Fig. 2: Levelized cost of water-conscious green methanol production (LCOM) by 2050 at 10% of the maximum technical potential.<\/b>![\"Fig.](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/08\/41467_2026_76865_Fig2_HTML.png\")
<\/p>\n
The maximum technical potential is derived by utilizing all available renewable energy potential for methanol production (see Methods). a<\/b> Spatial distribution of the LCOM worldwide. The considered regions are expected to face significant levels of water-stress by 205013<\/a>, making water-conscious production essential. All regions with LCOM greater or equal to 1400 \u20ac tMeOH\u22121 are colored the same, as are all regions with cost less than or equal to 600 \u20ac tMeOH\u22121. b<\/b> Analysis of the influence of open-field photovoltaic (OFPV) and wind full load hours (FLH) on the LCOM. The FLH of OFPV and wind plants are evaluated based on the utilized input supply profiles. c<\/b>, d<\/b> Analysis of the effect of average relative humidity and temperature on LCOM. The yearly average temperature and relative humidity are derived from the hourly resolved weather input data for the considered weather year 2018. A scientifically developed color scheme85<\/a> is used in this and the following figures to fairly represent the data and to make the figures universally readable. Country shapes from GADM26<\/a>.<\/p>\n The assessment of input data effects on LCOM reveals the full load hours (FLH) of the energy supply system as the most influential factor, with wind FLH being decisive for reaching the lowest LCOM. Regions with both low wind and OFPV FLH exhibit the highest production costs, while an increase in OFPV FLH can significantly reduce LCOM, reaching values as low as 800 \u20ac tMeOH\u22121. However, regions with poor wind resources are not able to reach the lowest production costs, independent of the OFPV FLH. Only when wind FLH exceed ~2500\u2009h a\u22121, LCOM in a range of 700\u2013800 \u20ac tMeOH\u22121 are observed. The lowest production costs are predominantly found in regions benefiting from both high wind and OFPV FLH, particularly those exceeding 3000 wind FLH and 1750 OFPV FLH (Fig.\u00a02b<\/a>). In contrast, the influence of average relative humidity and ambient temperature on LCOM is generally less severe (Fig.\u00a02c, d<\/a>). Although the highest LCOM values are observed in regions with particularly high relative humidity, some of the lowest costs also occur under such conditions. A slight trend suggests that increasing relative humidity tends to lead to higher LCOM, which can be attributed to the increased energy demand of DAC in humid regions, as detailed in Fig.\u00a09<\/a> and further analyzed later.<\/p>\n Several countries exhibit significant potential for water-conscious green methanol production exceeding 1 GtMeOH a\u22121, such as the USA, China and Australia (Fig.\u00a03a<\/a>). A global demand of 500 MtMeOH a\u22121, as forecasted for 205011<\/a>, can be met at average production costs of 604 \u20ac tMeOH\u22121. Figure\u00a03a<\/a> presents merit order curves for three regions with particularly low production costs \u2013 USA, Mauritania (MRT), and Australia (AUS) \u2013 as well as China (CHN), which currently accounts for 65% of global methanol demand, with further growth expected10<\/a>. Spain (ESP) and France (FRA) are included to assess the potential for domestic European production. Potential in Spain and France is limited to about 48 MtMeOH a\u22121 at 10% of the maximum technical potential, with an average cost of 872 \u20ac tMeOH\u22121, making them the least competitive among the selected regions. In contrast, Australia has the highest potential, reaching about 4.8 GtMeOH a\u22121 at 10% of the maximum technical capacity. It is found that Australia alone could meet the projected global methanol demand of about 500 MtMeOH a\u22121 by 205011<\/a> at an average cost of 693 \u20ac tMeOH\u22121 and a marginal cost of 700 \u20ac tMeOH\u22121. However, considering all regions, the projected demand of 500 Mt a\u22121 could be supplied at an average cost of 604 \u20ac tMeOH\u22121 (Supplementary Note\u00a02<\/a>). The USA and China are found to have a comparable production potential, though costs are generally lower in the USA. Among the investigated regions, Mauritania emerges as the most cost-competitive location, with marginal (average) cost of 656 (636) \u20ac tMeOH\u22121 at production of 100 MtMeOH a\u22121.<\/p>\n Fig. 3: Specific analysis of selected regions.