Download Category: Economic Model

What are the costs of different technologies in the energy transition? We construct economic models in order to capture costs, compare costs, and stress-test sensitivities.

Our modelling framework always focuses on marginal cost. This is the price that would need to be charged (in $/ton, c/kWh, etc), for a technology to earn a 10% full-cycle return, on its up-front capex costs (in $/Tpa, $/kW, etc), net of operating expenses, maintenance and taxes.

In each of our models, we have aimed to aggregate capex costs and opex costs, based on company data and technical papers. This is just useful data to tabulate, in order to appraise what constitutes a “good” or a “challenging” project within individual industries.

Energy costs are also tabulated in most models, across electricity, heat and materials. This allows us to calculate the CO2 intensity of projects.

A general observation in our economic models is that they can be useful building blocks. For example, if you want to model the costs of making wind turbine blades, you need to know the cost of carbon fiber, which in turn hinges on the costs of polyacrylonitrile, which in turn depends on the price of ammonia, which in turn depends on the price of natural gas. These inter-connections are fascinating and suggest where there may be future bottlenecks and cost re-inflation in the energy transition.

Energy economics: an overview?

This data-file provides an overview of energy economics, across 175 different economic models constructed by Thunder Said Energy, in order to put numbers in context. This helps to compare marginal costs, capex costs, energy intensity, interest rate sensitivity, and other key parameters that matter in the energy transition. Our top five facts follow below.

This data-file model provides summary economic ratios from our different economic models across conventional fuels, conventional power, renewables, lower-carbon fuels, manufacturing processes, infrastructure, transportation and nature-based solutions.

For example, EBIT margins range from 3-70%, cash margins range from 4-80% and net margins range from 2-50%, hence you can use the data-file to ballpark what constitutes a “good” margin, sub-sector by sub-sector; and to screen different industries, according to the capital intensity, opex costs and resultant profitability (chart below).

Capital intensity ranges from $300-9,000kWe, $5-7,500/Tpa and $4-125M/kboed. So if you are trying to ballpark a cost estimate you can compare it with the estimated costs of other processes. The median average industry has a capex cost of $750/Tpa (chart below).

Capital intensity of different energy sources also varies by an order of magnitude (chart below). Each $1 dollar that is disinvested from new hydrocarbon capex ideally needs to be replaced by $25 invested in wind and solar, in order to add the same amount of primary energy to the global energy system (chart below, note here).

Economies of scale are visible in the data-file, across our models of Air Separation, Cables, Comminution, Compressors, Electric Motors, Electrowinning, Fans, Flotation, Gas Dehydration, Harmonic Filters, Heat Exchangers, Inverters, Motor Drivers, Pumps, Rankine Engines, Tanks and Turbines. Generally, making these units 10x larger reduces their unit costs by around 45%.

Cost reduction from scale for different energy technologies.

Interest rate sensitivity is visible in our overview of energy economics. Each 1% increase in capital costs re-inflates new energies 10-20%, infrastructure 2-20%, materials 2-6%, and conventional energy 2-5% (chart below, note here).

Marginal cost inflation per 1% WACC increase for different energy technologies, materials, and infrastructure projects.

The energy intensity of materials is visible across our models of Acetylene, Aluminium, Ammonia, Carbon Fiber, Cement, Copper, Cyanides, Desalination, Glass, H2O2, Hydrogen, Industrial Gases, Lithium Batteries, Methanol, NaOH/Cl2, Nitric Acid, Paper, Plastics, Silicon, Silver, Steel, Wood Products. As a rule of thumb, energy is 50% of the cash cost of typical materials.

Renewables stand out. Despite high capital intensity (35% of revenues, 2x the average), once constructed, they also have the highest cash margins (75%, also 2x the average). The rise of wind, solar and electrification make capex costs and capital costs increasingly important.

The full data are available in the data-file below. However, please be aware that this is simply a compilation of headline figures across our library of 175 economic models. Access to all of the underlying models is covered by a Thunder Said Energy subscription.

Levelized cost of electricity: stress-testing LCOE?

Levelized cost of electricity of different electricity sources, in cents per kWh and their true CO2 intensity, in kg per kWh.

This data-file summarizes the levelized cost of electricity (LCOE), across 35 different generation sources, covering 20 different data-fields for each source. Costs of generating electricity can vary from 2-200 c/kWh. There is more variability within categories than between them. All the numbers can readily be stress-tested in the data-file.

