The oil and gas industry is responsible for 6% of total global CO2 emissions, from exploration to downstream petrochemical production and account for another 50% when including the use of its products. Thus, this industry has a significant role in realising the target of net “zero” CO2 emissions by 2070, essential to limit global warming to 1.8 °C [2], as introduced under the Paris agreement. Currently, the interactions of an extensive set of individual and combined CO2 mitigation measures along the value chain and over time are poorly assessed. This paper aims to assess a bottom-up CO2 mitigation potential for a complex refinery, including portfolios of combined mitigation options, considering synergies, overlap, and interactions over time for more realistic insight into the costs and constraints of the mitigation portfolio. A total of 40 measures were identified, covering a wide range of technologies such as energy efficiency measures (EEM), carbon capture and storage (CCS), bio-oil co-processing, blue and green hydrogen (BH2, GH2), green electricity import, and electrification of refining processes linked to the transition of the Colombian energy systems. Five deployment pathways were assessed to achieve different specific targets: 1-base case scenario, 2-less effort, 3-maximum CO2 avoidance, 4-INDC, and 5-measures below 200 €/t CO2. Two scenarios (3 and 5) gave the highest GHG emission reduction potentials of 106% and 98% of refining process emissions, respectively. Although significant, it represent only around 13% of the life-cycle emissions when including upstream and final-use emissions of the produced fuels. Bio-oil co-processing options account for around 60% of the mitigation options portfolio, followed by CCS (23%), green electricity (7%) and green H2 (6%). The devised methodological approach in this study can also be applied to assess other energy-intensive industrial complexes and shed light on the bias for estimating CO2 mitigation potentials, especially when combining different mitigation options. This is turn is vital to define optimal transition pathways of industrial complexes.@en

Estimating the oil recovery potential using CO2 (CO2-EOR) at a national level is resource-intensive at a scale that is not usually available. The aim of this study is two-fold: first, the potential for CO2 storage and enhanced oil recovery (EOR) in Colombia is evaluated; and, second, the results from two different calculation methods (stochastic and deterministic) are compared when there is lack of information for a quick screening of suitable oilfields. The deterministic approach is based on expert insight and data found in the literature; while, the stochastic uses statistical data from two different databases (commercial and simulation-based results) to run a Monte Carlo simulation. Potential estimates based on typical values from the literature (deterministic) results in 277 MMbbl (million barrels) of oil and 36 Mt (million tonnes) of CO2. In contrast, a probabilistic-based method using a wide simulation database (stochastic) provides higher values of 690 MMbbl of oil and 203 Mt of CO2. Results using simulation-based and commercial project data also show significant differences. The volume of CO2 injected, which affects the recovery factor, is usually 100% hydrocarbon pore volume (HCPV) in simulation, while commercial projects have nowadays regularly increased from 30% to exceed the 100% threshold. A combination of these approaches avoids a resource-intensive estimation process and effectively provides a more realistic picture of the feasibility of applying CO2-EOR technologies.@en

Integrated energy system modeling tools predominantly focus on the (inter)national or local scales. The intermediate level is important from the perspective of regional policy making, particularly for identifying the potentials and constraints of various renewable resources. Additionally, distribution variations of economic and social sectors, such as housing, agriculture, industries, and energy infrastructure, foster regional energy demand differences. We used an existing optimization-based national integrated energy system model, Options Portfolio for Emission Reduction Assessment or OPERA, for our analysis. The modeling framework was subdivided into four major blocks: the economic structure, the built environment and industries, renewable energy potentials, and energy infrastructure, including district heating. Our scenario emphasized extensive use of intermittent renewables to achieve low greenhouse gas emissions. Our multi-node, regionalized model revealed the significant impacts of spatial parameters on the outputs of different technology options. Our case study was the northern region of the Netherlands. The region generated a significant amount of hydrogen (H₂) from offshore wind, i.e. 620 Peta Joule (PJ), and transmitted a substantial volume of H₂ (390 PJ) to the rest of the Netherlands. Additionally, the total renewable share in the primary energy mix of almost every northern region is ∼90% or more compared to ∼70% for the rest of the Netherlands. The results confirm the added value of regionalized modeling from the perspective of regional policy making as opposed to relying solely on national energy system models. Furthermore, we suggest that the regionalization of national models is an appropriate method to analyze regional energy systems.