<\/b> a<\/b> Supply curves showing the (cost-) potential for six countries. b<\/b> Cost contributions of the different system components to the total LCOM in nine regions across the globe (USA \u2013 United States of America, MEX \u2013 Mexico, ARG \u2013 Argentina, FRA \u2013 France, SAU \u2013 Saudi Arabia, ZAF \u2013 South Africa, IND \u2013 India, CHN \u2013 China, AUS \u2013 Australia, ESP \u2013 Spain, MRT \u2013 Mauretania). The regions are encoded according to the second administrative level, as defined in the global administrative boundaries dataset26<\/a>. SOEC Solid Oxid Electrolysis Cell, OFPV open-field photovoltaic, DAC direct air capture.<\/p>\n The cost contribution of the process chain \u2013 including DAC, SOEC and methanol synthesis \u2013 varies between about 300 \u20ac tMeOH\u22121 in a selected US region to over 500 \u20ac tMeOH\u22121 in an Indian region (Fig.\u00a03b<\/a>). While the cost contribution of the DAC unit remains relatively consistent across regions, the SOEC contribution shows significant variation, suggesting substantial oversizing in some regions. The cost of the energy supply system, comprising OFPV and onshore wind, varies significantly depending on the region, ranging from 200 to 500 \u20ac tMeOH\u22121. In most cases, hybrid OFPV and wind systems are implemented to minimize costs. However, notable exceptions include the selected region in Mexico where wind potential is absent, leading to an exclusively OFPV-powered system.<\/p>\n In OFPV-dominated regions, substantial oversizing of the SOEC and methanol synthesis unit is observed. Generally, battery storage is most implemented in OFPV-dominated regions. When the share of installed PV capacity exceeds 80% of the total power supply capacity, battery storage deployment increases significantly, along with notable SOEC oversizing (Supplementary Fig.\u00a013<\/a>). The methanol synthesis unit is typically oversized in regions where SOEC oversizing occurs. This is attributed to the assumed flexibility of the system, where operating the energy-intensive electrolysis during periods of high power availability and shutting it down during periods of low supply proves to be the most cost-effective strategy. Due to the high cost and energy demand of syngas compression and storage, the SOEC and methanol synthesis units operate in a coupled manner. In wind-rich regions, all system components generally follow wind power availability, with additional operational peaks during OFPV generation periods (Supplementary Fig.\u00a017a, c<\/a>). Large-scale battery storage is not found to be economically viable under any conditions. If built, battery storage is primarily used to increase the capacity factor of the DAC unit (Supplementary Fig.\u00a017b<\/a>). While the DAC unit is capital intensive and an increased capacity factor lowers costs, this might also be explained by potentially less energy-intensive conditions for DAC operation during times of low or no power supply, or by the need to shift DAC operation due to water co-adsorption restrictions. These aspects are more deeply investigated in the following section.<\/p>\n Weather dependency of direct air capture<\/p>\n Since the only material input to DAC is ambient air, its energy requirements and costs depend strongly on atmospheric conditions such as CO2 concentration, pressure, temperature, and humidity22<\/a>,27<\/a>,28<\/a>,29<\/a>. These parameters vary substantially across both space and time, with pronounced fluctuations not only seasonally but also on an hourly scale. Thus, in this analysis, we account for hourly variations in temperature and humidity at each location. While site-specific studies have demonstrated the value of incorporating hourly CO2 concentration dynamics29<\/a>, such data is not yet available on a global scale. We therefore begin by examining how weather impacts DAC performance and system costs, before investigating its implications for water supply under worldwide weather variability.<\/p>\n Varying weather conditions significantly impact the cost contribution of the DAC plant, with high relative humidity leading to contributions below 100 \u20ac tMeOH\u22121, while low-humidity conditions can increase contributions to over 200 \u20ac tMeOH\u22121 (Fig.\u00a04a<\/a>). This can be attributed to the higher productivity of the DAC plant under humid conditions, where water adsorption facilitates CO2 uptake (see Methods). The lowest cost contributions are observed in regions with average relative humidity above 60%, spanning a broad temperature range. In contrast, the highest costs are primarily found in regions with low average humidity (<30%) and high temperatures (>20\u2009\u00b0C). This is a direct result of reduced DAC productivity under such conditions (Fig.