Educational
How good for the climate is investing into low carbon energy, really? We’ve built a web app so you can explore it yourself
Oct 22, 2025 @ London by Krista Tukiainen
Green energy projects vary hugely in climate impact — we built a web app to show how much CO₂ each dollar of investment really avoids, and where it makes the biggest difference.
We know that green (low carbon and renewable) energy investments are a positive thing, but opaque and diverse carbon methodologies and complex datasets still don’t do a good job of explaining this well to either the expert sustainable analyst, let alone the layperson.
We hear about the growth of these investments, both locally to you and famous large-scale projects globally. Ethiopia’s new Nile Dam is all of the news, for example. As are the many nuclear projects taking off spurred by AI hyperscalers.
But how good are these for the environment really? And where in the world is the best place to invest in green energy to maximise climate impact?
Try it yourself! The web app is live and free to use. No login, no data collection, no waitlist.
If you're an energy or sustainability professional, you might even be evaluating, say, a specific $10M solar project in Indonesia. You need to know: How much energy will this generate? How many tonnes of CO2 emissions will it avoid? And, how does this compare to the same investment in Vietnam, for example?
The data exists. The formulas are straightforward. But pulling it together quickly, consistently, and in an accessible format? That's the problem we solved this week.
We've intentionally made this simple to use and transparent in methodology. If you find errors, questionable assumptions, or better data sources—let us know. We'll update it. And let us know which project types you would like to see next!
At ClimateAligned, we’ve built an impact methodology to compare green bonds’ actual carbon impact as the reported numbers often come with opaque or inconsistent methodologies.
For example, one Norwegian green bond issuer measures the carbon impact of a solar project using coal-fired power generation and the solar PV potential of Europe as the baselines, while another uses the almost zero-carbon Norwegian grid and the relatively low solar PV potential of Norway. Clearly, these methodological differences aren’t incorrect, but result in vastly different carbon impact in terms of the emissions avoided with the money spent from these bonds.
This methodology has been a game changer in the green bond space, and we realised that its usefulness can be even broader. In fact, many sustainability professionals still don’t have an easy way to compare and visualise these impact data on green energy projects.
We built a web app that takes three inputs (investment amount, project type, location) and returns immediate impact estimates. It's live, it's free, and the methodology is fully transparent.
This post walks through exactly how we built it, what data sources we used, and how the calculations work. If you want to replicate this, improve on it, or spot where we've made questionable assumptions - this is for you.
This tool is useful if you're:
It's not a replacement for detailed feasibility studies or bankable project assessments.
The fundamental question is: How much energy will this project generate annually?
The formula:
Annual Energy (MWh) = Installed Capacity (MW) × Capacity Factor × 8,760 hours
Simple enough. But each component requires careful consideration.
This is determined by dividing the investment amount by the capital cost per MW for each technology type.
Installed Capacity (MW) = Investment Amount / Capital Cost per MW
Data source: We use the dataset that underpins IRENA's Renewable Power Generation Costs reports, which provide global weighted-average capital costs for different renewable technologies. These are updated annually and represent the most comprehensive dataset available for project costs worldwide.
For example (2024 data):
Key assumption: We use global averages. In reality, capital costs vary significantly by region due to labor costs, supply chains, and local regulations. A solar project in Germany costs more per MW than one in India. We acknowledge this limitation - for a quick estimate tool, global averages provide a reasonable baseline.
The capacity factor is the ratio of actual energy output to theoretical maximum output if the plant ran at full capacity 24/7.
Geographic localisation: For solar PV, we add geographic nuance by adjusting capacity factors using country-specific solar irradiation data from the World Bank's Global Solar Atlas. A solar project in Spain will have a higher capacity factor than one in Norway, even with identical technology. For all other renewable technologies (wind, hydro, bioenergy, geothermal), we use global average capacity factors from IRENA or IEA, acknowledging that actual performance varies by location but providing reasonable baseline estimates for our web app calculations.
