To assess the economic viability of sugarcane bagasse-based biochar as a catalyst for agricultural and environmental advancement in Brazil, various economic models were implemented. These include a life cycle cost analysis, cost-revenue-based break even analysis, return on investment, cost-benefit analysis, net present value and internal rate of return, and sensitivity analyses, all forming a comprehensive Biochar Economic Viability Model.

In order to evaluate sugarcane bagasse-based biochar as an economically viable catalyst for agricultural and environmental advancement in Brazil, and to analyze biochar economically within an area where its aforementioned significant potential exists, scenarios within the study area of Brazil were considered. Scenarios A—the direct sale of biochar (carbon credits) following production, and B—the land application of produced biochar followed by the sale of surplus biochar (carbon credits)—were chosen to reflect possible business structures adopted by a farm producing biochar. Each scenario (A, B) was considered for farms of 10,000 ha, 20,000 ha, and 50,000 ha in size, for a total of 6 distinct scenarios.

Table 1 and Table 2 detail relevant values determined through a review of existing biochar-related literature and relevant values determined for this study, respectively. All chosen values or assumptions for this study were developed with the cooperation and insights from PSR Energy Consulting and Analytics in Rio de Janeiro, Brazil.

The To calculate the life cycle costs for each studied scenario, the following model was used (Equations (1)-(3))

$C_{total}=C_i+{\displaystyle {\sum }_{y=0}^n{C}_y}$ (1)

$C_i=C_{pd}+C_{iw}+C_{es}+C_{pv}$ (2)

$C_y=C_m+C_o+C_l+C_{cc}+C_{la}$ (3)

where $C_{total}$ is total life cycle costs, $C_i$ is initial investment costs, $C_y$ is yearly costs, $n$ is the total number of years $C_{pd}$ is planning and design costs, $C_{iw}$ is indirect and working capital costs, $C_{es}$ is equipment and setup costs, $C_{pv}$ is permit and carbon credit project validation costs, $C_m$ is maintenance costs, $C_o$ is operation costs, $C_l$ is labor costs, $C_{cc}$ is carbon credit certification costs, and $C_{la}$ is land application costs, for which Scenario A has none.

[a] Bagasse needs to be dried from 50% to 10%wt moisture for pyrolysis [b] Carbon Credit [c] Crop management cost, including tillage, irrigation, and other soil preparation.

[a] Based on Ramirez et al. [3] study.

To calculate the value of the variables, a reference study of three biofuel plants simulating an 84,000 t/y use of sugarcane bagasse in Queensland, Australia was used [3] . Values were then location-adjusted to be relevant for the study area of Brazil, using a ratio of the Price Level Index (PLI) of both locations for the equipment, setup, and operation costs [53] , and a ratio of the minimum wage (2024) of both locations for the labor costs [54] [55] . Reference values from the Ramirez et al., 2019 study are outlined in Table 3 .

To conduct a cost-revenue-based break even analysis for each studied scenario, cumulative life cycle costs from the life cycle cost analysis were used, as well as life cycle revenues calculated using the following models (Equation (4)) and (Equation (5)) for Scenario A and Scenario B, respectively

$R_{total}=R_b+R_{cc}$ (4)

$R_{total}=R_b+R_{cc}+R_s$ (5)

where $R_{total}$ is total life cycle revenues, $R_b$ is revenues from biochar sales, $R_{cc}$ is revenues from carbon credit sales, and $R_s$ is revenues from increased sugarcane sales, for which Scenario A has none.

To calculate the return on investment percentage per year for each studied scenario, the following model was used ( Equation (6 ))

$ROI=\frac{R_t-C_t}{C_i}\times 100$ (6)

where $R_t$ is total revenues for year t, $C_t$ is the total costs for year t (where $C_t$ for the first year does not include the initial investment cost), and $C_i$ is the cost of the initial investment.

To compare the costs and benefits for each studied scenario, total life cycle costs from the life cycle cost analysis were used. In addition, to calculate the benefits, the following model was used (Equations (7)-(8))

$B_{total}=R_{total}+S_{total}$ (7)

$S_{total}=S_f+S_o$ (8)

where $B_{total}$ is total benefits, $R_{total}$ is total life cycle revenues from the cost-revenue-based break even analysis, $S_{total}$ is total savings, applicable to Scenario B only, as these savings are results of biochar land application, $S_f$ is fertilizer savings, and $S_o$ is operational savings.

To then conduct the cost-benefit analysis, the following model was used (Equation (9))

$BCD=B_{total}-C_{total}$ (9)

where $BCD$ is benefit-cost difference, $B_{total}$ is total benefits, and $C_{total}$ is total costs.