\u00a09<\/a>), necessitating significant oversizing of the DAC unit in these regions. Additionally, elevated costs are observed in regions with relative humidity around 70% and temperatures below \u22125\u2009\u00b0C. However, since these conditions do not negatively impact DAC productivity, the oversizing in these regions cannot be attributed to the weather dependency of the DAC plant and is instead explained by poor conditions for the renewable energy supply system.<\/p>\n Fig. 4: Influence of the prevailing weather conditions on the direct air capture (DAC) unit.<\/b> a<\/b> DAC cost contribution (without energy costs) to the total levelized cost of methanol (LCOM). b<\/b> Influence of the relative humidity on total system energy efficiency. As the DAC plant is the only component of the process chain directly affected by weather conditions, the variations in system efficiency are mainly attributed to the DAC unit.<\/p>\n The overall energy efficiency of the system is strongly influenced by relative humidity, which significantly impacts energy demand of the DAC unit (Fig.\u00a04b<\/a>). Total system efficiencies range from 39% to 49% (based on the lower heating value of methanol), emphasizing the importance of modeling DAC\u2019s weather dependency. Since the energy demand of other process chain components is not affected by the prevailing weather conditions, variations in system efficiency are primarily driven by the fluctuating energy demand of the DAC unit. Other factors, such as the efficiency of the air-source heat pump, have only a minor effect. In regions with average relative humidity below 25%, low system efficiencies in a range of 41\u201343% are reached. This is primarily due to the elevated temperature in these regions which leads to an increased power demand for DAC operation, particularly for fans and vacuum pumps (Fig.\u00a09<\/a>). As relative humidity increases, system efficiency generally improves, peaking in regions with average relative humidity between 30% and 60%. However, beyond 60%, a clear trend emerges where higher relative humidity correlates with decreasing efficiency. The lowest observed system efficiency, ~39.5%, occurs in a region where the average relative humidity approaches 90%. This is explained by the substantial increase in DAC\u2019s heat demand at higher relative humidities (Fig.\u00a09a<\/a>). While the geospatial analysis reveals clear relationships between weather conditions, cost contributions and system efficiency, these observations should primarily be interpreted as correlations rather than direct causal relationships. The proposed explanations are based on the underlying process mechanisms represented in the model; however, multiple interdependent factors simultaneously influence system design and operation, meaning that individual parameter effects cannot always be isolated clearly. This limitation should also be considered in the following assessment of water supply from DAC.<\/p>\n To produce the required synthesis gas for methanol synthesis, the SOEC needs water and CO2 as a feedstock, both directly provided from ambient air by the DAC unit. However, as discussed in the Methods section, a water supply ratio below 0.841 tH2O tCO2\u22121 leads to insufficient water availability for synthesis gas production and, consequently, methanol synthesis (Fig.\u00a09c, e<\/a>). Notably, only 590 out of the 20,265 analyzed regions are affected by an average water supply ratio below 0.841 tH2O tCO2\u22121 (Fig.\u00a09e<\/a>). Although DAC plants in these regions would be unable to supply sufficient water if operated continuously throughout the year, they nonetheless experience periods with desorption ratios exceeding 0.841 tH2O tCO2\u22121 (Supplementary Fig.\u00a08<\/a>). Since no alternative water source is available in the analyzed system, a shortfall in water supply necessitates either drawing from buffer storage or shifting DAC operation to periods with sufficiently high water co-adsorption. In any case, the DAC unit must provide the total water requirement for methanol production over the course of a year. To assess potential operational constraints related to DAC water supply, the yearly excess water production per produced unit of methanol is analyzed.<\/p>\n In most regions, water supply by DAC does not impose constraints on water-conscious green methanol production, and excess water production is generally observed. In these regions, an additional water source, i.e. seawater desalination, would not have changed the system design, operation and resulting cost, since sufficient water from DAC operation is available anyway. Therefore, these regions are considered suitable for DAC-based production, as the water demand can be fully met through atmospheric capture without additional water-related constraints. The highest excess water production, exceeding 2.5 tH2O tMeOH\u22121, is found in Indonesia and Peru (Fig.