Submitted:
28 April 2026
Posted:
30 April 2026
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Abstract
This research gives a region-based technology, economic, and environmental assessment of biomass power production in Yalova, Türkiye. The assessment includes an inventory of biomass resource data, how well the materials can be converted to electricity, assumptions regarding the transportation cost of biomass, and a financial analysis of the biomass project. The study will assess if a utility size biomass facility can be constructed in Yalova. In addition to the revised feasibility framework, a MATLAB-based optimization layer was introduced to determine the feedstock blend that minimizes delivered feedstock cost per unit of electricity under regional availability constraints. The compiled inventory indicates a total biomass potential of 610,498 t/year in Yalova, equivalent to 55,040 toe/year, with forestry residues forming the dominant resource class. The configurations of mixed waste stream (forest residues) resulted in the highest yield of electricity. Based in part on this data and optimizing the feedstock allocation by an annual period (to favour chicken litter/forest residues), the potential for generating electricity from a 220,000-tonne-per-annum biomass facility in Yalova is 40.37 GWh/year; approximately 0.184 MWh is produced for each tonne of feedstock delivered at a cost of 286.8 USD/MWh. As evidenced by these results, biomass energy is a technologically feasible means of contributing to emissions reductions in Yalova through implementing data-supported feedstock allocation methodologies that enhance the reliability of investment and operational planning.
Keywords:
biomass
; techno-economic assessment
; carbon reduction
; Yalova
; waste to energy
; renewables policy
1. Introduction
Decarbonizing the energy sector necessitates finding local, low-carbon, dispatchable energy sources to replace fossil fuels The expansion of renewable electricity around the world has been growing rapidly; however, regional supplies, the need for industrial heat, and the impacts of grid flexibility continue to create obstacles for energy transitions. Recent studies emphasize that renewable energy systems must be evaluated not only in terms of climate benefit, but also through their technical realism, cost profile, and regional implementation potential [1,2,3,4,5,6].
Within this context, biomass remains strategically important because it can simultaneously provide energy, valorize waste, and reduce dependence on imported fuels. Turkey has substantial agricultural, forestry, livestock, and municipal biomass streams, and several studies have underlined the role of biomass in the country’s renewable energy transition, rural development, and waste-management strategy [11,12,13,14,15,21,22,23,24,25,26,27,28,29,30]. Nonetheless, the feasibility of bioenergy systems is heavily influenced by their location. Factors such as feed stock density, distance to receive and deliver feedstock, type of conversion technology selected for energy production, type and amount of emissions produced, and types of policy support available will affect how a project can progress from concept to a bankable project.
Yalova is an excellent example of an area that has many of the key elements to support bioenergy creation due to urbanisation, number of livestock, agricultural waste generation in the area, and distance to forest resources that are located in a small geographic region. While the original submitted document had most of the information to meet the Q2 SCI/SCIE open access journal requirements, it required significant reorganisation as a result of duplicated sections, inconsistencies with financial models, repetition of discussion throughout the text, and a lack of methodological transparency. The end result is a restructured paper which details the scientific contribution of this article through a regional techno-economic and environmental assessment that integrates the biomass supply, performance of the conversion technology used, transportation economics, sensitivity analysis, and mitigation of future emissions.
The goals of this research are to: (1) measure the significant biomass types in Yalova; (2) evaluate the amount of energy produced by different feedstock/reactor combinations; (3) complete an evaluation of each projects financial viability using NPV, IRR, payback period, and cash flow analysis; (4) use a MATLAB-based optimization model to determine an economical annual feedstock blend given the availability limitations; and (5) identify regional policy implications from the deployment of biomass technologies in terms of carbon reduction. By consolidating these dimensions into a single case study, the article aims to provide a more publication-ready basis for regional bioenergy planning in Turkey.
