Levelized Cost of Lettuce (LCoL) Analysis

• Levelized Cost of Lettuce (LCoL) is a metric that divides annualized capital and operating expenses by total lettuce production to gauge system viability.
• The metric integrates detailed cost inputs such as lighting, wiring, HVACD, and structural expenses, highlighting sensitivity to local energy prices and environmental settings.
• Results show a trade-off between increased PPFD for growth and higher energy costs, with significant geographic variability influencing both economic performance and carbon emissions.

Searching arXiv for the specified paper to ground the article and citation. Levelized Cost of Lettuce (LCoL) is a cost metric for vertical farming that expresses the sum of annualized capital costs, including replacements, and annual operating expenditures per unit of annual lettuce output. In “Toward Sustainable Vertical Farming: Impacts of Environmental Factors and Energy Mix on Performance and Costs” (Ceccanti et al., 24 Jul 2025), LCoL is used to evaluate the economic viability of a vertical farming system across 162 scenarios combining temperature, photosynthetic photon flux density (PPFD), and CO$_2$ concentration in Norway, China, and Dubai. Within that framework, LCoL functions as an integrative measure that balances productivity against energy use, local electricity prices, labor, leasing, and other operating factors, thereby linking crop-growth simulation, energy modeling, and techno-economic assessment.

1. Definition and formal structure

In the cited study, the Levelized Cost of Lettuce is defined as the sum of annualized capital costs, including replacements, plus annual operating expenditures, divided by the total annual lettuce yield (Ceccanti et al., 24 Jul 2025). The paper gives the formulation as

$$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$$

The variables are specified as follows: CAPEX is the initial capital expenditure, $C_{\mathrm{Rep}}$ is the present value of future replacements, CRF is the capital recovery factor, OPEX is the annual operating expenditure, and $M_{\mathrm{crop}}$ is the annual fresh-matter lettuce production in kg/year (Ceccanti et al., 24 Jul 2025).

The same source computes the real discount rate from the nominal interest rate $r_{\mathrm{nom}} = 8.5\%$ and inflation $i = 2\%$ as

$$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$

with the capital recovery factor

$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$

and system lifetime $n = 20$ years (Ceccanti et al., 24 Jul 2025).

This formulation places LCoL within the family of levelized cost metrics used in infrastructure and energy-system analysis. A plausible implication is that the metric is intended not merely as an accounting ratio but as a normalized decision variable for comparing alternative environmental setpoints and site conditions.

2. Cost components and annualization

The study decomposes CAPEX into lighting, wiring, HVACD, vertical-farm structure, and insulation costs (Ceccanti et al., 24 Jul 2025). The reported expression is

$$\mathrm{CAPEX} = C_{\mathrm{Light}}\,P_{\mathrm{Light}} + C_{\mathrm{Wir}}\,\bigl(P_{\mathrm{Light}}+P_{\mathrm{HVACD}}\bigr) + C_{\mathrm{HVACD}}\,P_{\mathrm{HVACD}} + C_{\mathrm{VF}}\,A_{\mathrm{crop}} + C_{\mathrm{Ins}}\,A_{\mathrm{env}}.$$

The parameter values reproduced in the source are $$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$$03.76\,\mathrm{W}^{-1}LCoL=(CAPEX+CRep) CRF+OPEX⏟Annualized Total CostMcrop.\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.1C_{\mathrm{Wir}} = $\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$2 for electrical wiring, $\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$31.39\,\mathrm{W}^{{-1}$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$4C_{\mathrm{VF}}} = $\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$5 for structure and shelving, and $\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$64.80\,\mathrm{m}^{{-2}$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$7A_{\mathrm{crop}}} = 90\,\mathrm{m}<sup>2$ (Ceccanti et al., 24 Jul 2025).

Replacement cost is treated explicitly. LEDs are replaced twice, in year 8 and year 16, with present value

$$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$$8

OPEX is formulated as

$$\mathrm{LCoL} = \frac{ \underbrace{\bigl(\mathrm{CAPEX} + \mathrm{C_{Rep}}\bigr)\,\mathrm{CRF} + \mathrm{OPEX}}_{\text{Annualized Total Cost}} }{ \mathrm{M_{crop}} }.$$9

where $C_{\mathrm{Rep}}$01.14\,\mathrm{kg}^{-1}CRepC_{\mathrm{Rep}}1C_{\rm CO_2} = $C_{\mathrm{Rep}}$2 as a global average; labor time is $C_{\mathrm{Rep}}$3; and $C_{\mathrm{Rep}}$4 of land leased. Electricity price, water price, hourly wage, and leasing cost are location dependent (Ceccanti et al., 24 Jul 2025).