\u00a05a<\/a>), driven by the high relative humidity in these regions (Supplementary Fig.\u00a07b<\/a>). Regions with low excess water production are distributed globally, including parts of China, Chile, Africa and the USA. In regions where excess water production is exactly zero, system design and operational adjustments \u2013 such as building additional energy storage and shifting DAC operation to periods of higher water co-adsorption \u2013 may be necessary to ensure sufficient water supply, given that no other water source is available in the investigated system. Among the 20,265 analyzed regions, 606 show no excess water production, primarily located in the USA, Northern Africa, the Middle East and Australia (Fig.\u00a05a<\/a>). These 606 regions are nearly identical to the 590 regions where the average water co-adsorption ratio of a continuously operated DAC plant is below 0.841 tH2O tCO2\u22121 (Fig.\u00a09e<\/a>). Additionally, some regions with a theoretically sufficient average water supply ratio are affected, since cost-optimal DAC operation deviates from continuous operation and often is observed to be during times of high power supply and lower water co-adsorption, e.g. during daytime in summer. To quantify the impact of water supply constraints, these 606 regions were again optimized, this time in an unrestricted scenario, allowing for free water import. In the following, the resulting system configurations and LCOM values from this scenario are compared to the base scenario to evaluate potential differences.<\/p>\n Fig. 5: Analysis of the water supply by direct air capture (DAC) and the effect of insufficient water supply on the system.<\/b> a<\/b> Specific excess water production in all considered regions. Regions with no excess water production are indicated by red hatches. b<\/b> Analysis of the levelized cost of methanol (LCOM) increase due to water supply restrictions. To quantify the increase, regions with zero excess water production were again optimized, this time in an unrestricted scenario allowing for free water import. c<\/b>, d<\/b> Additional built battery and heat storage to overcome water supply restrictions. Country shapes from GADM26<\/a>.<\/p>\n Regions experiencing extremely low levels of relative humidity, especially in combination with high temperatures, are potentially less suited for water-conscious green methanol production with LCOM increases up to 80 \u20ac tMeOH\u22121 resulting from water supply constraints (Fig.\u00a05b<\/a>). Notably, regions with high LCOM increases due to water supply restrictions are exclusively landlocked regions far from the shore, such as southern Algeria, northern Niger, Afghanistan or northern China (Supplementary Fig.\u00a014<\/a>). Three distinct operational zones for DAC-based water supply can be revealed (Fig.\u00a05b<\/a>). In most regions, average temperature and relative humidity do not impose significant constraints, allowing unrestricted operation. A second zone, where only a slight LCOM increase (<20 \u20ac tMeOH\u22121) is observed, corresponds to regions with relative humidity levels of ~30\u201335% at 20\u2009\u00b0C or 40\u201345% at 0\u2009\u00b0C. Here, DAC can generally provide sufficient water, requiring only minor operational adjustments and system design modifications. In contrast, regions with even lower relative humidity at these temperature levels face severe constraints on DAC water supply. This necessitates substantial system design adaptations and operational shifts, especially increased battery and heat storage capacity (Fig.\u00a05c, d<\/a>), leading to significantly higher LCOM. These system modifications enable a shift in DAC operation to periods of higher relative humidity, ensuring sufficient water adsorption. As a result, areas experiencing these specific temperature and relative humidity conditions may be less suitable for water-conscious green methanol production enabled by solid sorbent DAC.