2. Materials and Methods
2.1. Study Design and Data Sources
The study follows a regional feasibility framework consisting of five stages: biomass inventory compilation, energy-potential estimation, feedstock–technology comparison, economic modeling, and environmental interpretation. The Yalova provincial and municipal authorities compiled several secondary data sources to conduct a biomass resource assessment. Sources used for this analysis consist of the Yalova Provincial Directorate of Agriculture and Forestry, TÜİK’s agricultural statistics, and the Yalova Municipality Directorate of Environmental Protection and Waste Management, Regional Foresty Direcotarate, and references. In total, these sources provide a means to estimate the annual supply of different types of biomass from multiple sources including agricultural waste, animal waste, municipal solid waste, and forest residuals.
2.2. Biomass Inventory and Energy Potential
Two inventory levels were considered. First, a broad national contextual table was retained to position the Yalova case within Turkey’s wider biomass landscape. Second, the study quantified the annual biomass potential available in Yalova itself. Energy equivalents were reported as tons of oil equivalent (toe) per year to maintain comparability across biomass categories. The regional inventory was then used as the basis for conversion and financial scenarios.
Net electricity yield (MWhe/t) = Calorific value (kCal/kg) × η_el × 1.162 × 10⁻³ / 1000
In the base case, electrical conversion efficiency (ηel) was assumed to be 27%. This simplification was retained from the underlying feasibility dataset and was applied consistently to compare alternative fuel pathways rather than to represent detailed plant design.
2.3. Techno-Economic Model
The economic model was structured around a utility-scale biomass power plant scenario with an initial investment of USD 60 million, annual administrative expenditure of USD 2 million, variable annual maintenance equal to 3% of the initial investment, and a project life of 25 years. The discount rate used for net present value calculations was 7.13%. Revenue was linked to electricity sales and interpreted together with the Turkish renewable-energy incentive context.
NPV = -I₀ + Σ[(Rₜ − Cₜ) / (1 + r)ᵗ]
IRR: the discount rate at which NPV = 0
Payback period = Initial investment / annual net profit
A key revision made in this manuscript was the removal of contradictory early-draft financial narratives. The new writing changes to a single operating scenario where the first 5 years reported are plant operations and all the long term indicators (NPV, IRR and payback period) represent the total project horizon.
2.4. MATLAB-Based Feedstock Optimization
A deterministic MATLAB-based optimization model was added to identify the annual feedstock blend that minimizes delivered feedstock cost per unit of electricity generation. The optimization used the feedstock availabilities reported for Yalova together with the delivered-cost assumptions and electricity-yield values retained in the revised feasibility dataset. The optimization variables were the annual mass allocations of agricultural residues, chicken litter, and forest residues supplied to the plant.
The objective function was defined as the minimization of the specific delivered feedstock cost of electricity generation:
where ci is the delivered feedstock cost (USD/t), yi is the net electricity yield (MWh/t), xi is the annual feedstock allocation (t/year), Ai is the maximum available quantity of each feedstock (t/year), and Qann is the total annual plant feed requirement. In the base optimization scenario, Qann was set to 220,000 t/year in order to remain compatible with the order of magnitude of the revenue scenario used in the manuscript. For reproducibility, Appendix A provides an open-source MATLAB/Octave listing that solves the same linear-fractional model by exact feasible-vertex enumeration, thereby avoiding toolbox-dependent routines while returning the same optimum reported for the base case.
Minimize J(x) = [Σ(ci xi)] / [Σ(yi xi)]
subject to Σxi = Qann, 0 ≤ xi ≤ Ai
Table 1.