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The oil industry needs to reduce CO₂ emissions across the entire lifecycle of fossil fuels to meet environmental regulations and societal requirements and to sustain its business. With this goal in mind, this study aims to evaluate the CO₂ mitigation potential of several bio-oil co-processing pathways in an oil refinery. Techno-economic analysis was conducted on different pathways and their greenhouse gas (GHG) mitigation potentials were compared. Thirteen pathways with different bio-oils, including vegetable oil (VO), fast pyrolysis oil (FPO), hydro-deoxygenated oil (HDO), catalytic pyrolysis oil (CPO), hydrothermal liquefaction oil (HTLO), and Fischer–Tropsch fuels, were analyzed. However, no single pathway could be presented as the best option. This would depend on the criteria used and the target of the co-processing route. The results obtained indicated that up to 15% of the fossil-fuel output in the refinery could be replaced by biofuel without major changes in the core activities of the refinery. The consequent reduction in CO₂ emissions varied from 33% to 84% when compared with pure equivalent fossil fuels replaced (i.e., gasoline and diesel). Meanwhile, the production costs varied from 17 to 31€/GJ (i.e., 118–213$/bbl_eq). Co-processing with VO resulted in the lowest overall performance among the options that were evaluated while co-processing HTLO in the hydrotreatment unit and FPO in the fluid catalytic cracking unit showed the highest potential for CO₂ avoidance (69% of refinery CO₂ emissions) and reduction in CO₂ emissions (84% compared to fossil fuel), respectively. The cost of CO₂ emissions avoided for all of the assessed routes was in the range of €99–651 per tCO₂.

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The use of CO₂ for enhanced oil recovery (CO₂-EOR) is a promising alternative for reducing the cost of carbon capture and storage (CCS). In this study the techno-economic potential of integrated CCS-EOR projects for reducing greenhouse gas (GHG) emissions in the Colombian oil industry is estimated. For this purpose, a source-sink matching process is carried out, including CO₂ capture potentials in sources from the petroleum, cement, power generation, and bioethanol industries, as well as from the CO₂ storage in suitable oil fields for EOR. The results indicate that a total of 142 million tons of carbon dioxide (MtCO₂) could be stored, while delivering 465 MMbbl through five CCS-EOR projects in four clusters identified around the country. The levelised cost for capture ranged between 12–209 €/tCO₂, followed by the cost of CO₂ during EOR operations with a variation of 24–59 €/tCO₂, and finally the CO₂ transport, from 1 €/tCO₂ to 23 €/tCO₂. The CO₂ mitigation potential of CCS-EOR represents 25 % of the forecasted oil industry emissions in Colombia for the period of 2025–2040. As compared to the intended nationally determined contribution (INDC) target set by the Colombian government, CCS-EOR projects could contribute 7 % of the total accumulated emissions reductions by 2040.

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Biomass feedstock can be used for the production of biofuels or biobased chemicals to reduce anthropogenic greenhouse gas (GHG) emissions. Earlier studies about the techno-economic performance of biofuel or biobased chemical production varied in biomass feedstock, conversion process, and other techno-economic assumptions. This made a fair comparison between different industrial processing pathways difficult. The aim of this study is to quantify uniformly the factory-gate production costs and the GHG emission intensity of biobased ethanol, ethylene, 1,3-propanediol (PDO), and succinic acid, and to compare them with each other and their respective fossil equivalent products. Brazilian sugarcane and eucalyptus are used as biomass feedstock in this study. A uniform approach is applied to determine the production costs and GHG emission intensity of biobased products, taking into account feedstock supply, biobased product yield, capital investment, energy, labor, maintenance, and processing inputs. Economic performance and net avoided GHG emissions of biobased chemicals depend on various uncertain factors, so this study pays particular attention to uncertainty by means of a Monte Carlo analysis. A sensitivity analysis is also performed. As there is uncertainty associated with the parameters used for biobased product yield, feedstock cost, fixed capital investment, industrial scale, and energy costs, the results are presented in ranges. The 60% confidence interval ranges of the biobased product production costs are 0.64–1.10 US$ kg ⁻¹ ethanol, 1.18–2.05 US$ kg ⁻¹ ethylene, 1.37–2.40 US$ kg ⁻¹ 1,3-PDO, and 1.91–2.57 US$ kg ⁻¹ succinic acid. The cost ranges of all biobased products partly or completely overlap with the ranges of the production costs of the fossil equivalent products. The results show that sugarcane-based 1,3-PDO and to a lesser extent succinic acid have the highest potential benefit. The ranges of GHG emission reduction are 1.29–2.16, 3.37–4.12, 2.54–5.91, and 0.47–5.22 CO _2eq kg ⁻¹ biobased product for ethanol, ethylene, 1,3-PDO, and succinic acid respectively. Considering the potential GHG emission reduction and profit per hectare, the pathways using sugarcane score are generally better than eucalyptus feedstock due to the high yield of sugarcane in Brazil. Overall, it was not possible to choose a clear winner, (a) because the best performing biobased product strongly depends on the chosen metric, and (b) because of the large ranges found, especially for PDO and succinic acid, independent of the chosen metric. To quantify the performance better, more data are required regarding the biobased product yield, equipment costs, and energy consumption of biobased industrial pathways, but also about the production costs and GHG emission intensity of fossil-equivalent products.