<\/p>\n Water supply remains robust to DAC model choice<\/p>\n Multiple DAC process models have been developed by research groups worldwide, differing in sorbent selection, process design, and scope of investigation. However, only a limited number of studies report comprehensive data on energy demand, productivity and, most importantly, water desorption ratio under a wide range of ambient conditions22<\/a>,28<\/a>,30<\/a>. Given the differences in modeling approaches and underlying assumptions, the choice of DAC model can substantially influence overall system performance. This is particularly relevant since the DAC plant supplies all necessary feedstocks for downstream processes \u2013 variations in water co-adsorption behavior might therefore render the entire system infeasible. To investigate the influence of DAC model selection on the system performance, we utilize data from a second, alternative DAC model28<\/a> and integrate this into the system under investigation (see Methods). By keeping all other system components constant and re-optimizing the total system for all 20,265 regions, the influence of DAC model choice can be evaluated. The results of this alternative scenario, from here on referred to as the altDAC scenario, are compared against the base scenario.<\/p>\n In the altDAC scenario, substantial additional excess water production is observed relative to the base scenario across nearly all regions and ambient conditions (Fig.\u00a06a<\/a>). Only 13 out of the 20,265 regions exhibit lower excess water production relative to the base scenario, all located at high elevations in central Asia. These regions experience high average relative humidity (60\u201380%) at low average temperatures approaching \u221210\u2009\u00b0C, yet they are not subject to insufficient water supply. Regions facing insufficient water supply in the base scenario generally experience no or a reduced shortfall when the alternative DAC model is applied. Specifically, while 606 regions exhibit potential water insufficiency in the base scenario, only 261 regions show zero excess water production in the altDAC scenario. A comparison of the weather-dependent water desorption ratio of the base DAC model (Fig.\u00a09<\/a>) and the alternative DAC model (Supplementary Fig.\u00a011<\/a>) confirms that the alternative DAC model consistently supplies more water from ambient air. Consequently, the proposed system remains feasible and direct water supply from ambient air via DAC for hydrocarbon production is validated as a robust approach.<\/p>\n Fig. 6: Evaluation of direct air capture (DAC) model choice on system performance and feasibility.<\/b> The scenario with an alternative DAC model (altDAC) is compared against the base scenario. a<\/b> Additonal excess water production in the altDAC scenario compared to the base scenario at varying ambient conditions. b<\/b> Total system efficiency of the altDAC and base scenario depending on average relative humidity. c<\/b> Deviations in resulting levelized cost of methanol (LCOM) when comparing altDAC and base scenario. d<\/b>, e<\/b> Additional cost contributions by different system components (Sources include wind & open-field photovoltaic plants, HP includes all heat pumps and EH all electric heaters) in the altDAC scenario.<\/p>\n Nevertheless, a comparison of total system efficiency and the resulting LCOM for both scenarios emphasizes the significant impact of DAC model choice. At low relative humidity, particularly in combination with elevated temperatures, the alternative DAC model requires less heat (Supplementary Fig.\u00a011<\/a>), resulting in an overall higher system efficiency and lower LCOM (Fig.\u00a06b, c<\/a>). However, at higher relative humidity, the heat demand of the alternative DAC model increases severely, resulting in an overall reduction of the total system efficiency (Fig.\u00a06b<\/a>, Supplementary Fig.\u00a011<\/a>). The reduced system efficiency at higher relative humidity translates into a significant increase in LCOM, reaching ~40% under extreme conditions (Fig.\u00a06c<\/a>). The additional cost is primarily driven by the need for greater energy input from renewable sources (wind and PV) and by the integration of additional conversion technologies, including heat pumps and electric heaters (Fig.\u00a06d, e<\/a>). Despite these substantial deviations under extreme ambient conditions, more than 71% of regions exhibit LCOM variations of less than 15%.<\/p>\n The pronounced differences between the two DAC models can largely be attributed to the substantially higher predicted water uptake and higher resulting heat demand of the alternative model compared to the base DAC model. The reasons for these deviations are manifold and cannot be attributed to a single factor, since both DAC models are based on different sorbents, adsorption formulations, and process assumptions. In particular, the alternative DAC model28<\/a> employs humidity-dependent reaction mechanisms and adsorption kinetics for a proprietary amine-functionalized sorbent28<\/a>,31<\/a>. In contrast, the base scenario model22<\/a> applies a co-adsorption model32<\/a>, where adsorbed water directly influences the equilibrium CO2 uptake of Lewatit22<\/a>. In