Interview Questions for Lifecycle Analysis for Hydrogen Systems

A typical Lifecycle Assessment (LCA) for a hydrogen production system encompasses the entire life cycle, from resource extraction and processing to manufacturing, operation, transportation, storage, end-use, and ultimately, disposal or recycling. It’s not just about the production process itself; it considers the environmental impacts associated with every stage. Think of it like tracing a hydrogen molecule’s journey from its ‘birth’ in a production facility to its ‘death’ after fulfilling its intended purpose. The scope will vary depending on the specific system and the goals of the assessment, but usually includes a cradle-to-grave or cradle-to-gate perspective.

For instance, an LCA for a hydrogen fuel cell vehicle would cover the hydrogen production, the fuel cell’s manufacturing and operation, and the vehicle’s production and end-of-life management. A more narrowly focused study might only examine the production stage of a specific hydrogen electrolysis system.

The hydrogen lifecycle can be broken down into several key stages, each with its own environmental footprint:

• Production: This involves generating hydrogen using various methods like electrolysis (using electricity to split water), steam methane reforming (SMR) (using natural gas), or gasification of biomass. SMR, for example, releases significant greenhouse gases, including CO2 and methane. Electrolysis, powered by renewables, offers a cleaner alternative, but its electricity generation and equipment manufacturing still have environmental consequences.
• Purification & Compression: After production, hydrogen requires purification to remove impurities and compression for storage and transport. These steps consume energy, adding to the overall environmental burden.
• Storage: Hydrogen can be stored as a compressed gas, cryogenic liquid, or chemically bound in a material like ammonia. Each method has different energy requirements and associated environmental impacts (e.g., material extraction and processing for storage tanks).
• Transportation: Transporting hydrogen, whether via pipelines, trucks, ships, or specialized trailers, incurs emissions from the energy consumed and the manufacturing of the transport infrastructure.
• End-Use: Finally, the hydrogen is used in applications like fuel cells in vehicles, industrial processes, or power generation. The end-use efficiency and waste disposal methods also influence the overall LCA.

It’s crucial to understand that the environmental impacts of each stage are interconnected and need to be considered holistically.

Key environmental indicators assessed in hydrogen LCA studies include:

• Greenhouse gas (GHG) emissions: This is often the most crucial indicator, usually expressed as CO2 equivalents (CO2e), accounting for the global warming potential of various gases.
• Acidification potential: Measures the contribution to acid rain.
• Eutrophication potential: Assesses the impact on water bodies due to excess nutrients.
• Ozone depletion potential: Quantifies the impact on the ozone layer.
• Resource depletion: Considers the consumption of non-renewable resources like fossil fuels and minerals.
• Land use: Evaluates the impact on land use changes due to infrastructure development, energy source production, and material extraction.
• Water consumption: Measures the amount of water used in the entire process.

The specific indicators chosen will depend on the purpose of the study and the available data.

Assessing the carbon footprint of different hydrogen production pathways involves quantifying the GHG emissions associated with each stage. For electrolysis using renewable electricity, the carbon footprint will largely depend on the emissions from the electricity generation (e.g., solar, wind, hydropower) and the manufacturing of the electrolyzer itself. For SMR, emissions are directly related to the natural gas used in the process, and indirectly linked to the natural gas extraction and processing.

The assessment involves collecting data on energy consumption, emission factors (grams of CO2e per kWh of electricity, for example), and material usage at each stage. Life cycle inventory (LCI) software is often used to model the processes and calculate the overall GHG emissions. For example, a study might compare the carbon footprint of green hydrogen (produced via electrolysis with renewable energy) to blue hydrogen (produced via SMR with carbon capture and storage) by calculating the CO2e emissions per kg of hydrogen produced. This comparison helps in evaluating which pathway is more sustainable in a given context.

Quantifying the life cycle impacts of hydrogen storage and transportation presents several challenges:

• Energy losses during compression and liquefaction: Significant energy is required for compressing hydrogen to high pressures or liquefying it, resulting in added emissions. Accurately accounting for these energy losses is crucial.
• Material selection and manufacturing: Different storage materials and transportation infrastructure (pipelines, tanks, etc.) have varying environmental impacts due to material extraction, processing, and manufacturing.
• Leakage rates: Hydrogen is a small molecule and can leak from storage and transportation systems. These leaks contribute to emissions of a potent greenhouse gas, methane, as well as impact air quality.
• Data scarcity: Reliable data on energy consumption and emissions for specific storage and transport technologies can be limited, leading to uncertainties in the LCA.