Levelized cost of electricity (LCOE) breaks down the costs of adding new electricity generation, across capex, capital, tax, fuel, O&M, CO2 and T&D, distilled down in c/kWh terms, or $/MWH terms.

We have constructed over 200 economic models calculating the specific levelized costs of onshore wind, offshore wind, solar, hydro, nuclear, gas power, coal power, biomass, RNG, diesel gensets, geothermal, hydrogen, fuel cells, power transmission, batteries, thermal storage, redox flow, pumped hydro, compressed air, flywheels, CCS and nature-based CO2 removals.

The goal in this data-file is to allow for easy comparisons between different power generation options, across 20 different dimensions. We have written that we hate levelized cost, because it is often portrayed as though one energy source will emerge “to rule them all”, whereas there is more variability within each category than between them (see below).

Levelized cost: ten things I hate about you?

The simple chart below shows how our levelized cost estimates change if we make simple changes in this comparison file: flexing risk-free rates between 1-5%, flexing fuel costs +/- 50%, flexing capex costs by +/- 50%, or changing the distances needed for AC power transmission, CCS pipelines, or other variables.

Levelized cost of electricity of different electricity sources, in cents per kWh-e. Coal, gas, and solar are some of the cheapest but there is a lot of variability within each category. Note that this is on a partial electricity basis, not total.

This kind of stress-testing is really the main point of the data-file, asking questions like: how do levelized costs change with WACCs? Or how do levelized costs change with higher gas prices? How do levelized costs change with capex deflation? What are the best options for lowering CO2 intensities of grids (chart below) without inflating total costs? The data-file answers these questions across several dimensions…

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their true net CO2 intensity, in kg per kwh-e. Coal is cheap yet polluting while green hydrogen is the opposite. Different gas options tend to be the best.

Capex costs are broken down for each category and are defined as the total installed capex, in $/kWe, which can then be divided by the total number of lifetime operating hours, yielding a number in c/kWh. Usually, the capex estimates in our underlying models draw from both top-down surveys of past projects and bottom-up build-ups.

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their capex cost, in $ per kWe. The best are coal and gas sources.

Capital costs can be described as the after-tax income that needs to be earned on top of recovering the capex to derive a passable IRR (usually 7-12%), after whatever build-time is incurred prior to start-up (usually 2-6 years). We have taken this requisite after-tax income level from our individual underlying models, where it captures nuances such as time value of money, decline curves, and volatility.

Tax costs come on top of after-tax income. For simplicity, our models assume a 25% corporate tax rate across the board, but not tax breaks or changeable policies. Thus, we can think about the numbers in our data-file as being true economic costs.  

Fuel costs cover the costs of buying gas for a gas plant, coal for a coal plant, hydrogen for a hydrogen plant, etc. By contrast, fuel costs are often zero for renewables. Again, these can readily be flexed in the model, which is especially important for gas value chains, amidst high dispersion in global gas prices.

O&M costs cover operations and maintenance; and are generally going to be lowest for large and simple systems.

T&D costs cover transmission and distribution, to move power to the load center. An advantage for on-site generation is that power can be used directly, whereas the average offshore wind farm in the North Sea needs to be transmitted 20km back to shore, then onwards. AC transmission costs 1.5c/kWh/100km at large scale. For CCS value chains, we also include $3/ton/100km for CO2 transport in the T&D line.

CO2 costs cover the cost for offsetting or disposing of gross CO2 emissions: either via nature-based CO2 removals, using high-quality reforestation at $50/ton; or for CO2 geological disposal in subsurface reservoirs with a base case cost of $15/ton. Otherwise, for CCS value chains, additional costs are reflected in higher up-front capex, higher fueling costs due to energy penalties, and higher maintenance costs.

Other dimensions are also compared for all of the generation sources in our database: TRLs, logistical risks, development times, efficiency factors, CO2 intensity (kg/kWh), typical load factors (%) and land intensity (acres per MW) (see below).

Levelized cost of total electricity of different electricity sources, in cents per kWh, versus their direct land intensity, in acres per MWe. This method only counts land used by the power plants themselves.

Fiber optic data transmission costs?

The costs of fiber optic data transmission run at $0.25/TB per 1,000km in order to earn a 10% IRR on constructing a link with $120 per meter capex costs. Capex is 85% of the total cost. This data fiber breaks down the costs of data transmission from first principles, across capex, utilization, electricity and maintenance.