This structure makes clear that LCoL is sensitive both to engineering design and to local factor prices. The paper states that climate is the dominant cost driver via grid prices, leasing, and labor, which suggests that geographically identical growing recipes need not be economically equivalent across sites.

3. Coupling between crop productivity and energy demand

The study analyzes 162 scenarios produced by combining three levels of temperature, PPFD, and CO$C_{\mathrm{Rep}}$5 concentration across three climatic zones, with two insulation thicknesses tested in each scenario (Ceccanti et al., 24 Jul 2025). According to the reported results, neither the insulation layer nor the external climate significantly influences crop productivity because of the heating, ventilation, and air conditioning and dehumidification (HVACD) system. PPFD proved to be the dominant factor in crop growth, with correlation $C_{\mathrm{Rep}}$6, followed by CO$C_{\mathrm{Rep}}$7 at $C_{\mathrm{Rep}}$8 and indoor temperature at $C_{\mathrm{Rep}}$9 (Ceccanti et al., 24 Jul 2025).

The same work reports that PPFD also emerged as the primary driver of overall energy consumption, with correlation $M_{\mathrm{crop}}$0, because it affects both lighting and HVACD loads. Specific Energy Consumption (SEC) ranged from $M_{\mathrm{crop}}$1 to $M_{\mathrm{crop}}$2 across the 162 scenarios, and lighting together with associated cooling accounted for 70–95% of SEC (Ceccanti et al., 24 Jul 2025). At the cost-optimal point, defined as $M_{\mathrm{crop}}$3, $M_{\mathrm{crop}}$4 PPFD, $M_{\mathrm{crop}}$5 CO$M_{\mathrm{crop}}$6, with insulation, the SEC was approximately $M_{\mathrm{crop}}$7, comprising lighting at about $M_{\mathrm{crop}}$8 and HVACD at about $M_{\mathrm{crop}}$9 (Ceccanti et al., 24 Jul 2025).

Converted to electricity via two air-to-water heat pumps, the Specific Electric Energy Consumption (SEEC) at that operating point was about $r_{\mathrm{nom}} = 8.5\%$0 in Trondheim, $r_{\mathrm{nom}} = 8.5\%$1 in Shanghai, and $r_{\mathrm{nom}} = 8.5\%$2 in Dubai (Ceccanti et al., 24 Jul 2025).

A central result is that the lowest SEC coincided with the lowest crop productivity, reported as $r_{\mathrm{nom}} = 8.5\%$3 (Ceccanti et al., 24 Jul 2025). This is important because LCoL is not minimized by minimizing energy input alone. The denominator, annual lettuce production, is endogenous to the environmental recipe, so reduced input intensity can also reduce throughput enough to worsen the cost per kilogram.

4. Cost-optimal operating point and geographic variation

The paper identifies a single lowest-LCoL configuration across all climates: $r_{\mathrm{nom}} = 8.5\%$4, $r_{\mathrm{nom}} = 8.5\%$5 PPFD, $r_{\mathrm{nom}} = 8.5\%$6 CO$r_{\mathrm{nom}} = 8.5\%$7, with insulation (Ceccanti et al., 24 Jul 2025). The resulting LCoL values are summarized below.

Location	LCoL (USD/kg)
Trondheim (NO)	6.38
Shanghai (CN)	4.57
Dubai (UAE)	6.48

In Trondheim, this value breaks down to roughly $r_{\mathrm{nom}} = 8.5\%$82.75/\mathrm{kg}$r_{\mathrm{nom}} = 8.5\%$9$i = 2\%$0 annualized CAPEX (Ceccanti et al., 24 Jul 2025). The reported values indicate that the same agronomic and engineering recipe can produce materially different cost outcomes across locations.

The study also provides an excerpt for Trondheim at $i = 2\%$1, $i = 2\%$2 CO$i = 2\%$3, insulated, comparing three PPFD levels: at PPFD 100, 250, and 400, the LCoL values are $i = 2\%$48.10$i = 2\%$5$i = 2\%$6, and $i = 2\%$77.25$, respectively (Ceccanti et al., 24 Jul 2025). This illustrates what the source describes as a “sweet spot” at intermediate PPFD. A plausible implication is that the productivity response to increased light is nonlinear and eventually offset by the additional CAPEX and OPEX associated with lighting and HVACD loads.