Addressing these challenges involves using sophisticated modelling techniques, conducting experimental measurements, and employing advanced data analysis methods to reduce uncertainties.

The energy source used to produce hydrogen significantly influences its overall LCA. Using renewable energy sources (solar, wind, hydro) for electrolysis results in a significantly lower carbon footprint compared to using fossil fuels (natural gas) in SMR. In fact, green hydrogen (produced from renewable sources) has the potential to become a nearly carbon-neutral energy carrier.

For instance, producing hydrogen via electrolysis powered by a coal-fired power plant results in a high carbon footprint due to the emissions from coal combustion. On the other hand, utilizing solar or wind power for electrolysis drastically reduces the carbon footprint of the hydrogen production, making the process significantly more sustainable. This is a key driver for research and investment in renewable energy-based hydrogen production technologies.

Handling data uncertainty and variability in hydrogen LCA studies is crucial for ensuring the reliability and credibility of the results. This involves:

• Sensitivity analysis: Exploring the impact of variations in input data on the final LCA results. This helps to identify the most critical parameters and data gaps.
• Uncertainty propagation: Employing statistical methods to quantify and propagate the uncertainties in input data through the LCA model.
• Monte Carlo simulation: A probabilistic approach using random sampling to generate a range of possible outcomes, providing a more realistic representation of the uncertainty.
• Data quality assessment: Evaluating the reliability and consistency of the data used in the LCA. This includes checking data sources, methodologies, and potential biases.
• Scenario analysis: Exploring different scenarios, such as variations in energy prices, technological advancements, and policy changes, to assess their potential impact on the LCA results.

Transparency is key in reporting the uncertainties associated with the results, allowing for a more informed interpretation of the findings.

Current LCA methodologies for hydrogen systems face several limitations. One major challenge is the data scarcity for certain production pathways and system components. Accurate LCAs require comprehensive data on energy consumption, material usage, and emissions throughout the entire lifecycle, from resource extraction to end-of-life disposal. However, for emerging hydrogen technologies, this data is often incomplete or unreliable, leading to uncertainties in the results.

Another limitation is the difficulty in modeling complex interactions. Hydrogen systems involve numerous interconnected processes and energy carriers, making it challenging to capture all the relevant environmental impacts accurately. For example, the energy used to produce hydrogen might come from various sources with varying carbon intensities, adding complexity to the analysis.

Furthermore, the allocation of impacts in coupled systems (e.g., hydrogen production integrated with electricity generation) presents a significant challenge. Different allocation methods can lead to substantially different results, highlighting the need for careful consideration and justification of the chosen approach. Finally, current LCAs often focus on environmental aspects, neglecting crucial social and economic considerations that impact the overall sustainability of hydrogen systems.

Several software packages are used for hydrogen LCA studies, each with its strengths and weaknesses. SimaPro and GaBi are two widely used commercial software platforms offering a wide range of functionalities, including databases of environmental impact factors, and user-friendly interfaces. These are particularly suitable for comprehensive and complex LCAs, capable of handling multiple pathways and processes.

OpenLCA is a free and open-source alternative providing flexibility for customized analyses. Its openness allows users to adapt the software to specific needs and data sets, but requires more technical expertise compared to commercial platforms. The choice between these tools depends on factors like budget, available expertise, and the complexity of the study. For instance, a simpler study might benefit from the user-friendliness of SimaPro, while a researcher aiming for deep customization might opt for OpenLCA.

Ultimately, the best software is the one that best suits the specific requirements of the project, allowing for accurate data input and robust analysis, regardless of its open-source or commercial nature. It’s crucial to select a package with a well-documented impact assessment methodology and robust data validation procedures.

Incorporating social and economic factors into a hydrogen LCA necessitates an expanded approach beyond traditional environmental assessments, using methods like Social Life Cycle Assessment (SLCA) and Economic Input-Output Life Cycle Assessment (EIO-LCA).

SLCA focuses on the social aspects throughout the hydrogen system’s lifecycle. This may involve evaluating factors such as occupational health and safety risks during production, transportation, and storage; the impact on local communities; and the fairness of resource distribution. For example, an LCA might investigate the potential for job creation in a region due to hydrogen infrastructure development, contrasting it with potential job losses in other sectors.