This model captures the costs of transmitting data across fiber optic cables, with a base case of $0.25/TB to earn a 10% IRR per 1,000km of data transmission. For an overview, please see our data-file into the energy use of fiber optic cables.

Energy intensity of fiber optic cables?

The economics of fiber optic cables are sensitive to cable length, utilization, and especially capex costs, which comprise 85% of the total costs, and are estimated in the data-file by tabulating details on 25 past fiber optics projects and their fiber counts (chart below).

The fiber running to an individual household might have 1-2 fiber optic strands, while a data-center interconnection (DCI) between two hyperscale facilities can have several thousand fibers.

Generally, each 10x increase in the number of fiber optic strands per cable only doubles the cost. A key reason is that 60-80% of total costs are construction, which do not change materially for higher-capacity cables.

Other factors that impact the capex costs of fiber optic cables: underground cables cost around 2-3x aerial cables. Undergrounding costs are also higher in rockier soil types and in urban environments. There are some nice cost breakdowns in the Capex_costs tab of the model.

Capex costs per Tbps of bandwidth can also be estimated, with a formula linking capex costs ($/m) to the bit rate (Trillion bits of data transfer capacity per second, Tbps). Some intercontinental fiber links quote a specific bandwidth in Tbps, helped by amazing multiplexing. For others, we can estimate the bandwidth from the fiber count.

Utilization rate is another variable that impacts the costs of fiber data transmission, which can be stress-tested in the model. Hurdle rate also matters. The energy consumption of fiber optic cables matters less, but is also included in the model.

Power distribution: the economics?

Cash flow for our financial model of a power distribution project over 30 years.

Power distribution costs to residential, commercial and industrial consumers are estimated at 3.5 c/kWh in this model, to generate a 10% levered return, in a 5km x 10MW distribution line, at 17kV, rated up to 400A, with a $150/kW-km capex cost, a 5% line loss and 40% annualized utilization. All of these inputs can be stress-tested in the data-file.

Power grids move electricity from generation sources, through the high-voltage transmission network (120-500kV), stepping down via transformers to the medium voltage grid (35-120kV) and then finally through smaller distribution lines (4-35kV), before ultimately reaching residential, commercial and industrial customers.

This data-file aims to model the costs of power distribution, across projects that average 4-35kV voltages, 10MW (strictly MVA) of average capacities, and distances from 1-30km. Our base case estimate is for 3.5 c/kWh for distributing electricity to consumers.

Power distribution costs are highly sensitive to capex costs and utilization rates, as shown in the chart below. 40% annualized utilization is a good rule-of-thumb for a distribution line that is at full capacity at the 1-2% peak load hours throughout the year. But capex is more complex.

Cost of power distribution as a function of average utilization for different capex levels. Higher utilization guarantees lower overall distribution cost.

One challenge is that no two projects are identical, which is borne out by reviewing different power cable configurations plus hundreds of planned projects in the capital improvement programmes of regional system operators. For example, MISO makes details available for all MISO transmission projects.

What is challenging is finding meaningful data-points, which represent the cost of adding new MVA-km to the distribution network. Most planning regions do not report the smaller expenditures separately (e.g., those costing <$1M, or <250k). Many projects also replace old equipment or improve reliability (e.g., transformers, circuit breakers, switchgear) but do not add any length to the network.

Nevertheless we have aimed to gather useful data-points in the projects tab (chart below). The range is very broad, from $10 to $5,000 /kW-km. As good average rules of thumb, large-scale overhead transmission lines cost $1.5/kW-km, rising to $30/kW-km for rural overhead distribution lines, $100/kW-km for urban distribution lines and $200/kW-km for underground distribution lines.

Capex cost of grid connections versus line capacity for projects in our database.

These costs matter for connecting new loads to the grid, such as electric vehicle charging points or other electricity-consuming facilities captured in our broader economic models. Please download the data-file to stress test the costs of electricity distribution.

Power transmission: the economics?

This data-file captures the costs of AC power transmission, requiring a 1.5c/kWh spread to earn a 10% levered IRR on a new 100km and 1,000MW transmission line, with capex costs of $1.5/kW-km. These numbers are supported by backup tabs, tabulating the costs of recent projects and a granular breakdown for the capex costs across 15 lines.