To calculate the net present value, or the present value of cash flows over the analysis time frame, the following model was used (Equation (10))

$NPV={\displaystyle {\sum }_{t=0}^n\frac{C{F}_t}{{\left(1+r\right)}^t}}$ (10)

where $NPV$ is net present value, n is the total number of years, $C{F}_t$ is total cash flow for year t, t is the year, and r is the discount rate, which is used to translate future cash flows into present value by considering factors such as expected inflation and the ability to earn interest [56] . It is equal to the Brazilian nominal interest rate which accounts for both interest and inflation, and was determined using current forecasts for the years following 2024 [48] [57] [58] .

To calculate the internal rate of return, or the annual rate of growth that an investment is expected to generate, the following model was used (Equation (11))

$0={\displaystyle {\sum }_{t=0}^n\frac{C{F}_t}{{\left(1+IRR\right)}^t}}$ (11)

where $IRR$ is internal rate of return, n is the total number of years, $C{F}_t$ is total cash flow for year t, and t is the year.

The variables of 1) carbon credit market price and 2) available bagasse were identified as relevant variables to the results of the economic viability analysis of sugarcane bagasse-based biochar.

For the variable of carbon credit market price, current metrics suggest that biochar carbon credits are worth between $100 and $200 per ton of CO₂ emissions reduced/sequestered, with an average market price of $179/tCO₂e [1] [10] . However, these prices are a reflection of a relatively new market [59] , and at the scale of carbon credits in this study, this price may not be expected to hold. Therefore, a sensitivity analysis with their market price ranging from $50 to $200 per ton of CO₂e was chosen.

For the variable of bagasse availability, current sugarcane farms in Brazil are commonly using a percentage of their produced sugarcane bagasse for bioelectricity or heat in sugar and ethanol production processes [2] . The leftover bagasse (ex. 70% if the farm utilizes 30% for bioelectricity, a common practice), is what is available to be converted into biochar through pyrolysis without affecting current costs. Since the use of bagasse for bioelectricity varies from farm to farm depending on production, equipment, and bioelectricity usage, the percentage of bagasse available can also vary. Therefore, a sensitivity analysis with the percentage of available bagasse ranging from 50% to 90% was chosen.

As such, sensitivity analyses were conducted for both of these variables, analyzing the net present value of all studied scenarios with the variables ranging from 50 to 200 ($/tCO₂e) for carbon credit market price and 50 to 90 (%) for bagasse availability.

The economic models employed in this study were selected to provide a comprehensive assessment of biochar’s financial viability in Brazil, where both agricultural and carbon markets present unique challenges and opportunities. A life cycle cost analysis (LCCA) and net present value (NPV) were chosen to capture the full scope of investment and operational costs over the long term, particularly suited for high upfront capital projects like biochar production. To complement these, the internal rate of return (IRR) and cost-benefit analysis (CBA) models were applied to provide a clearer picture of profitability under different farm sizes and operational strategies. These models are well-suited for projects with high initial capital and long-term revenue streams, as they capture the full scope of investment, operating costs, and potential returns over time.

Given the nascent nature of the biochar and carbon credit markets, scenario-based modeling with these tools were necessary for a detailed examination of different operational scales and conditions.

To ensure scientific rigor and reproducibility, this study implemented these models using inputs derived from peer-reviewed sources, industry consultations, and localized agricultural data. Key parameters—such as bagasse conversion rates, carbon credit pricing, and operational costs—were adapted to reflect the specific conditions of the Brazilian sugarcane industry.

A series of sensitivity analyses on the most uncertain factors like carbon credit market prices and bagasse availability allowed for an exploration of the effects of market fluctuations on profitability and ensured that the results were adaptable to future shifts in both agricultural and environmental policy.

By integrating these established models and adapting them to Brazil’s specific agricultural context, the study ensures a rigorous, replicable approach, making this analysis both scientifically grounded and highly relevant for evaluating biochar’s feasibility in various agricultural settings.

The life cycle costs of all six studied scenarios are detailed in Figure 1 .

The initial investment for both scenarios of the small farm amounted to $57.5 million USD, with yearly costs over 20 years around $91 million USD for Scenario A and $148 million USD for Scenario B. For the initial investment, the highest amount lies in the equipment and setup of the pyrolysis plant, totaling $39.5 million USD. Out of the yearly costs, the highest costs are operation, followed by maintenance and labor for Scenario A, at $61 million USD, $23 million USD, and $7 million USD, respectively over 20 years accounting for inflation, and operation followed by land application and maintenance for Scenario B, at $61 million USD, $57 million USD, and $23 million USD, respectively over 20 years accounting for inflation.