EIO-LCA integrates economic data to assess the broader economic impacts. It helps to understand the economic dependencies associated with hydrogen production and usage, considering aspects like resource costs, employment levels, and economic benefits or losses across different sectors. A practical example is an analysis of the economic viability of a local hydrogen production facility, factoring in energy prices, material costs, and potential government subsidies. Combining these approaches provides a more holistic and comprehensive understanding of the sustainability of hydrogen systems.

The functional unit in a hydrogen LCA defines the standardized service provided by the hydrogen system being assessed. It’s essential for making meaningful comparisons between different hydrogen production or utilization technologies. The functional unit serves as the reference point against which environmental impacts are measured. For example, one functional unit might be the production of 1 kg of hydrogen with a certain purity, or the delivery of 1 kWh of energy from a hydrogen fuel cell.

Choosing the appropriate functional unit is crucial for ensuring the validity and comparability of LCA results. A poorly defined functional unit can lead to misinterpretations and inaccurate conclusions. For instance, comparing a large-scale hydrogen production plant with a small-scale electrolyzer without defining a consistent functional unit (e.g., hydrogen produced per year) would render the comparison meaningless.

The selection of the functional unit needs careful consideration and clear documentation to ensure that all life cycle stages are accurately reflected and that comparisons are truly meaningful and relevant to the intended application.

Material choices significantly influence the environmental performance of hydrogen components. For example, using lightweight materials like carbon fiber reinforced polymers (CFRP) in hydrogen storage tanks can reduce the weight and thus the energy needed for transportation, decreasing transportation-related emissions. However, the production of CFRP often involves high energy consumption and the release of greenhouse gases. The embodied carbon in these materials needs to be carefully assessed.

Similarly, selecting stainless steel for electrolyzer components offers good corrosion resistance, but its production is energy-intensive. Using alternative materials with lower embodied energy and reduced environmental impact, such as recycled metals, could improve the overall performance. In fuel cell components, the choice of catalysts (e.g., platinum) and membrane materials drastically impacts both performance and environmental footprint. Research into less expensive and more sustainable catalyst materials is vital to reduce the environmental cost of hydrogen fuel cells.

A comprehensive LCA should carefully evaluate the trade-offs between the performance benefits and the environmental impacts of different materials throughout the entire lifecycle of hydrogen components, considering material sourcing, manufacturing processes, and end-of-life management.

Data quality and transparency are paramount in hydrogen LCA studies. The reliability of LCA results hinges on the accuracy and completeness of the input data. Inaccurate or incomplete data will inevitably lead to misleading conclusions. For example, using outdated emission factors for electricity generation will result in an inaccurate assessment of the carbon footprint of hydrogen produced via electrolysis.

Transparency ensures the reproducibility and trustworthiness of the study. This involves clearly documenting all data sources, assumptions, and methodological choices. A transparent LCA allows others to scrutinize the analysis, identify potential biases, and replicate the study if needed. This is crucial for building confidence and acceptance in the results. A standardized data reporting framework and open access to datasets are crucial steps toward achieving transparency in hydrogen LCA studies.

Furthermore, using validated databases of environmental impact factors and consistently applying appropriate methodologies enhance data quality and improve the credibility of the LCA. Regular data updates and peer review processes can further enhance the reliability of hydrogen LCA studies.

Conducting a comparative LCA of different hydrogen systems requires careful consideration of several key aspects. First, it is crucial to define a consistent functional unit for all systems being compared. This ensures fair comparisons between systems with varying capacities or applications. Secondly, consistent system boundaries should be defined to ensure that all relevant stages of the lifecycle are included in the analysis for each technology. Ignoring certain processes or stages could lead to biased results.

Data quality and consistency are also vital. Ensuring that the input data for each system is accurate, reliable, and from comparable sources is crucial for valid comparisons. Finally, sensitivity analysis helps to determine the influence of uncertainties in input data on the overall LCA results, quantifying the robustness of conclusions. This is especially important when dealing with data scarcity for emerging technologies. A transparent reporting of these uncertainties is crucial to the reliability of the comparative study.

By paying meticulous attention to these factors, researchers can develop rigorous and meaningful comparative LCAs of different hydrogen systems to inform technology choices and policy decisions.

Well-to-wheel (WtW) and well-to-tank (WtT) efficiencies are crucial metrics in assessing the overall energy efficiency of hydrogen systems. They represent the percentage of energy initially present in the energy source (e.g., natural gas, electricity from renewable sources) that is finally available at the wheel (in a vehicle) or in the tank (stored hydrogen).