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The aviation industry accounts for more than 2% of global CO ₂ emissions. Biojet fuel is expected to make an essential contribution to the decarbonization of the aviation sector. Brazil is seen as a key player in developing sustainable aviation biofuels owing to its long-standing experience with biofuels. Nevertheless, a clear understanding of what policies may be conducive to the emergence of a biojet fuel supply chain is lacking. We extended a spatially explicit agent-based model to explore the emergence of a biojet fuel supply chain from the existing sugarcane–ethanol supply chain. The model accounts for new policies (feed-in tariff and capital investment subsidy) and new considerations into the decision making about production and investment in processing capacity. We found that in a tax-free gasoline regime, a feed-in tariff above 3 R$/L stimulates the production of biojet fuel. At higher levels of gasoline taxation (i.e., 2.46 R$/L), however, any feed-in tariff is insufficient to ensure the production of biojet fuel. Thus, at these levels of gasoline taxation, it is needed to introduce regulations on the production of biojet fuel to ensure its production. Given the current debate about the future direction of the biofuel policy in Brazil, we recommend further research into the effect of market mechanisms based on greenhouse gas emissions on the emergence of a Brazilian biojet fuel supply chain.

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The Brazilian government aims to increase the share of biofuels in the energy mix to around 18% by 2030, which implies an increase of ethanol production from currently 27 bln liters to over 50 bln liters per year. Biofuel policies play an important role in ethanol production, consumption, and investment in processing capacity. Nevertheless, a clear understanding of how current policies affect the evolution of the market is lacking. We developed a spatially-explicit agent-based model to analyze the impact of different blend mandates and taxes levied on gasoline, hydrous, and anhydrous ethanol on investment in processing capacity and on production and consumption of ethanol. The model uses land use projections by the PCRaster Land Use Change model and incorporates the institutions governing the actors’ strategic decision making with regard to production and consumption of ethanol, and the institutions governing the interaction among actors. From the investigated mix of policy measures, we find that an increase of the gasoline tax leads to the highest increased investments in sugarcane processing capacity. We also find that a gasoline tax above 1.23 R$/l and a tax exemption for hydrous ethanol may lead to doubling the production of ethanol by 2030 (relative to 2016).

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The oil and gas sector represents 39% of the world's total industrial final energy consumption, and contributes to around 37% of total greenhouse gas (GHG) emissions. This study investigates the potential for improvements in energy efficiency, and their implications for CO2 abatement, in the Colombian oil industry value chain. It also assesses the potential cost of conserved energy and mitigated CO2-eq. A bottom-up approach was used to identify energy efficiency measures based on an assessment of specific operational data at the process unit level. In total, 20 measures and technologies were identified and applied in 48 cases throughout the chain, representing energy savings of 15.8 PJ and GHG savings of 0.75 Mt CO2-eq per year. This accounts for 25% and 19% of the total energy consumption and GHG emissions, respectively. Ninety-six percent of the total energy savings come from measures that are already cost-effective and could be implemented in the short term. The results of this study offer a better understanding of the critical stages for energy and GHG savings potentials, as well as investment cost and revenue from a full value chain perspective, based on operational data processing.@en