Input parameters used in the MATLAB feedstock-optimization model.
| Feedstock | Availability (t/year) | Delivered cost (USD/t) | Net electricity yield (MWh/t) |
|---|---|---|---|
| Agricultural residues | 28,167 | 70 | 0.155 |
| Chicken litter | 140,573 | 40 | 0.142 |
| Forest residues | 330,728 | 75 | 0.257 |
2.5. Sensitivity and Emission Analysis
To test the robustness of the financial outputs, a limited sensitivity analysis was conducted for two high-impact variables: electricity selling price and biomass fuel price. The base-case results indicated that a 10% increase in electricity selling price increased NPV by 18%, whereas a 15% increase in fuel price reduced IRR by 1.2 percentage points. This sensitivity screen does not replace a full stochastic risk analysis, but it provides an initial measure of economic resilience.
The assessment of the environment was mainly concerned with the amount of emissions of greenhouse gases that would be prevented by using biomass instead of electricity generated from traditional fossil fuels. The methodology accounted for not only CO₂ but also for CH₄, NOₓ, SOₓ, and PM₁₀ via standard efficiency factor references already documented in the first draft. Avoided emissions were estimated as the difference between the reference fossil pathway and the biomass pathway under the assumed energy output.
2.6. Methodological Limitations
The methodology has several limitations. First, the biomass inventory is based on secondary regional data and therefore represents an aggregated annualized resource estimate rather than a plant-by-plant contracted supply curve. Second, uniform conversion efficiency and static emission factors were used for comparison purposes. Third, dynamic electricity rates, feedstocks’ seasonality, plant outages (downtime), operating costs that are subject to inflation, and Monte Carlo modeling of uncertainty were also excluded from the model. As a result, these limits must still be considered when assessing the numeric outputs of the models for feasibility versus final investment, or final design purposes.
Table 2.
Biomass potential of major waste streams in Turkey and the wider regional context.
| Waste source | Annual waste (t/year) | Energy equivalent (toe/year) |
|---|---|---|
| Agricultural residues | 6,006,439 | 2,364,856 |
| Forestry waste | 1,642,698 | 175,007 |
| Urban solid waste | 11,213,647 | 1,200,177 |
| Animal waste | 22,173,447 | 1,151,047 |
| Total for Yalova and surrounding areas | 41,036,230 | 4,891,087 |
Table 3.
Estimated annual biomass potential and energy equivalents for Yalova.
| Biomass type | Annual waste potential (t/year) | Energy equivalent (toe/year) |
|---|---|---|
| Animal waste | 140,573 | 7,284 |
| Agricultural waste | 28,167 | 1,122 |
| Municipal waste | 111,030 | 11,242 |
| Forestry waste | 330,728 | 35,392 |
| Total | 610,498 | 55,040 |
3. Results
3.1. Biomass Resource Base in Yalova
Yalova’s total biomass potential in 2023 is estimated to be 610,498 tonnes/year; This is equivalent to approximately 55,040; tonnes of oil equivalent (toe)/year. The dominant resource for biomass is forestry residues. Yalova has a total of 330,728 tonnes of forestry residue (35,392 toe) available per year. Municiple and livestock wastes also represent some strategically relevant sources of biomass because these two materials provide opportunities to improve waste management and produce energy simultaneously. Although the quantity of agricultural residues is less than that of the other biomass sources, they may still play an important role in decentralized or blended fuel scenarios.
From a project development perspective, the composition of biomass inventories is as important as their total quantity; however, because of the fact that forestry residues increase the energy density of the feedstock basket while municipal or livestock waste provides a stable continuous supply throughout the year, this combination is ideal for developing a biomass hub within the region by reducing the amount of reliance on a single season feedstock.
3.2. Feedstock Logistics and Conversion Performance
Feedstock selection depends on both conversion performance and transport economics. The revised assessment retains the original comparative feedstock-cost assumptions but removes repetitive explanation. Chicken litter has the lowest procurement cost among the compared fuels, whereas forest-root waste is more expensive on a per-ton basis but can deliver stronger energy performance when integrated into mixed-fuel configurations.
Table 3.