WtT efficiency focuses on the energy conversion processes up to hydrogen storage. This includes energy extraction from the primary source, conversion to hydrogen (e.g., steam methane reforming, electrolysis), purification, compression, and storage. Losses occur at each step due to inefficiencies inherent in the processes (heat losses, parasitic power consumption etc.). For example, if 100 units of energy are input into an electrolysis system, and only 70 units are stored as hydrogen, the WtT efficiency is 70%.

WtW efficiency extends the assessment to the entire process, encompassing the energy losses during hydrogen utilization in a fuel cell vehicle. It considers WtT efficiency plus the energy conversion from hydrogen to electricity in the fuel cell, the energy needed to power the vehicle’s ancillary systems (e.g., pumps, compressors), and mechanical transmission losses. So, if 70 units of hydrogen were stored (WtT efficiency of 70%), and only 50 units are ultimately used for vehicle propulsion, the WtW efficiency would be 50% (relative to the initial energy source).

Understanding these efficiencies is vital for comparing different hydrogen production pathways and evaluating the overall environmental impact of the system.

Evaluating the recyclability and end-of-life management of hydrogen system components is critical for minimizing environmental impacts and resource depletion. This requires a systematic approach, considering both material composition and potential recovery/recycling processes.

• Material Characterization: We first identify the materials used in each component – from fuel cells and electrolyzers to storage tanks and pipelines. This detailed material breakdown is crucial to assess the potential for recycling or reuse.
• Recycling Technologies: Then we evaluate the feasibility and efficiency of existing or emerging technologies for recycling or recovering valuable materials. This could involve processes such as material sorting, dismantling, metal extraction, or chemical recycling.
• End-of-Life Scenarios: Different scenarios need to be considered depending on the component’s lifespan and condition (e.g., component failure vs. planned obsolescence). Landfilling should be the last resort.
• Life Cycle Assessment (LCA): We integrate all these aspects into the LCA framework, quantifying the environmental burdens associated with different end-of-life management options. This includes energy use, emissions, and resource consumption.
• Economic Analysis: Finally, an economic analysis assesses the costs associated with recycling and disposal, helping to determine the most sustainable and economically viable approach.

For example, platinum group metals (PGMs) in fuel cells are valuable and should be prioritized for recycling. Similarly, certain plastics can be recycled, while others might require incineration with energy recovery.

Sensitivity analysis plays a vital role in hydrogen LCA studies by identifying the key parameters and uncertainties that most significantly influence the overall environmental impacts. It helps refine the model, prioritize data collection efforts, and improve the robustness of the results.

We typically employ techniques like:

• One-at-a-time (OAT) sensitivity analysis: This involves changing one parameter at a time and observing the impact on the overall results. It is simple to understand but may miss interactions between parameters.
• Global sensitivity analysis: This sophisticated method, such as variance-based methods (e.g., Sobol indices), quantifies the influence of each parameter and their interactions. This helps rank parameters in order of importance.

Consider the electrolysis of water: The electricity source’s carbon intensity significantly influences the overall carbon footprint. A sensitivity analysis reveals this influence and allows us to assess the uncertainty related to fluctuating renewable energy shares in the grid.

By identifying these sensitive parameters, we can focus research and development efforts on those areas where improvements can have the biggest impact on environmental performance.

Communicating LCA results effectively to different stakeholders – policymakers, investors, researchers, and the public – requires tailored approaches.

• Visualizations: Use clear and concise charts and graphs (bar charts for comparing different pathways, Sankey diagrams for visualizing energy flows) to illustrate key findings. Avoid overly technical jargon.
• Summaries: Provide executive summaries that highlight the main findings and implications for each stakeholder group. Tailor the language and level of detail to match the audience’s technical expertise.
• Interactive Tools: Consider web-based dashboards or interactive tools that allow stakeholders to explore the data and results in greater detail.
• Uncertainty Communication: Clearly communicate uncertainties associated with the LCA results. Use ranges or probability distributions to represent the variability of the findings.
• Scenario Analysis: Present results for different scenarios (e.g., variations in renewable electricity mix, hydrogen production technologies) to enhance understanding of the implications of various policy choices or technological developments.

For instance, for policymakers, focus on the overall environmental benefits and policy implications. For investors, emphasize the economic viability and risks. For the public, utilize plain language and relatable analogies.