For both scenarios of the medium farm, the initial investment amounted to $93 million USD, with yearly costs over 20 years around $148 million USD for Scenario A and $263 million USD for Scenario B. The equipment and setup of the pyrolysis plant account for the largest portion of the initial investment, coming in at $64 million. Once again, out of the yearly costs, the highest costs are operation, followed by maintenance and labor for Scenario A, at $99 million USD, $37 million USD, and $11 million USD, respectively over 20 years accounting for inflation, but for Scenario B, the highest cost was land application, followed by operation and maintenance, at $115 million USD, $99 million USD, and $37 million USD, respectively over 20 years accounting for inflation.

For the large farm, the initial investment in both scenarios was $177 million USD. Yearly costs over the 20 years were $281 million USD for Scenario A and $568 million USD for Scenario B. For the initial investment, the highest amount was the equipment and setup of the pyrolysis plant, totaling $121 million USD. Over 20 years and accounting for inflation, total yearly costs are most expensive for operation, followed by maintenance and labor in Scenario A, coming in at at $189 million USD, $70 million USD, and $21 million USD, respectively, and land application followed by operation and maintenance for Scenario B, coming in at $287 million USD, $189 million USD, and $70 million USD, respectively.

Overall, operation and equipment pose the largest costs, with land application costs being among the most expensive for all farms in Scenario B. Planning & design, permit, and carbon credit certification costs are negligible in all scenarios, accounting for less than 10% of total costs combined. Comparing scenarios directly, scenarios for the medium farm cost about 2 times the corresponding small farm scenarios, and large farm scenarios cost twice their corresponding medium farm scenarios, and four times their corresponding small farm scenarios. In general, Scenario B costs around one and a half times Scenario A. Finally, all scenarios would require a significant initial investment as well as significant yearly costs. The economic feasibility of such scenarios depends on available investment capital as well as the resulting revenue and savings.

Figure 2 details the break even for the studied scenarios based on cumulative costs and revenues, while Table 4 details the respective break even values in years.

The revenue to cost ratio over 20 for each scenario (increasing farm size, Scenario A then B), the revenue to cost ratio was 1.7, 2.3, 2.1, 2.7, 2.7, and 3.2, respectively, with large farm scenarios being the most profitable overall. All investments took less than 13 years to break even, and the large farm in Scenario B broke even in 5.15 years.

Figure 3 details the return on investment percentages for the studied scenarios, and Table 5 details the respective total return on investment percentages.

For each scenario (increasing farm size, Scenario A then B), the return on investment percentages ranged from 2% to 33%, −1% to 66%, 4% to 43%, 0% to 83%, 7% to 59%, and 1% to 113% over the 20 years, respectively.

The average return on investment per year for each scenario or each scenario (increasing farm size, Scenario A then B) was 13%, 27%, 18%, 35%, 26%, and 48%, respectively. Scenario B farms did significantly better than their Scenario A counterparts in terms of return on investment for all farm sizes.

The cost benefit comparison for the studied scenarios is detailed in Figure 4 .

The difference between the total revenues and total cost for all scenarios was positive, and for each one (increasing farm size, Scenario A then B), came out to 100, 408, 255, 873, 782, and 2326 million $ USD, respectively. Again, Scenario B farms outperform their Scenario A counterparts for all farm sizes, and large farm scenarios are the highest performing.

It can be noted that the fertilizer and crop management savings that result from biochar land application for Scenario B farms considered as a benefit in the analysis greatly exceed the land application cost that Scenario B farms spend.

Figure 5 details the net present value of 20 years for each of the studied scenarios.

The net present value for all scenarios except for small farm Scenario A is positive, indicating a rate of return above the discount rate, and, in general, a positive investment. For small farm Scenario A, the net present value is negative, indicating a negative investment and rendering the biochar implementation in the scenario not economically viable for the purposes of this study’s evaluation. For each scenario, (increasing farm size, Scenario A then B), the net present value is −6, 50, 25, 136, 158, and 436 million $ USD, respectively. Scenario B farms, due to their increased revenue streams, had a higher net present value than their Scenario A counterparts, and large farm scenarios had net present values about 5 times greater than the medium farms, and over 8 times greater than the small farms depending on the scenario.

The internal rate of return over 20 years for all studied scenarios are detailed in Figure 6 .