CCS is considered a primary strategy to curb CO2 emissions. In many models, CCS is an essential technology to meet the 2◦C target [1]. Drastically reducing GHG emissions in the oil and gas industry will be needed to reach the 2◦C target as this industry is one of the five most energy and GHG intensive sectors [2]. The implementation of CCS technologies has been widely studied for the refining stage of the oil value chain. According to Berghout et al. [3], between 80 to 90% of GHG emissions in the refining industry could be reduced using CCS. However, the cost involved for its implementation, make it an unattractive pathway. CO2-EOR is currently a promising alternative to reduce CCS costs [4], and would be able to produce an oil with low-associated emissions during its recovery. This paper aims to estimate the techno-economic potential of CO2-EOR for reducing GHG emissions in the Colombian oil industry. For this purpose, the supply and demand of CO2 is studied by including the CO2 capture potential of the oil industry, cement, power generation and bioethanol, as well as the storage potential of CO2 through the oil recovery miscible process. The state-owned oil company Ecopetrol S.A. was taken as a case study, which represent the oil value chain in Colombia, with about 70% of crude oil produced and 100% of oil transported and refined in the country. A total of 193 Mt CO2 between 2025 and 2040 could be stored through CO2-EOR. As the projected emissions of the oil and gas industry in Colombia from 2010 to 2040 are estimated at 570 Mt CO2 [11], this is equivalent to a potential 34% reduction in CO2 emissions. This mitigation represents approximately 20% of the total reduction under the INDC target for the period 2025 to 2030.@en

This work compares the part load techno-economic performance of CO₂ capture from a combined cycle gas turbine (CCGT) using a membrane configuration with selective CO₂ recycle and using monoethanolamine (MEA) solvent, under the assumption of flexible power plant dispatch. This is the first time that the techno-economic performance of CO₂ capture technologies is compared assuming a flexible dispatch profile, and the assessment was done using a comprehensive, new, part load assessment approach. Analyzing the part load performance of CO₂ capture and storage (CCS) technologies is relevant because of significant changes in our power systems, dramatically reducing the utilization of thermal power plants. The technical performance of the configurations with and without CCS was simulated at steady state, at operating points between maximum continuous rating (100% gas turbine loading) and minimum stable load (35% gas turbine loading). The performance at these operating points was then aggregated into weighted averages to produce single performance indicators (specific CO₂ intensity, specific primary energy per tonne of CO₂ avoided (SPECCA), and levelized cost of electricity (LCOE)) over the dispatch profile of the power plant. The technical performance of the MEA configuration was favorable over the membrane configuration over the whole CCGT loading range. The MEA SPECCA increased from 3.02 GJ/(t of CO₂) at 100% GT loading to 3.65 GJ/(t of CO₂) at 35% GT loading; the membrane SPECCA increased from 3.35 to 4.20 GJ/(t of CO₂). The higher SPECCA of the membrane configuration is caused by the reduced gas turbine efficiency, due to the selective recycling of CO₂ to the GT. When equal GT efficiency was assumed for combustion with normal air and with CO₂ enriched air, the membranes' technical performance was comparable with that of MEA. The capital costs of the CCGT with membrane configuration were 35% higher than the CCGT with MEA configuration. That, and the 6 year replacement frequency of the membranes, led the membrane LCOE to be 10 €/(MW h) higher than the MEA LCOE, when calculated with the part load approach. The membrane LCOE was 8 €/(MW h) higher when a full load was assumed. The new part load approach proved instrumental in highlighting performance (differences) at flexible dispatch conditions and aggregating those into easy to understand performance indicators.

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This work describes an analysis of the techno-economic performance of CCGT’s with an advanced membrane concept and compares this with MEA postcombustion capture. The analysis is undertaken at part-load (off-design) conditions to mimic realistic power plant dispatch. It integrates the part-load performance of operating points from minimal stable load to maximum continuous rating into weighted single techno-economic performance indicators that allow comparing the performance of CCS technologies under more realistic conditions than full load.@en

This paper addresses the uncertainty and variability in techno-economic studies of carbon capture technologies, based on a detailed comparison of the results of different studies on postcombustion CO2 capture with advanced amines, and on an in-depth uncertainty analysis using a combination of sensitivity and pedigree analyses. The results show that despite efforts to harmonize capital cost estimates, the capital cost results of the same PCC carbon capture systems can still show large (65%) differences. This uncertainty may simply be inherent to early stage cost estimates. Amongst the most important causes for the variability shown in this work are differences in equipment sizing methods and purchased equipment cost estimates. This capital cost variability only mildly propagates into the Levelised Cost of Electricity and Cost of CO2 Avoided, more so in case of low power plant utilisation scenarios. To enhance insight into these uncertainties and enable their communication, the paper argues to use in-depth uncertainty evaluation for early stage techno-economic studies. It suggests to complement current practice of sensitivity analysis with pedigree analysis and to combine the results of both analyses in diagnostic diagrams. This may lead to more informed interpretation of the results of techno-economic studies, and helps focus techno-economic research efforts towards the parameters that most influence final performance indicators.@en

