 Mt is projected to be captured in the ROA, which is still about one third less than in the PF model. Furthermore, the results show that uncertainty leads to less development of trunklines. All this leads to an increase in the transport and storage costs. For instance, for our case study, the average CO 2 transport and storage costs in 2050 increase from 2.8 €/t to 13 €/t in the base scenario of the ROA compared to the PF model. If the volatility is reduced with 50%, the transport and storage costs decrease to 7.5 €/t in the ROA, which is still 2.5 times as much as in the PF model. Our findings indicate that the implementation of CCS can best be stimulated by reducing the volatility of the CO 2 price, reducing capture costs and facilitating cooperation between nearby sources. [ABSTRACT FROM AUTHOR]

The expansion of the ethanol industry in Brazil faces two important challenges: to reduce total ethanol production costs and to limit the greenhouse gas (GHG) emission intensity of the ethanol produced. The objective of this study is to economically optimize the scale and location of ethanol production plants given the expected expansion of biomass supply regions. A linear optimization model is utilized to determine the optimal location and scale of sugarcane and eucalyptus industrial processing plants given the projected spatial distribution of the expansion of biomass production in the state of Goiás between 2012 and 2030. Three expansion approaches evaluated the impact on ethanol production costs of expanding an existing industry in one time step (one-step), or multiple time steps (multi-step), or constructing a newly emerging ethanol industry in Goiás (greenfield). In addition, the GHG emission intensity of the optimized ethanol supply chains are calculated. Under the three expansion approaches, the total ethanol production costs of sugarcane ethanol decrease from 894 US$/m 3 ethanol in 2015 to 752, 715, and 710 US$/m 3 ethanol in 2030 for the multi-step, one step and greenfield expansion respectively. For eucalyptus, ethanol production costs decrease from 635 US$/m 3 in 2015 to 560 and 543 US$/m 3 in 2030 for the multi-step and one-step approach. A general trend is the use of large scale industrial processing plants, especially towards 2030 due to increased biomass supply. We conclude that a system-wide optimization as a marginal impact on overall production costs. Utilizing all the predefined sugarcane and eucalyptus supply regions up to 2030, the results showed that on average the GHG emission intensity of sugarcane cultivation and processing is −80 kg CO 2 /m 3 , while eucalyptus GHG emission intensity is 1290 kg CO 2 /m 3 . This is due to the high proportion of forest land that is expected to be converted to eucalyptus plantations. Future optimization studies may address further economic or GHG emission improvement potential by optimizing the GHG emission intensity or perform a multi-objective optimization procedure. [ABSTRACT FROM AUTHOR]