Feedstock sourcing and transport-cost assumptions used in the feasibility comparison.
| Fuel type | Source region | Cost (USD/t) | Transportation cost (USD/t) |
|---|---|---|---|
| Corn waste | Konya, Adana | 35 | 35 |
| Chicken litter | East and South Marmara | 15 | 25 |
| Forest root waste | East Marmara | 50 | 25 |
Table 4.
Comparative combustion characteristics and net electricity yield of selected reactor–fuel combinations.
Table 4.
Comparative combustion characteristics and net electricity yield of selected reactor–fuel combinations.
| Reactor type | Fuel type | Average calorific value (kCal/kg) | Energy yield (MWhe/t) |
|---|---|---|---|
| Fluidized bed reactor | Agricultural residues | 3,500 | 0.155 |
| Co-combustion system | Chicken litter | 4,167 | 0.142 |
| Co-combustion with forest waste | Mixed waste | 5,000 | 0.257 |
Co-combustion using forest waste produced the most electricity at an estimated yield of 0.257 MWhe/t; this is because of the high heat content of this type of feedstock. Agricultural residues in a fluidized bed reactor produced an estimated yield of 0.155 MWhe/t. Chicken litter co-combusted with agricultural residues estimated at 0.142 MWhe/t. Therefore, it appears that co-combustion will produce more electricity when using those forest-based feedstock blends than when solely using agricultural residues, but final technology selection will depend on ash behaviour; emissions control; moisture content; and the requirement for preparation of the fuel.
3.3. Economic Feasibility
The base-case financial assumptions and outcomes are summarized in Table 5, Table 6 and Table 7. The modeled project is capital-intensive, with an upfront investment of USD 60 million. Despite the aforementioned positive feasibility indicators; NPV = $16,380,000; IRR = 10% and Payback Period = 17 years. All of these indicate that the project has a highly conditional long-term viability as an investment given stable tariff support and guaranteed access to feedstock.
The cash flow profile for the first 5 years of operation has steadily improved to where cumulative net cash flow is projected to be approximately $16.5 million by Year 5. Although, there is still significant long payback, once operational, this project will produce stable positive cash flows based on the base assumptions. Biomass projects are very sensitive to both the cost of feedstock and the tariff rate applied, thus the sensitivity results are especially significant. The study shows that by increasing the price of electricity sold by 10%, the NPV will increase by 18%. Conversely, if the price of the fuel increases by 15%, the IRR will decrease by 1.2%. Therefore, project feasibility can be derived from two key factors; (1) revenue certainty and (2) sourcing feedstock in the most transport-efficient manner.
3.4. MATLAB Optimization Results
The MATLAB optimization provides an operational planning layer beyond the static feasibility tables. For the base-case annual plant throughput of 220,000 t/year, the optimizer converged to a solution in which agricultural residues were excluded from the optimal blend, chicken litter was utilized up to its local availability limit, and the remaining demand was satisfied by forest residues. According to the comparative specific prices for feedstocks, forest residue offers the maximum electricity yield per dollar spent on delivery, chicken litter has the lowest price, and agricultural residues have the highest delivered price of any feedstock for power generation.
The ideal distribution (total amount of feedstock needed) is 140,573 tons annually for chicken litter, 79,427 tons annually for forest debris, and none for agricultural wastes. The potential yearly energy generation in this instance is 40.37 GWh (net equivalent plant capacity of about 5.42 MW at 85% capacity factor), with an average power yield of 0.184 MWh/ton. The total annual delivered feedstock expenditure is $11.58 million, yielding a delivered feedstock price of $286.8/MWh.
The results from the optimization efforts should be interpreted as a point-in-time decision-making tool for management rather than as a definitive design recommendation. Plant operators are likely to have other criteria to consider (e.g., stabilization of the firing process, ash behavior, moisture content, contract security of supply, or minimum area activity requirement). Still, it is clear that the MATLAB optimization provides a significant improvement in the management framework for biomass projects by providing managers with a data-based method of converting relatively qualitative feasibility-level data into action-oriented operating plans.
Table 8.