The internal rate of return, indicating the annual rate of growth that the investment is expected to generate, of 8%, 16%, 11%, 19%, 17%, and 25% for each scenario (increasing farm size, Scenario A then B) reveal that while all scenarios show positive growth, the larger farm scenarios demonstrate significantly higher returns. While all scenarios have a positive internal rate of return, the large farm scenarios have values around 1.6 times those of the small farm and 1.3 times those of the medium farm. Additionally, the internal rates of return for small farm Scenario A and medium farm Scenario A, being under 15%, indicate an unfavorable investment given the high investment costs found in the life cycle cost analysis.

Figure 7 details the sensitivity of the total net result, in present value over 20 years based on a carbon credit market price ranging from $50 to $200/tCO₂e.

For each scenario (increasing farm size, Scenario A then B), the carbon credit market price at which the net result was positive was $200, $100, $160, $70, $120, and $50/tCO₂e, respectively. In general, Scenario A for both small and medium farms produced negative results for any price below the current average market price of $179/tCO₂e, with small farm Scenario A not resulting in a positive result except when the price was $200/tCO₂e. This indicates the two scenarios may not be economically viable if the price drops. For the remaining scenarios, all produced a positive net result at about $120/tCO₂e, indicating an almost $60 margin for the market price to drop. This suggests that medium and large farms, particularly those in Scenario B, have a buffer to withstand fluctuations in the carbon credit market without becoming economically unfeasible. The highest performing scenario was large farm Scenario B, which had a positive net result even at $50/tCO₂e, a low price considering today’s biochar carbon credit market. Importantly, the highest fluctuation in net result was also observed in the large farm scenarios, with results varying by almost $450 USD million depending on the price. This highlights the dependence of the economic viability of a biochar operation even in large farms on the price that the generated carbon credits can be sold for.

The sensitivity of the total net result, in present value over 20 years based on bagasse availability ranging from 50% to 90% are detailed in Figure 8 .

The only scenarios to produce a negative net result at any value were small farm Scenario A and medium farm Scenario B, producing positive results at 70% and 80% bagasse availability, respectively. For the other scenarios, the net result remains positive for bagasse availability ranging from 50% to 90%, indicating viability in most cases where bagasse availability can be affected by an individual farm’s equipment and electricity demands.

While the results of this study demonstrate the potential economic viability of biochar, particularly for large-scale operations, several technical and economic challenges remain, especially in the context of Brazil’s agricultural sector. The pyrolysis process requires specialized equipment with high capital costs, making the initial investment a significant barrier, particularly for small and medium-sized farms.

Economically, the long-term feasibility of biochar production is closely tied to fluctuations in the carbon credit market and the operational costs associated with labor and energy. As demonstrated by the sensitivity analysis, carbon credit prices play a pivotal role in determining profitability. For smaller farms, biochar production becomes financially unfeasible when carbon credit prices fall. This highlights the vulnerability of biochar operations to market volatility, particularly in Brazil, where the carbon credit market is still maturing. Additionally, operational costs, particularly energy for pyrolysis and labor, remain significant, especially in regions where energy prices fluctuate or access to affordable labor is limited.

Moreover, Brazil’s fragmented agricultural landscape, characterized by both large agribusinesses and smallholder farmers, makes the uniform implementation of biochar technology difficult. Smaller farms, which could benefit most from the soil-enhancing properties of biochar, are often the least equipped to handle the associated technical and economic burdens. Without targeted financial incentives or subsidies, these farms may struggle to adopt biochar on a meaningful scale.

To address these challenges, policy support is critical. Government intervention in the form of subsidies, financial incentives, or carbon credit guarantees could alleviate some of the economic pressure on smaller operations. Infrastructure development, particularly in regions where transportation costs are high, would also enhance the scalability of biochar production. Without such measures, the broad-scale implementation of biochar in Brazil remains constrained by both technical and economic limitations.

This study has provided a detailed economic analysis of sugarcane bagasse-based biochar production in Brazil; however, several limitations must be acknowledged. One significant limitation is the lack of large-scale biochar operations implemented or extensively studied in practice. This introduces an inherent uncertainty regarding the real-world applicability and outcomes of the modeled scenarios. While the theoretical models and assumptions used are robust and well-grounded in existing literature, they may not fully capture the operational complexities and unforeseen challenges that might arise in actual biochar production and application.

Moreover, the values concerning the soil-amending and crop yield-increasing properties of biochar are specific to the area of the biochar operation, and significant variations in the effects of biochar application may arise depending on farm location [15] . Finally, the nascent nature of the biochar market, particularly in Brazil, adds another layer of uncertainty. The assumptions regarding market prices, availability of carbon credits, and economic incentives are based on current trends and projections, which can be highly variable and subject to change. While this study attempts to evaluate the impact of any uncertainties by conducting sensitivity analyses, considering these uncertainties and the evolving nature of biochar technology and market dynamics is crucial.