addition, the operational strategies of both studies differ considerably. Cai et al. evaluate DAC performance using fixed cycle times under all ambient conditions28<\/a>, whereas Jajjawi et al. optimize adsorption and desorption durations as a function of temperature and humidity to minimize the specific energy demand. As discussed by Cai et al., fixed cycle times under humid conditions lead to incomplete desorption steps, reducing effective CO2 productivity28<\/a>. This results in higher water-to-CO2 ratios and substantially larger specific heat demands. The base model, in contrast, can partially compensate for unfavorable adsorption conditions through dynamic cycle adaptation, reflected by longer desorption durations under humid conditions22<\/a>, leading to a lower co-adsorption ratio and specific heat demand.<\/p>\n Overall, the observed differences reflect the sensitivity of DAC performance predictions to the underlying sorbent properties, co-adsorption formulations, and operational assumptions. Importantly, despite the considerable deviations in energy demand and LCOM under extreme climatic conditions, the central conclusion of this work remains robust: both DAC models consistently indicate that DAC-based water supply is sufficient for water-conscious methanol production across a wide range of regions and environmental conditions.<\/p>\n Air cooling as a water-conscious opportunity<\/p>\n To enable water-conscious methanol production, a cooling system that operates without water consumption is crucial, besides supply of the water feedstock. Generally, waste heat is primarily generated in the SOEC and methanol synthesis unit, totaling in about 1.34 MWh tMeOH\u22121. This heat must either be integrated into the process, i.e. by upgrading it via a heat pump for use in the DAC unit, or dissipated through an air cooling system (see Methods).<\/p>\n Air cooling has only a minor impact on the total LCOM and appears to be a promising cooling solution (Fig.\u00a07a<\/a>). Overall, the cost contribution remains below 5 (3) \u20ac tMeOH\u22121 in 18,538 (14,200) out of the 20,265 analyzed regions. Notably, the lowest cost contributions are observed in Northern Africa and the Middle East. While these regions experience high average air temperatures (Supplementary Fig.\u00a07a<\/a>), which could suggest a need for increased air cooling capacity (see Methods), the opposite is observed. Instead, less waste heat is dissipated through air cooling, and a larger share is integrated into the process via a heat pump (Fig.\u00a07b<\/a>). This indicates that in regions with elevated temperatures, waste heat is preferentially upgraded and utilized rather than cooled, as the efficiency and cost-effectiveness of the air cooling system declines at higher temperatures (see Supplementary Methods). In some regions, more than 90% of the total generated heat is from upgraded waste heat, demonstrating that a substantial portion of the DAC unit\u2019s heat requirement \u2013 ranging from 1.5 to 2.7 MWh tMeOH\u22121, depending on the region \u2013 can be covered by waste heat integration. In colder regions, generally, a significant share of total heat generation comes from electric boilers (Fig.\u00a07c<\/a>). In these cases, a combination of air cooling and electric boilers is more cost-effective than utilizing waste heat pumps, as the air cooling system operates efficiently at lower temperatures, and electric boilers have substantially lower capital costs compared to heat pumps. The lower efficiency of electric boilers does not present a major drawback, as they are primarily operated during periods of high power supply (Supplementary Figs.\u00a017<\/a>, 18<\/a>). To balance heat supply and waste heat integration, relatively large heat storage is used (Supplementary Fig.\u00a018<\/a>).<\/p>\n Fig. 7: Analysis of the air cooling and heat supply system.<\/b> a<\/b> Cost contribution of the air cooling system (capex and fixed opex, without energy cost) to the total levelized cost of methanol (LCOM). All regions with cost contribution greater than or equal to 6 \u20ac tMeOH\u22121 are colored the same. The highest observed cost contribution is about 30 \u20ac tMeOH\u22121. b<\/b> Analysis of the waste heat pathways in all considered regions dependent on the average ambient air temperature. Besides being integrated by a heat pump, the waste heat could also directly be used for CO2 gasification. c<\/b> Share of the electric boiler in total heat generation. Country shapes from GADM26<\/a>.