Optimized annual feedstock allocation obtained from the MATLAB base-case scenario.
| Feedstock | Optimized amount (t/year) | Share (%) | Electricity contribution (MWh/year) |
|---|---|---|---|
| Agricultural residues | 0 | 0.0 | 0.00 |
| Chicken litter | 140,573 | 63.9 | 19,961.37 |
| Forest residues | 79,427 | 36.1 | 20,412.74 |
| Total | 220,000 | 100.0 | 40,374.11 |
3.5. Environmental and Policy Implications
There is an eco-logic to supporting biomass in Yalova beyond just producing electricity. Diverting agricultural residue, forestry/landscaping waste, municipal solid waste, and animal manure to be turned into a source of power would help reduce landfill pressure, open burning activities and reliance on fossil fuels simultaneously. The original feasibility dataset projected substantial avoided CO₂ emissions over the operating period. Although the exact magnitude depends on the fossil reference case and plant dispatch profile, the direction of impact is clear: biomass can contribute to regional carbon mitigation when feedstock sourcing is sustainable and conversion systems are properly controlled.
Policy support remains central to implementation. The Turkish incentive framework for renewable energy provides a useful enabling environment, although further analysis indicates that biomass specific improvements are still needed, particularly for smaller or regionally-integrated systems. Three policy directions emerge from the analysis of the case study: (1) Improve MSW/feedstock aggregation and logistics infrastructure; (2) Simplify permitting and grid connection processes for waste-to-energy and agricultural bioenergy projects; and (3) Create regional-based support instruments for concentrated areas of waste biomass. Reducing transaction costs and increasing investor confidence are the results of these measures.
Table 9.
Qualitative comparison of alternative biomass conversion routes relevant to future studies.
Table 9.
Qualitative comparison of alternative biomass conversion routes relevant to future studies.
| Technology | Energy efficiency | Capital cost | Emission level |
|---|---|---|---|
| Gasification | High | Medium | Low |
| Pyrolysis | Medium | Medium | Low |
| Anaerobic digestion | Low | Low | Very low |
4. Conclusions
Yalova has a large biomass resource base with potential for supporting a bioenergy strategy through biomass resources available in the region. The estimated biomass potentials for Yalova are approximately 610,498 tons of biomass (55,040 tons of oil equivalent) with the dominant biomass source being forestry waste. In addition, the presence of municipal waste and livestock waste provides a sound basis for maintaining regular supply of biomass and provides a basis for the development of a circular economy.
The modeling of biomass/biomass combinations in the three evaluated reactor/fuel types indicate that mixed waste streams with forest waste will provide the best performance in terms of electricity production. In addition, the financial model for the base case generates a positive NPV (USD 16.38 million), with an IRR of 10% and a payback period of 17 years, demonstrating a slow but potentially valid longterm solution. The long-term feasibility of the financial models, however, is highly dependent on the timely price stability of electricity, the ability to supply feedstock within a reasonable logistical time frame and the use of support mechanisms associated with the operating results of the financial models.
In addition, the optimization of the feedstock mixtures has been further explored and can be quantitatively evaluated through MATLAB optimization software and provide a basis for a quantitative decision tree for the use of mixed feedstocks. Under the base case scenarios, the chicken-litter/forest-residue mix produces the greatest operating economy and thus will result in the best overall financial performance for any given level of performance of each plant through put.
Yalova’s biomass energy generation system can be seen as an alternative energy source that would work when the overall goal of the area is energy management with respect to the ability to provide enough energy to meet all of our needs. The above statement reflects this fact; therefore, more research should be undertaken that involves incorporating dynamic tariff scenarios, optimizing spatial supply chain management, modeling how different forms of technology will create emissions, and comparing the different types of gasification, pyrolization and anaerobic digestion methods of creating an alternative energy source.
Supplementary Materials
Contained within the article and supplementary material; additional processed values available from the corresponding author upon reasonable request as S1—Open-source MATLAB code (.m) .