The capex costs of AC power transmission lines depend on both the capacity and length of the line, hence neither metric alone is particularly stable, when we tabulate the cost of past projects (chart below). Lengths range from 50-1,000km and power ratings range from 300-3,000MW. A better metric is the cost per kW of capacity and per km of distance, which we abbreviate as $/kW-km, averaging $1.5/kW-km.

The capex costs of AC power transmission lines can also be built up from first principles, as a breakdown of this $1.5/kW-km cost, across fifteen separate categories. The largest cost lines are installation (c25%), the metal structures (c20%), their foundations (c10%), the conductors (c10%), land preparation (c10%), substations (7%) and smaller contributors. Our source for these estimates are excellent granular disclosures from PJM.

As general rules of thumb, a higher voltage line requires larger and more expensive towers (first chart below), while a higher current line requires larger and more expensive conductors (second chart below). Nevertheless, all else equal, higher voltage and higher current lines will increase the power rating of a cable, and lower its total costs.

However our base case is relatively generous and can easily come in at $2-3/kW-km. Please download the data-file for sensitivies around land acquisition, permitting, site preparation, line length, line power, circuits per line, et al. While trenched lines are more resilient, they can also be 5-20x more expensive, according to some studies.

The key challenge, however, is not cost, but timing, as the average project in our screen takes 8-years to plan/permit, then 3-years to construct. It may take a long time to resolve power grid bottlenecks.

Power grids: the biggest bottleneck in the world?

Other cost lines are taken from the disclosures of regulated utilities, conductor costs, and other data-files we have constructed into high-voltage transmission lines. We have also separately modeled the costs of HVDCs, for longer-distance transmission. For more details, please see our overview of power transmission.

Power transmission: raising electrical potential?

Road costs: economic model?

A typical road costs $3M/lane-km to construct, with an effective cost of $0.25 per vehicle-km subsequently travelled. The range varies with utilization and road complexity. Around 10% of the costs are materials, mainly aggregates, while the remainder of the capex is spent on construction and engineering.

Road costs matter as an input to other economic models, as building any new piece of infrastructure requires access to that infrastructure, whether it is a wind farm, solar project, power transmission line or any other industrial facility.

Our economic model captures a road as though it were an economic entity, charging $0.25 per vehicle-km, to generate a 6.5% IRR on $3M/lane-km of capex. Of course, built by governments, there is no tax charge. Eliminating the tax charge boosts the IRR to 7.5% at the same road cost. Or alternatively, it reduces the ‘road cost’ by c10% for the same IRR.

In other words, the average American derives $4,000 of ‘road value’ as they drive 10,000 miles per year. To put this number in perspective, most of the US’s inter-state highway system was built during Eisenhower’s $620bn highway-building initiative from 1956-1990, possibly one of the best infrastructure-investments ever made. These roads are largely depreciated today. Although the US continues to spend $250bn pa, or $750 per American, maintaining and enhancing the road network.

The capex costs of a road vary with complexity. The cheapest roads cost $1M/lane-km. Roads with more junctions, bridges, overpasses or tunnels might be in the range of $5-10M/km. Land costs matter too, as per other infrastructure.

Around 10% of road costs are materials, especially aggregates and/or sand (15,000 tons per lane-km), followed by steel (35 tons per lane-km), bitumen (85 tons/lane-km) and cement (60 tons/lane-km). And materially more for large highways. The material must also be transported to site which maybe accounts for $20/ton of material in a large hauler.

The cost buildup of roads in terms and the breakdown of materials costs. The largest components are construction works and engineering. A majority of material costs are from aggregate materials.

Leading companies producing aggregate materials for road-building include Vulcan Materials, Martin Marietta and leading-cement producers, such as CRH, Heidbelerg and LafargeHolcim.

Capex costs also vary with the size of the road. For example, the material intensities can vary by a factor of 2x between smaller and larger roads, categorized by road layer thicknesses.

The costs per vehicle also depend on road utilization. The US sees 3.3 trn vehicle miles per year, across 4.2M miles of roads, which means each road is driven once every 40-seconds. We are effectively using this same cadence in our models. But doubling/halving the amount of vehicle traffic, all else equal, halves/doubles the effective road cost per vehicle.

Industrial cooling: chillers and evaporators?