Future advancements in hydrogen LCA methodologies will focus on several key areas:

• Data Availability and Quality: Improving data quality and expanding the availability of data on emerging technologies and materials will enhance the accuracy and reliability of LCAs.
• Integration of Social and Economic Aspects: Moving beyond purely environmental assessments to include social and economic factors (e.g., job creation, community impacts) to provide a more holistic picture.
• Dynamic LCAs: Developing dynamic LCA models that can account for changes in technology, policy, and market conditions over time. This allows for better long-term assessments.
• Artificial Intelligence (AI) and Machine Learning (ML): Using AI and ML to improve data analysis, uncertainty quantification, and the development of predictive models.
• Standardization of Methods and Databases: Greater standardization of LCA methodologies and the creation of comprehensive, publicly accessible databases will enhance comparability across studies.

These advancements will help us create more robust, comprehensive, and policy-relevant LCAs for hydrogen systems, driving innovation towards a sustainable hydrogen economy.

Hydrogen LCA plays a crucial role in informing policy decisions by providing objective, scientific evidence on the environmental impacts of different hydrogen pathways. This supports the development of policies that promote a sustainable and responsible hydrogen economy.

Specifically, LCA results can:

• Inform Policy Targets: Help set ambitious but realistic targets for greenhouse gas emissions reductions in the hydrogen sector.
• Guide Technology Selection: Identify and prioritize the most environmentally friendly hydrogen production and utilization technologies.
• Support Incentive Programs: Inform the design of effective incentive programs to promote sustainable hydrogen technologies.
• Assess Policy Impacts: Evaluate the environmental consequences of different policy options (e.g., carbon pricing, renewable energy mandates).
• Facilitate International Cooperation: Enable comparison and benchmarking of national hydrogen strategies.

For example, LCA studies can compare the environmental performance of green hydrogen (produced from renewable electricity) with blue hydrogen (produced from natural gas with carbon capture and storage). This comparison helps inform policy decisions about which pathway to prioritize, considering the trade-offs between cost and environmental impact.

Defining the system boundaries is crucial in hydrogen LCA studies as it determines which processes and impacts are included in the assessment. A poorly defined boundary can lead to misleading or incomplete results.

Key considerations when defining boundaries include:

• Upstream Processes: Should the LCA encompass the entire supply chain, from raw material extraction to hydrogen production? This ‘cradle-to-gate’ approach is common but can be resource-intensive.
• Downstream Applications: Should the LCA extend to the end use of hydrogen, for instance, including transportation, energy storage, or industrial applications? A ‘cradle-to-grave’ approach provides a more complete assessment.
• Geographic Scope: Should the assessment consider only local impacts, or also those from geographically distant processes such as raw material sourcing?
• Allocation: If a facility produces hydrogen and other products, how are the environmental impacts allocated between these products? Different allocation methods can yield different results.

The choice of system boundaries depends on the study’s objectives and the level of detail required. It’s important to explicitly state and justify the chosen boundaries in the LCA report, allowing for transparency and comparability with other studies.

For instance, comparing two hydrogen production methods, one might choose a ‘well-to-tank’ boundary if the focus is on the hydrogen production stage only. However, a ‘well-to-wheel’ boundary might be needed to compare the overall environmental performance in a transportation application.

My experience with LCA databases and datasets is extensive, encompassing both general-purpose databases and specialized datasets for hydrogen systems. I’ve worked extensively with ecoinvent, a widely used database containing life cycle inventories (LCIs) for numerous processes and materials, including those relevant to hydrogen production, transport, and storage. I’m also familiar with databases like ReCiPe and IMPACT World+, which offer different impact assessment methodologies that allow for comparative analysis of environmental impacts. For hydrogen-specific data, I have experience leveraging datasets from research projects and government initiatives, often supplementing publicly available data with information from peer-reviewed literature and industry reports to address data gaps.

For example, in a recent study assessing the lifecycle impacts of green hydrogen produced via electrolysis, I utilized ecoinvent for the LCI of the electrolysis process itself and complemented it with data from a research paper on the specific energy consumption of the electrolyzer technology under investigation. Another project required me to use a combination of ecoinvent and a dedicated dataset on the lifecycle emissions from renewable energy sources to ensure that the renewable electricity used for electrolysis was accurately represented. The selection of datasets is always based on relevance and data quality, ensuring the accuracy of the final LCA results.