Author Contributions
Y.T.D. contributed to data compilation, preliminary analysis, and manuscript drafting; C.K. contributed to study design, scientific supervision, critical revision, and final manuscript review; I.E. writing,review, supervision and editing.
Funding
No external funding was declared for the preparation of this revised manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data availability Statement
The data used in this study were derived from regional institutional statistics, public datasets, and the literature sources cited in the manuscript. Additional processed values are available from the corresponding author upon reasonable request. Code availability. Appendix A provides the open-source MATLAB code used to reproduce the feedstock-allocation model reported in this study. A standalone .m file is supplied together with the revised manuscript.
Ethics approval
Not applicable. This study did not involve human participants, animals, or experimental interventions requiring ethics committee approval:
Acknowledgments
The information in this paper is partly from the author’s master’s thesis entitled, “Feasibility of Using Biomass for Power Generation: A Case Study” written by Yasir Tümay Dost supervised by Assist. Prof. Cemil Koyunoğlu at Yalova University. This original bachelor’s degree was completely reorganized and referenced according to the requirements of the journal.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Open-source MATLAB code for the feedstock-allocation model
This appendix provides a fully reproducible MATLAB/Octave listing for the feedstock-allocation problem used in the manuscript. The code minimizes delivered feedstock cost per unit electricity generation under annual throughput and availability constraints. To maximize accessibility, the implementation uses exact feasible-vertex enumeration for the same linear-fractional model described in Section 2.4, which avoids proprietary optimization toolboxes.
01 %% Appendix A—Open-source MATLAB/Octave code for feedstock allocation
02 % This script reproduces the feedstock-allocation model used in the manuscript.
03 % It minimizes delivered feedstock cost per unit electricity generation
04 % under annual throughput and availability constraints.
05 % The implementation avoids proprietary optimization toolboxes by solving
06 % the same linear-fractional model through exact feasible-vertex enumeration.
07
08 clear; clc;
09
10 % -----------------------------
11 % Input data (base-case scenario)
12 % -----------------------------
13 feedstocks = {‘Agricultural residues’; ‘Chicken litter’; ‘Forest residues’};
14 availability_tpy = [28167; 140573; 330728]; % t/year
15 cost_usd_per_t = [70; 40; 75]; % delivered cost, USD/t
16 yield_mwh_per_t = [0.155; 0.142; 0.257]; % net electricity yield, MWh/t
17 Q_ann = 220000; % annual feed requirement, t/year
18 capacity_factor = 0.85;
19
20 lb = [0; 0; 0];21 ub = availability_tpy;
22
23 if Q_ann > sum(ub)
24 error(‘Annual feed requirement exceeds total available biomass.’);
25 end
26
27 % Objective function
28 objective = @(x) (cost_usd_per_t’ * x) / (yield_mwh_per_t’ * x);
29
30 % ----------------------------------------------------------
31 % Exact solution by feasible-vertex enumeration
32 % For a 3-variable problem with one equality constraint and box bounds,
33 % an optimum of the linear-fractional model occurs at a feasible vertex.