<\/p>\n Sensitivity analysis and alternative process chains<\/p>\n Figure\u00a08a<\/a> illustrates the influence of changes in various parameters on the resulting LCOM compared to the base case. The discount rate has the most significant impact, with changes of ~150 \u20ac tMeOH\u22121 for the corresponding region in India and around 75 \u20ac tMeOH\u22121 for the selected region in Argentina. As anticipated, regions with higher overall costs are more significantly affected by changes in the discount rate. The second strongest influence comes from changes in the capital expenditures (capex) of OFPV. Interestingly, the region exclusively powered by OFPV (MEX.16.24_1) is not the most sensitive to changes in the capex of OFPV units. While a\u2009\u00b1\u200910% change in OFPV capex results in a shift of about \u00b140 \u20ac tMeOH\u22121 for this region, it causes a\u2009\u00b1\u200945 \u20ac tMeOH\u22121 shift in the Indian region. This is explained by the high share of OFPV in IND.34.18_1, combined with fewer FLH than in MEX.16.24_1 (Supplementary Fig.\u00a09<\/a>), which requires more installed capacity. A change in wind capex leads to LCOM variations of up to \u00b130 \u20ac tMeOH\u22121 across the analyzed regions, with CHN.26.16_1 experiencing the greatest impact \u2013 greater than the exclusively wind-powered region in Argentina \u2013 again due to the difference in FLH. Changes in the capex of SOEC lead to LCOM variations of 15 to 30 \u20ac tMeOH\u22121, while changes in DAC capex result in relatively consistent LCOM differences of about 10 \u20ac tMeOH\u22121. The smaller variation between regions can be attributed to the generally lower oversizing of DAC units, as discussed above. Finally, changes in the capex of the battery affect only OFPV-dominated regions, with relatively minor effects on LCOM, about 5 \u20ac tMeOH\u22121.<\/p>\n Fig. 8: Sensitivity analysis for the investigated system.<\/b> a<\/b> Variation of input parameters and resulting differences in levelized cost of methanol (LCOM) compared to the base case. The capex of key system components, such as open-field photovoltaic (OFPV), onshore wind, direct air capture (DAC), solid oxide electrolysis (SOEC), and battery, were varied by \u00b110%. Additionally, the impact of different discount rates was evaluated, with scenarios considering a discount rate of 6% and 10%, compared to the base discount rate of 8%. Sensitivity analysis was performed across five distinct regions. Three of these regions are powered by hybrid OFPV-wind systems (USA.51.4_1, IND.34.18_1, CHN.26.16_1), one region relies exclusively on OFPV (MEX.16.24_1), and one region is solely powered by wind (ARG.20.6_1). b<\/b> Variation of the DAC water supply ratio and influence on the resulting LCOM. The regions with the lowest average water ratio (ARG.17.15_1) the one with the highest (PER.17.3_1) and one with a moderate ratio (DEU.1.1_1) are colored. Additionally, 30 regions with varying humidity are depicted in gray to highlight the variance. c<\/b> Comparison to the alternative process chain with polymer electrolyte membrane (PEM) electrolysis and direct CO2-hydrogenation for selected regions (USA \u2013 United States of America, MEX \u2013 Mexico, ARG \u2013 Argentina, FRA \u2013 France, SAU \u2013 Saudi Arabia, ZAF \u2013 South Africa, IND \u2013 India, CHN \u2013 China, AUS \u2013 Australia). The regions are encoded according to the second administrative level, as defined in the global administrative boundaries dataset26<\/a>.<\/p>\n In addition to the capex sensitivities shown in Fig.\u00a08a<\/a>, a separate sensitivity analysis was conducted to assess the influence of potentially increased maintenance and replacement requirements by increasing the annual operational expenditures (opex) of both DAC and SOEC systems from 4% in the base scenario to 6%. For SOEC systems, the increased opex can, for instance, be interpreted as a simplified representation of higher stack replacement costs resulting from intermittent operation and associated degradation. For DAC systems, it may reflect increased maintenance requirements under harsh environmental conditions, such as enhanced filter maintenance due to dust exposure. The results show a comparatively moderate impact on LCOM, with increases of ~1\u20132% for higher DAC opex and 3\u20134% for higher SOEC opex. While these effects are not negligible, they indicate that even increased maintenance and replacement requirements remain economically manageable within the investigated system configurations.<\/p>\n Given the study\u2019s focus on evaluating water supply from DAC under varying ambient conditions and the associated uncertainties, Fig.