Ensuring the credibility and reliability of LCA results is paramount. My approach involves a multi-faceted strategy. First, I meticulously select and critically evaluate the data used in the analysis, paying attention to the geographical context, temporal relevance, and data uncertainty. I always strive for transparency in data sourcing, documenting all data sources and their associated uncertainties. Secondly, I employ appropriate LCA methodologies, choosing the most suitable impact assessment methods based on the study’s objectives and scope. This includes careful consideration of allocation methods in the case of multi-product processes.

Thirdly, I perform sensitivity analysis to assess how variations in input data affect the overall results. This helps to identify key parameters and uncertainties that significantly influence the conclusions. Finally, I rigorously document the entire LCA process, including all assumptions, limitations, and methodological choices, providing complete transparency and enabling peer review and verification of the study. This detailed documentation ensures the reproducibility and robustness of the results, critical for building trust and credibility in the LCA findings. For instance, in an LCA on hydrogen storage, we ran sensitivity analyses to evaluate the influence of storage technology, leakage rates and transportation distances on overall impacts.

I have extensive experience with SimaPro, Gabi, and Brightway2, each offering unique strengths. SimaPro, with its user-friendly interface and extensive database connectivity, has been my primary tool for many projects due to its streamlined workflow and reporting capabilities. I’ve used it to model complex hydrogen systems, from production pathways to end-use applications. Gabi is another powerful tool, known for its comprehensive impact assessment methods and detailed database. I’ve utilized Gabi’s advanced features in projects requiring in-depth analysis of specific environmental impacts, such as ecotoxicity or resource depletion.

Brightway2, with its emphasis on open-source principles and flexibility, is particularly useful when working with custom datasets and developing new methods. I’ve integrated Brightway2 into projects where we needed to develop specific LCI datasets or tailor impact assessment methods to the specific characteristics of hydrogen production pathways. The choice of software always depends on the specifics of the project, the available data, and the desired level of analysis.

Attributional and consequential LCA are two fundamentally different approaches to life cycle assessment. Attributional LCA focuses on the environmental impacts associated with a specific product or process as it currently exists, typically using a snapshot of existing data. It answers the question: “What are the impacts of this product/process as it is produced today?” This approach is relatively simpler to conduct but may not capture the full picture of indirect or future impacts.

Consequential LCA, on the other hand, takes a more dynamic approach, assessing the impacts of a change in the system. It considers how introducing a new product or process might affect other related processes and overall market dynamics. This approach, more complex, explores the “what-if” scenarios, analyzing the potential consequences of changes. For example, an attributional LCA of a hydrogen fuel cell car might only consider the impacts of manufacturing the car and the hydrogen fuel. A consequential LCA would also account for the potential changes in electricity generation, increased hydrogen production and associated land use changes required to meet an increase in demand.

Incorporating land use change impacts in a hydrogen LCA is crucial, especially for renewable hydrogen production methods that require significant land areas, such as biomass gasification or solar and wind farms. The method involves quantifying the land area required for each stage of the hydrogen lifecycle, from resource extraction to energy production, transport and infrastructure. Then, the impact of converting that land from its previous use is assessed, considering factors such as carbon sequestration, biodiversity loss, and habitat change.

Several datasets and models can be used to quantify these impacts, including land use change factors derived from life cycle inventory databases or specialized land use change models. The impact assessment typically uses impact categories such as land occupation, biodiversity loss, or changes in ecosystem services. The choice of method depends on data availability and the specific environmental impacts of concern. For example, in an LCA of green hydrogen produced via electrolysis using wind power, the land use change associated with building the wind farm needs to be considered in addition to the impacts of material production and construction.

In a recent project analyzing the lifecycle impacts of blue hydrogen (produced from natural gas with carbon capture and storage), we encountered a significant challenge related to quantifying the carbon dioxide storage process. The uncertainty associated with long-term CO2 storage security was considerable, and existing datasets provided only limited information on potential leakage rates and long-term stability. We addressed this by consulting with experts in carbon capture and storage (CCS) technology, reviewing relevant scientific literature, and using a range of plausible leakage scenarios in our sensitivity analysis.

We employed a Monte Carlo simulation to incorporate uncertainty around leakage rates and CO2 permanence into our LCA. This allowed us to estimate a range of potential environmental impacts, reflecting the uncertainty associated with CCS technology. The results highlighted the importance of further research into CO2 storage security to accurately assess the environmental footprint of blue hydrogen. By adopting a transparent and methodical approach, we produced robust and reliable results, despite the challenging uncertainties associated with the CCS process.