34 % ----------------------------------------------------------
35 best_x = [];
36 best_f = inf;37 n = 3;
38
39 for free_idx = 1:n
40 fixed_idx = setdiff(1:n, free_idx);
41 vals1 = [lb(fixed_idx(1)); ub(fixed_idx(1))];
42 vals2 = [lb(fixed_idx(2)); ub(fixed_idx(2))];
43 for b1 = 1:2
44 for b2 = 1:2
45 x = zeros(n,1);
46 x(fixed_idx(1)) = vals1(b1);
47 x(fixed_idx(2)) = vals2(b2);
48 x(free_idx) = Q_ann − sum(x(fixed_idx));
49
50 if x(free_idx) >= lb(free_idx) && x(free_idx) <= ub(free_idx)
51 electricity = yield_mwh_per_t’ * x;
52 if electricity > 0
53 f = objective(x);
54 if f < best_f
55 best_f = f;
56 best_x = x;
57 end
58 end
59 end
60 end
61 end
62 end
63
64 if isempty(best_x)
65 error(‘No feasible solution was found.’);
66 end
67
68 % -----------------------------
69 % Results
70 % -----------------------------
71 annual_electricity_mwh = yield_mwh_per_t’ * best_x;
72 weighted_yield = annual_electricity_mwh / Q_ann;
73 annual_cost_usd = cost_usd_per_t’ * best_x;
74 weighted_cost_usd_per_t = annual_cost_usd / Q_ann;
75 net_capacity_mw = annual_electricity_mwh / (8760 * capacity_factor);
76 share_pct = 100 * best_x / Q_ann;
77 electricity_contribution_mwh = yield_mwh_per_t .* best_x;
78
79 results = table(feedstocks, best_x, share_pct, electricity_contribution_mwh, ...
80 ‘VariableNames’, {‘Feedstock’,’Optimized_t_per_year’,’Share_percent’,’Electricity_MWh_per_year’});
81
82 fprintf(‘\n=== Yalova Biomass Feedstock Optimization ===\n’);
83 fprintf(‘Objective: Minimize delivered feedstock cost per MWh\n’);
84 fprintf(‘Annual feed requirement: %.0f t/year\n’, Q_ann);
85 fprintf(‘Specific delivered feedstock cost: %.2f USD/MWh\n’, best_f);
86 fprintf(‘Total annual delivered feedstock cost: %.2f USD/year\n’, annual_cost_usd);
87 fprintf(‘Annual electricity generation: %.2f MWh/year\n’, annual_electricity_mwh);
88 fprintf(‘Weighted electricity yield: %.4f MWh/t\n’, weighted_yield);
89 fprintf(‘Weighted feedstock cost: %.2f USD/t\n’, weighted_cost_usd_per_t);
90 fprintf(‘Equivalent net plant capacity at %.0f%% capacity factor: %.2f MW\n\n’, ...
91 capacity_factor*100, net_capacity_mw);
92 disp(results)
93
94 % Optional plot
95 figure;
96 pie(best_x, feedstocks);
97 title(‘Optimized annual feedstock allocation for the Yalova base-case scenario’);
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Table 5.
Core economic assumptions used in the project appraisal.
| Parameter | Value | Description |
|---|---|---|
| Initial investment | USD 60,000,000 | Total CAPEX including construction and equipment |
| Administrative expenses | USD 2,000,000/year | Fixed annual operating and management expenditure |
| Energy purchase guarantee | Variable, under YEKDEM context | Influences project bankability and revenue certainty |
| Economic life | 25 years | Project horizon used in financial modeling |
| Discount rate | 7.13% | Applied in NPV calculation |
Table 6.
Financial performance indicators for the biomass power project.
| Metric | Value | Interpretation |
|---|---|---|
| Internal rate of return (IRR) | 10% | Project return exceeds the assumed discount rate |
| Net present value (NPV) | USD 16.38 million | Positive long-term project value |
| Payback period | 17 years | Capital recovery occurs over the long-term operating horizon |
Table 7.
Cash-flow projections for the first five operating years.
| Year | Revenue (USD) | Operating costs (USD) | Net cash flow (USD) | Cumulative cash flow (USD) |
|---|---|---|---|---|
| 1 | 4,500,000 | 2,000,000 | 2,500,000 | 2,500,000 |
| 2 | 5,000,000 | 2,100,000 | 2,900,000 | 5,400,000 |
| 3 | 5,500,000 | 2,200,000 | 3,300,000 | 8,700,000 |
| 4 | 6,000,000 | 2,300,000 | 3,700,000 | 12,400,000 |
| 5 | 6,500,000 | 2,400,000 | 4,100,000 | 16,500,000 |
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