5. Sensitivity structure and dominant drivers

The paper uses distance correlation coefficients $i = 2\%$8 to quantify each input’s influence on LCoL (Ceccanti et al., 24 Jul 2025). The reported values are as follows.

Variable	$i = 2\%$9 with LCoL
Climate	0.734
CO$$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$0 Conc.	0.327
PPFD	0.200
Temperature	0.108
Insulation	0.050

The source states that climate, via grid prices, leasing, and labor, is the dominant cost driver. CO$$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$1 fertilization lowers LCoL, with $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$2, because faster growth improves the production denominator, but beyond approximately $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$3 returns diminish (Ceccanti et al., 24 Jul 2025). Higher PPFD raises CAPEX and OPEX strongly, with $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$4 for total energy, but boosts productivity nonlinearly, yielding only a modest net effect on LCoL, reported as $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$5 (Ceccanti et al., 24 Jul 2025). Indoor temperature and insulation have only minor impacts on LCoL once optimized (Ceccanti et al., 24 Jul 2025).

These results delimit a frequent misconception that lighting intensity should simply be maximized to improve output. The reported findings do not support that simplification. Instead, PPFD is simultaneously a productivity lever and an energy-cost amplifier, and the optimum emerges from the interaction between those effects rather than from either in isolation.

6. Carbon intensity, imported lettuce, and decarbonization constraints

Beyond cost, the study compares vertically farmed lettuce with an equivalent imported supply chain under different electricity-grid emission factors (Ceccanti et al., 24 Jul 2025). The electricity-grid emission factor $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$6 is taken from national data and reported as approximately $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$7 for Norway, whose grid is described as 89% hydro and 9% wind; approximately $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$8 for China; and approximately $$r_{\mathrm{real}} = \frac{1+r_{\mathrm{nom}}}{1+i}-1 \approx 6.37\%,$$9 for the UAE (Dubai) (Ceccanti et al., 24 Jul 2025).

The reported CO$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$0 savings relative to imported lettuce are:

Location	CO$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$1 Saving (g CO$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$2/kg)
Trondheim	–230 (–70%)
Shanghai	+650 (+60% penalty)
Dubai	+550 (+45% penalty)

The source concludes that only the near-zero-carbon Norwegian grid yields net CO$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$3 savings (Ceccanti et al., 24 Jul 2025). In China and Dubai, vertical farming currently increases the carbon footprint unless the grid decarbonizes or SEC falls by approximately 60–99% (Ceccanti et al., 24 Jul 2025).

This establishes a distinction between economic and environmental optimality. The lowest-LCoL configuration is not, by itself, sufficient to guarantee emissions reductions. A plausible implication is that LCoL should be interpreted jointly with grid carbon intensity whenever vertical farming is assessed as a climate mitigation strategy.

7. Interpretation within vertical-farming assessment

The study situates LCoL within a broader analysis of efficiency, sustainability, and economic viability for indoor agriculture (Ceccanti et al., 24 Jul 2025). The dominant role of PPFD in crop growth, the large share of lighting and associated cooling in SEC, and the importance of climate-linked local prices jointly show that LCoL is a hybrid metric at the intersection of controlled-environment agriculture and energy systems analysis.

The paper’s summary states that LCoL is built up from first principles—capital and operating costs annualized over a 20-year lifetime—using detailed cost inputs, dynamic energy modeling, and crop-growth simulation (Ceccanti et al., 24 Jul 2025). It further states that the dominant cost factors are grid electricity, driven by PPFD and HVACD loads, and CAPEX for lighting, while crop-growth parameters such as PPFD, CO$$\mathrm{CRF} = \frac{r_{\mathrm{real}}(1+r_{\mathrm{real}})^n}{(1+r_{\mathrm{real}})^n-1},$$4, and temperature shift the productivity denominator and thereby moderate those energy costs (Ceccanti et al., 24 Jul 2025). Climate and local cost structures then determine the final LCoL and carbon footprint, with significant variation across Norway, China, and the UAE (Ceccanti et al., 24 Jul 2025).

Taken together, these results define LCoL as a site-dependent and recipe-dependent metric rather than a universal benchmark. It is informative precisely because it compresses a coupled system—environmental control, plant response, infrastructure cost, and regional energy context—into a single normalized economic indicator, while still retaining interpretability through the underlying CAPEX, OPEX, SEC, and emissions components.