Biofuel production is not cost competitive and thus requires governmental intervention. The effect of the institutional framework on the development of the biofuel sector is not yet well understood. This paper aims to analyze how biofuel production and production capacity could have evolved in Germany in the period 1992–2014. The effects of an agricultural policy intervention (liberalization of the agricultural market) and a bioenergy policy intervention (a tax on biodiesel after an initial exemption) are explored. Elements of the Modeling Agent systems based on Institutional Analysis (MAIA) framework, complex adaptive systems (CAS) theory, and Neo Institutional Economics (NIE) theory were used to conceptualize and formalize the system in an agent-based model. It was found that an early liberalization of the agricultural market led to an under-production of biodiesel; a late liberalization led to the collapse of biodiesel production. An early introduction of the biodiesel tax led to stagnation in biodiesel production and production capacity; a late introduction led to an increase in sunk costs provided that the biofuel quota is binding. Also, a lack of agents’ adaptation mechanism to forecast prices led to a decrease in patterns of biodiesel production when an external shock was introduced in the system. In sum, we argue that system behavior is influenced by individual behavior which is shaped by institutions.

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this work addresses the methodological challenges of undertaking techno-economic assessments of very early stage (technology readiness level 3–4) CO₂ capture technologies. It draws lessons from a case study on CO₂ capture from a natural gas combined cycle with exhaust gas recycle and electric swing adsorption technology. The paper shows that also for very early stage technologies it is possible to conduct techno-economic studies that give a sound first indication of feasibility, providing certain conditions are met. These conditions include the availability of initial estimates for the energy use of the capture technology, either from bench scale measurements, or from rigorous process models, and the possibility to draw up a generic (high level) equipment list. The paper shows that for meaningful comparison with incumbent technologies, the performance of very early stage technologies needs to be projected to a future, commercial state. To this end, the state of the art methods have to be adapted to control for the development and improvements that these technologies will undergo during the R&D cycle. We call this a hybrid approach. The paper also shows that CO₂ capture technologies always need to be assessed in sympathy with the CO₂ source (e.g. power plant) and compression plant, because otherwise unreliable conclusions could be drawn on their feasibility. For the case study, it is concluded that electric swing adsorption is unlikely to become economically competitive with current technologies, even in a highly optimised future state, where 50% of the regeneration duty is provided by LP steam and 50% by electricity: the net efficiency of an NGCC with EGR and optimised ESA (49.3%_LHV; min–max 45.8–50.4%_LHV) is lower than that of an NGCC with EGR and standard MEA (50.4%_LHV). Also, investment and operational costs are higher than MEA, which together with ESA's lower efficiency leads to an unfavourable levelised cost of electricity: 103 €/MWh (min–max 93.89–117.31 €/MWh) for NGCC with ESA, versus 91 €/MWh for NGCC with MEA.

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The economic performance of biofuels supply chains depends on the interaction of technical characteristics as technological pathways and logistics, and social structures as actor behavior, their interactions and institutions. Traditional approaches focus on the technical problems only. Little attention has been paid to the institutional analysis of biofuel supply chains. This paper aims to extend the analysis of the effect of institutions on the emergence of biofuel supply chains by developing a conceptual framework that combines elements of complex adaptive systems, (neo) institutional economics and socio-technical systems theory. These elements were formalized into an agent-based model. The proposed method is illustrated by a case study on a biodiesel supply chain in Germany. It was found that the patterns in production capacity result from investors basing their decisions on optimistic perceptions of the market development that increase with a favorable institutional framework. Conversely, patterns in biodiesel production cannot be completely explained by this mechanism. The proposed framework assisted the model conceptualization phase and allowed the incorporation of social structures into the agent-based model. This approach could be developed further to provide insights on the effect of different future deployment strategies on bioenergy systems emergence and development.

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This study presents the development, application, and uncertainty analysis of a process simulation model for postcombustion CO₂ capture with an AMP/PZ solvent blend based on state of the art knowledge on AMP/PZ solvent technology. The development includes the improvement of the physical property models of a software package designed for simulation of acid gas treatment and CO₂ capture technologies. The improvement particularly consisted of regression of AMP-PZ binary interaction parameters. The model was applied to a case study of postcombustion CO₂ capture from an Advanced Super Critical Pulverized Coal power plant. Uncertainly analysis was undertaken by validating the physical property models against laboratory measurements reported in literature; by comparing model results with pilot study results, and by evaluating the strength of the model with a novel method called pedigree analysis. The results show that AMP/PZ postcombustion technology performs better than MEA technology on most performance indicators, e.g., the Specific Reboiler Duty is reduced from 3.6 GJ/t CO₂ for MEA, to 2.9 GJ/t CO₂ for AMP/PZ, and the specific cooling water requirement is reduced from 4.1 to 3.4 GJ/t CO₂. Only amine slip to the atmosphere increases with AMP/PZ technology: from 0.18 g/t CO₂ to 15.3 g/t CO₂, although this value is still within emission limits from existing regulatory frameworks. The coal power plant net efficiency with AMP/PZ capture amounts to a value of 37.2%_LHV, compared to 46.1%_LHV for the case without CCS and 36.2%_LHV in case of CCS with MEA. The uncertainty analysis shows that the model is well capable of predicting experimental and pilot result. The remaining uncertainty is mostly in the reaction kinetics and in the flowsheet design. Validation could be further improved, by more elaborate comparison to independent measures of physical properties, and by comparison of the model outputs to results from large demonstration or commercial size capture plants.