This data-file captures the costs of industrial cooling, especially liquid cooling using commercial HVAC equipment, across heat-exchangers, cooling tower evaporators and chillers. Our base case is that removing 100MW-th of heat has capex costs of $1,000/ton, equivalent to c$300/kW-th, expending 0.12 kWh-e of electricity per kWh-th, with a total cost of 7 c/ton-hour.

Across the US cooling market, the most common metric for measuring cooling capacity is in ‘tons’. This is shorthand for the coolness provided by 1 US ton of ice melting over the course of a day, equating to 3.52kW-th of heat removal. Providing 3.52 kW-th of cooling for one hour can thus also be called 1 ton-hour.

Cooling can be delivered via three mechanisms: simple heat exchange with ambient air or water (depends on ambient temperatures), evaporating some of the water in an evaporating tower (depends on water availability) and chilling a working fluid using a refrigeration cycle. In practice, all three may be used in combination (as exemplified in the chart below).

Electricity use of a cooling system. About 50% is from pumping, 20% from the fans in cooling towers, and 30% from the chiller system.

Capex costs of industrial cooling depend on the precise combination of equipment that is used, but a good ballpark is $1,000/ton, equivalent to $300/kW-th, based on our models of compressors, heat-exchangers, pumps, fans and blowers, storage tanks, piping, VFDs, switchgear, grid connections, engineering and construction.

Installed cooling cost for data-centers. Installation and EPC make up ~60% of costs and the rest is equipment: pumps, cooling towers, chiller, piping, VFDs, etc.

Removing each kWh-th of heat requires 0.12 kWh-e of electricity, in our base case, but the numbers vary as a function of water evaporation rates and ambient temperatures, running anywhere from 0.03 to 0.5 kWh-e per kWh-th. Cooling in water-scarce and hot climates is c60% more costly than in water-abundant and cool climates.

Base case numbers in our commercial cooling model are primarily geared to data-centers, where 10-20% of total installed costs will be on cooling, in order to keep chips below a thermal limit of 27ºC or cooler. Water intensity of AI computing can thus be estimated in the range of 1,000-3,000 liters per MWH, meaning that each ChatGPT query consumes as much as 10-30ml of water. Or alternatively, PUEs can be increased by c5-10% to avoid any water use in evaporators. Hence the data-file also screens 20 companies, with 65% of the market in data-center cooling.

Market shares of companies providing equipment for data-center cooling versus the percentage of their business dedicated to it.

All of our numbers into the costs of industrial cooling can be stress-tested in the data-file. Backup tabs of the model contain details of companies and our notes from technical papers.

Data-centers: the economics?

Financial model over 25 years for a model data-center.

The capex costs of data-centers are typically $10M/MW, with opex costs dominated by maintenance (c40%), electricity (c15-25%), labor, water, G&A and other. A 30MW data-center must generate $100M of revenues for a 10% IRR, while an AI data-center in 2024 may need to charge $3M/EFLOP of compute.

Data-centers underpin the rise of the internet and the rise of AI, hence this model captures the costs of data-centers, from first principles, across capex, opex, land use and other input variables (see below).

In 2023, the global data-center industry is $250bn, across 500 large facilities, 20,000 total facilities, and around 40 GW of capacity, which likely rises by 2-5x by 2030.

A 30MW mid-scale data-center, costing $10M/MW of capex, must generate $100M pa of revenues, in order to earn a 10% IRR, after deducting electricity costs and maintenance.

The capex breakdown for a typical non-AI data center is built up in the data-file, drawing on cost data for various IT components, cooling, chillers, transformers, switchgear, battery UPS, backup generators, plus broader infrastructure such as generation, transmission and fiber links.

Capex buildup for a data-center. The cost is $10,000/kW, with the largest parts being the servers, cabling and installation. Another roughly $2,000/kW comes from associated infrastructure.

If the data-center is computation heavy, e.g., for AI applications, this might equate to a cost of around $3/EFLOP of compute in 2023. This fits with disclosures from OpenAI, stating that training GPT 4 had a total compute of 60M EFLOPs and a training cost of around $160M.

However, new generations of chips from NVIDIA will increase the proportionate hardware costs and may lower the proportionate energy costs (see ComputePerformance tab). An AI-enabled data-center can easily cost 2x more than a conventional one, surpassing $20M/MW.