Hydrogen is an energy carrier produced via electrolysis. Here we use data from the U.S. DOE, which estimates that PEM electrolyser systems cost approximately $2,000/kW. A $1M investment funds a 500 kW electrolyser.
Electrolysers require ~50 kWh of electricity per kg H₂. At 60% utilisation (5,300 hours/year), a 500 kW system produces ~52,600 kg H₂ annually. This represents ~1,740 MWh of chemical energy (33 kWh per kg H₂).
The climate benefit comes from displacing grey hydrogen (from natural gas), which emits ~9 kg CO₂ per kg H₂. Green hydrogen therefore avoids ~473 tonnes CO₂ per $1M invested annually. We convert hydrogen output to MWh for consistent "homes powered" calculations across all technologies.
This is constant: 8,760 hours per year (365 days × 24 hours).
Multiply installed capacity by capacity factor by annual hours, and you have annual energy production in MWh.
Energy production is meaningless for climate impact without context. The critical question: What emissions are being avoided?
The formula:
Annual CO2 Avoided (tCO2e) = Annual Energy (MWh) × Grid Carbon Intensity (tCO2e/MWh)
This is the emissions per unit of electricity from the local grid. When renewable energy displaces grid electricity, it avoids these emissions.
Data sources:
Grid carbon intensity varies dramatically:
Important caveat: Grid carbon intensity changes over time as grids decarbonise. We use 2022 data as that is the most recent year with complete data coverage on grid carbon intensity for nearly all countries globally. Importantly, this means our "avoided emissions" calculation represents current displacement, not lifetime project impact. A more sophisticated model would account for grid decarbonisation trajectories over a project's 20-25 year lifetime.
Raw MWh and tCO2e numbers don't resonate with most people. We include a "homes powered" equivalent to make the impact more concrete.
The formula:
Homes Powered = Annual Energy (MWh) / Average Home Consumption (MWh/year)
Data source: We use Ofgem's global average estimate of residential electricity consumption as a standardised baseline for our web app. This provides consistency across all calculations. In this case, we use the UK’s electricity consumption for residential homes at 2.7 MWh/year to allow for easy comparison against projects.
We made trade-offs to keep this tool simple and fast:
No additional lifetime calculations: We show annual impact, not 25-year project lifetime totals. This avoids assumptions about degradation rates, maintenance, and future grid conditions. (NB: Much of this is already baked into IRENA’s capacity factors.)
No financial modelling: We don't calculate IRR, LCOE, or payback periods. This is purely a climate impact calculation.
No site-specific optimisation: We don't account for specific wind speeds, panel tilt angles, or local shading. These matter enormously for real projects but require site-specific assessments. (NB: IRENA’s base data takes this into account to some extent.)
No cost differentiation between geographies: We use the most reputable global averages for installation costs, when in reality project costs can vary widely depending on local labour costs, costs of land, global supply chain access, local tax incentives, etc.
Simplified technology categories: We treat "solar PV" as one category, when in reality there are significant differences between monocrystalline, polycrystalline, and thin-film technologies, as well as concentrated solar power and more.
Let's be honest about the limitations:
We've chosen to use the best available global datasets rather than perfect local data, which doesn't exist for most regions. For due diligence on a specific project, you'd want site assessments and local studies. For quick scenario comparisons, this is good enough.
Climate impact analysis shouldn't be locked behind proprietary data sets and expensive consultants. The data is largely public. The calculations are straightforward. The barrier is putting it together in a usable format.
We built this for ourselves and figured others might find it useful. If you want to build your own version, improve on our methodology, or adapt it for your specific needs, please do.
The goal is better decisions, made faster, with more transparent assumptions. If this helps with that, we've succeeded.
Questions, feedback, or spotted an error in our methodology? Email us at info@climatealigned.co or reply on LinkedIn. We'll be updating this as we refine the tool.