\u00a08b<\/a> shows the effect of reduced or increased water-to-CO2 ratios on the resulting LCOM across multiple regions. In the region with the lowest average water ratio in the base scenario (ARG.17.15_1, 0.48 tH2O tCO2\u22121), reducing the water supply ratio to 75% of the base case increases the LCOM by about 12% due to water constraints and the required system adaptations discussed above. In contrast, the humid region with the highest average water ratio (PER.17.3_1) remains unaffected even when the water ratio is reduced to 40% of the original value. Regions with moderate climates, such as DEU.1.1_1 in central Europe, are also insensitive to substantial reductions in the water ratio. Overall, most regions maintain sufficient water supply even under reduced water ratios, supporting the potential of DAC-based water supply despite uncertainties in future developments.<\/p>\n Besides changes of single parameters, the system under investigation is compared against an alternative process chain, consisting of polymer electrolyte membrane (PEM) electrolysis in combination with direct CO2-hydrogenation. This combination is often found in scientific literature7<\/a>,18<\/a> and might be a relevant alternative, especially considering the higher maturity and lower capital cost of PEM electrolysis compared to SOECs33<\/a>.<\/p>\n The PEM-based system achieves lower LCOM in all regions, particularly in regions dominated by OFPV, where reductions of up to 150 \u20ac tMeOH\u22121 are observed (Fig.\u00a08c<\/a>). The primary reason for these significant reductions in cost is the cost contribution of PEM electrolysis compared to SOEC. This is attributed to the generally lower specific capex of PEM electrolysis, as well as the reduced capacity required, given that PEM electrolysis only performs water electrolysis, unlike SOEC, which performs co-electrolysis necessitating more installed capacity. Additionally, the DAC unit contributes less to the total LCOM in the PEM-based system, as the required capacity is lower. This is because the direct CO2-hydrogenation unit requires less carbon input compared to the CO-hydrogenation process used in the SOEC-based system (see Methods). Besides the obvious cost reduction potential, it should be noted that large-scale CO2-hydrogenation (>500\u2009kt a\u22121) has not yet been implemented12<\/a>. While the PEM-based process seems promising, the significantly higher hydrogen-to-carbon ratio required for direct CO2-hydrogenation could lead to a substantially increased needed water ratio of up to 1.23 tH2O tCO2\u22121, potentially resulting in water supply restrictions. Additionally, PEM electrolysis requires ultra-pure water34<\/a>, necessitating additional purification steps. Although the associated energy demand and cost increase are relatively small \u2013 ~0.2% additional energy demand and 0.3\u20130.5% additional cost (Supplementary Fig.\u00a016<\/a>) \u2013 the purification process introduces further water losses, which can increase the overall LCOM in arid and water-constrained regions by more than 10% (Supplementary Fig.\u00a016<\/a>). Combined with the higher cooling demand of PEM electrolysis, this process chain may therefore be less suitable for water-conscious production in hot and dry climates. Consequently, while PEM-based production appears promising at first glance, these aspects require further detailed investigation to fully evaluate its potential.<\/p>\n","protected":false},"excerpt":{"rendered":"Levelized cost of water-conscious green methanol production The geospatial analysis of over 20,000 regions reveals a wide range…\n","protected":false},"author":2,"featured_media":654605,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[269],"tags":[67044,18,3296,184298,440,1099,19,17,1100,961,133,37277],"class_list":["post-654604","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-carbon-capture-and-storage","tag-eire","tag-energy-and-society","tag-energy-modelling","tag-environment","tag-humanities-and-social-sciences","tag-ie","tag-ireland","tag-multidisciplinary","tag-renewable-energy","tag-science","tag-water-resources"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/117152952156892617","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/654604","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=654604"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/654604\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/654605"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=654604"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=654604"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=654604"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Fig.](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/08\/41467_2026_76865_Fig3_HTML.png\")
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