Reliability is also crucial to the economics of data-centers: uptime and utilization have a 5x higher impact on overall economics than electricity prices. This makes it less likely that AI data-centers will be demand-flexed to power them using the raw output from renewable electricity sources, such as wind and solar?

The cost of training AI depending on the price of electricity and data-center utilization. The price is more sensitive to utilization than to power prices.

Economic considerations may tip the market towards sourcing the most reliable power possible, especially amidst grid bottlenecks, and it also explains the routine use of backup power generation.

Another major theme is the growing power density per rack, rising from 4-10kW to >100kW, and requiring closed-loop liquid cooling.

Please download the data-file to stress-test the costs of a data-center, performance of an AI data-center, and we will also continue adding to this model over time. Notes from recent technical papers are in the final tab.

Sugar to ethanol: the economics?

This data-file captures the production cost of ethanol from sugar, as a biofuel. A 10% IRR requires $1-4/gallon ethanol, equivalent to $0.25-1/liter, or $60-250/boe, depending on input sugar prices. Net CO2 intensity is at least 50% lower than hydrocarbons.

Global ethanol production runs to 2Mbpd of liquids, or around 28bn gallons per annum. Around two-thirds is ethanol form corn, especially at US ethanol plants; while around one-third is ethanol from sugar, especially in Brazil and elsewhere in the emerging world.

Our base case scenario assumes the key input for ethanol production will be non-edible molasses, priced at $100/ton, generated as a non-crystallizing byproduct from sugar refining. Molasses might comprise c55% sugar by mass.

Molasses can be directly fermented into an alcoholic solution, then distilled to produce ethanol. Modern distilleries use the Melle-Boinot fermentation process, centrifuging and recycling yeasts. Distillation occurs in two stages, first recovering 94% ethanol from the mash, then 99.6% anhydroous ethanol, which can be blended as a fuel.

Feedstock comprises 60-70% of the cost of ethanol production in our base case, hence we have constructed an entire separate model to capture the costs of sugar production. A sensitivity of ethanol prices to input sugar prices is charted below.

Capex costs of ethanol production are estimated from past projects, specifically looking for examples that add a bioethanol unit adjacent to a pre-existing sugar refinery; while opex costs are based on disclosures in technical papers, also noted in the data-file.

Our build-up also captures the CO2 emissions of sugar->ethanol production. The carbon accounting is debatable, but generally shows sugar-based ethanol to be at least 50% lower-carbon than hydrocarbon fuels. Please download the data-file to stress test the cost of ethanol from sugar, or to compare with the cost of ethanol from corn.

Sugar production: the economics?

The costs of sugar production are estimated at $260/ton for a 10% IRR at a world-scale sugar refinery, in a major sugar-producing region. Higher returns are achievable at recent world sugar prices, and by valorizing waste streams such as molasses for ethanol and bagasse for cogenerated electricity.

Sugar is the crucial feedstock for one-third of the world’s 28bn gallons pa of bioethanol, or around 0.6Mbpd of biofuels; and as a sweetener across the world’s food system, with Western adults typically consuming 60-80 grams of added sugar per day. This data-file captures the costs of sugar production.

A sugar price of $260/ton is needed for a 10% IRR, on a MTpa-scale sugar refinery, while global sugar prices recently ranged from $400-750/ton, enough to unlock 30-60% IRRs at these facilities.

50% of the cost of sugar comes from sugarcane, as a feedstock, which is also built up from first principles in this data-file, averaging $25/ton in our base case. Prior to harvest, sugarcane typically comprises c55% moisture, 12% sugar, 20% fiber and 12% trash (which may not burned off or cut off and left in the field).

Costs of sugarcane are also sensitive to yields, which is a key reason that Brazil leads the world in biofuels production. Yields can average 110 wet tons per hectare, although also tend to vary year-by-year.

Sugar itself only comprises c60% of the revenues of a typical sugar refinery, with the remainder coming from non-edible molasses (useful as an input to ethanol production), bagasse (as a fuel) and cogenerated electricity. The prices of these components can also sway the economics of sugar refining.

Capex costs and opex costs are also built up in the data-file, using data from past projects and technical papers. Capex cost of sugar production plants can vary widely, depending on the country, and the specific details of what is actually built.

This data-file captures the costs of sugar production, energy use, CO2 intensity of sugar, plus a breakdown of capex and opex. It is an important input for stress-testing the costs of ethanol from sugar.

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