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This article aims to improve uncertainty evaluation of process models by combining a quantitative uncertainty evaluation method (data validation) with a qualitative uncertainty evaluation method (pedigree analysis). The approach is tested on a case study of monoethanolamine based postcombustion CO₂ capture from a coal power plant. Data validation was used to quantitatively assess the uncertainty of the inputs and outputs of the MEA model. Pedigree analysis was used to qualitatively assess the uncertainty in the current knowledge base on MEA carbon capture systems, the uncertainty in the MEA process model, and the uncertainty of the MEA model results. The pedigree review was done by 13 international experts in the field of postcombustion carbon capture with chemical solvents. The data validation showed that our MEA model is accurate in predicting specific reboiler duty, and CO₂ stream purity (4% and 1% difference respectively between model and pilot plant results), but in first instance it was less accurate in predicting liquid over gas ratio, and cooling water requirement (54% and 23% difference respectively between model and pilot plant results). The pedigree analysis complemented these results by showing that there was fairly high uncertainty in the thermodynamic, and chemistry submodels, as reflected in the low pedigree scores on most indicators. Therefore, the model was improved to better resemble pilot plant results. The results indicate that using a pedigree approach improved uncertainty evaluation in three ways. First, by highlighting sources of uncertainty that quantitative uncertainty analysis does not take into account, such as uncertainty in the knowledge base regarding a specific phenomenon. Second, by providing a systematic approach to uncertainty evaluation, thereby increasing the awareness of modeller and model user. And finally, by presenting the outcomes in easy to understand numerical scores and colours, improving the communication of model uncertainty. In combination with quantitative validation efforts, the pedigree approach can provide a strong method to gain deep insight into the strengths and weaknesses of a process model, and to communicate this to policy and decision-makers.

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This study aimed to analyze whether, and how, uncertainty influences the layout and costs of a CO2 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 CO2 price, tariff received per tonne of CO2 transported, the willingness, probability and moment that sources join the CO2 transportation network are incorporated in the analysis. The results show that uncertainty leads to higher required CO2 prices before investments in carbon dioxide capture and storage (CCS) are made. With a volatility of 47% in the CO2 price, the required CO2 price almost triples in comparison with the net present value approach. Hence, under uncertainty less sources are retrofitted with CCS and less CO2 is captured and stored over time. For instance, for the analyzed case study 31 Mt and 137 Mt CO2 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 CO2 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 CO2 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 CO2 price, reducing capture costs and facilitating cooperation between nearby sources.@en

The aim of this study is to assess whether the value of flexibility can influence the investment decision between CO₂ ship and pipeline transport and, therefore, the way the infrastructure develops. For this, the value of a carbon capture and storage project are calculated with the standard net present value (NPV) and with the least-squares Monte Carlo method, which is a real option approach (ROA).Results of the NPV and ROA show that ships are preferred for small volumes over large distances. For instance, for a design capacity of 2.5Mt/y, pipelines are preferred for 250km and ships for 500km. The ROA shows that the option value to abandon the project and to switch off the CO₂ capture unit temporarily are about 2-4 and 5 times as high for the ship compared to the pipeline configurations, respectively. The option to connect to another storage reservoir has a value of >1000M€ for the 10MtCO₂/y configurations. Consequently, this option turns the project values positive for the 10MtCO₂/y pipeline and shipping configurations over a distance of 250 and 500km.Overall, the value of flexibility did not change the preferred transportation mode from pipeline to ship transport, at least for the considered options to abandon the project, switch off the capture unit temporarily and switch to another storage reservoir. However, under the assumptions made, all 10MtCO₂/y cases were not profitable with the NPV approach, while they were profitable with the ROA.

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