Economic Vulnerability Assessment Method and Transition Pathways for Plant Factories

doi:10.12133/j.smartag.SA202604003

Abstract

Abstract:

[Objective] Plant factories with artificial lighting (PFALs) provide year-round production, controllable environments, consistent product quality, and high space-use efficiency, positioning them as an important form of controlled-environment agriculture (CEA) moving toward intensification and digitalization. However, their commercialization has been constrained by high capital investment, electricity dependence, labor and operation-and-maintenance costs, and insufficient realization of market value. Existing studies have typically examined crop yield, light-environment control, energy use, capital cost, or market price in isolation, with limited integration of crop production, control maturity, energy conditions, and market realization into a computable framework. The aim is to identify the profitability boundary of PFAL lettuce, clarify how control maturity affects marketable yield, unit electricity use, labor substitution, annualized capital cost, and unit cost, and identify feasible transition pathways. [Methods] A production-side accounting boundary was adopted. Annualized capital cost, maintenance cost, electricity cost, labor cost, nutrient solution and seed costs, and other operating costs were included, whereas cold-chain logistics, retail terminal costs, brand advertising costs, financing costs, and complete channel-organization costs were excluded. Three levels of control maturity, three energy scenarios, and three market scenarios were specified, resulting in 27 deterministic scenarios. The model was constructed following the logic of "scenario input – control mapping – cost – benefit calculation – profitability boundary identification – vulnerability diagnosis". Control maturity was incorporated into the profit function through gross yield, marketable rate, unit electricity consumption per unit of marketable product, labor-substitution coefficient, and unit capital expenditure (CAPEX). An economic vulnerability index (EVI), consisting of profit gap, energy exposure, and carbon-constraint exposure, was further constructed. Local elasticity analysis, weight-robustness tests, and extended scenarios involving policy support and channel costs were used to examine the explanatory boundary of the results. [Results and Discussions] The economic feasibility of PFAL lettuce exhibited a distinct "narrow-window" characteristic. Among the 27 deterministic scenarios, only 5 achieved positive profit, accounting for 18.5%, and all were concentrated in the high-value direct-supply market. Under the benchmark scenario of "conventional grid electricity + high-value direct-supply market", upgrading control maturity from basic control to closed-loop intelligent control increased marketable yield from 70.40 to 109.25 kg/(m²·year), reduced unit electricity consumption from 12.0 to 8.4 kWh/kg, decreased unit cost from 27.67 to 19.26 CNY/kg, and increased profit from -258.36 to 518.04 CNY/(m²·year). Cost decomposition showed that although control upgrading increased annualized capital cost per unit area, higher output diluted capital cost per unit product. Meanwhile, improved labor substitution and reduced unit electricity consumption lowered labor cost and electricity cost, respectively. Break-even analysis indicated that higher control maturity flattened the break-even boundary between selling price and electricity price, reflecting lower sensitivity to electricity price fluctuations. The EVI results further showed that profitability ranking and vulnerability ranking were not fully consistent. The best scenario was "closed-loop intelligent control + energy-abundant condition + high-value direct-supply market", with an EVI of 0.088, whereas the worst scenario was "basic control + high-price and high-carbon electricity condition + conventional fresh-food market", with an EVI of 0.727. Local elasticity analysis showed that marketable yield had the largest effect on unit cost, with an elasticity of approximately -0.57, followed by unit CAPEX at approximately 0.49. The elasticities of electricity price and unit electricity consumption were both approximately 0.33. Sensitivity analysis of policy support and channel costs showed that investment subsidies and preferential electricity prices improved the financial performance of some boundary scenarios, whereas additional costs associated with packaging, fulfillment, channel maintenance, and sales organization compressed profit margins in high-value markets. [Conclusions] The feasibility of PFAL lettuce production is not determined by single-factor cost reduction, but by the joint effects of control maturity, energy conditions, market value realization, and channel costs. Conventional fresh-food markets and general premium-brand markets are unlikely to support profitable PFAL lettuce production. Only in high-value direct-supply markets, and when the control level reaches at least the enhanced-control stage, can the system cross the break-even line. The value of control upgrading should not be understood merely as electricity saving, but as a comprehensive mechanism that simultaneously increases marketable yield, improves the marketable rate, reduces unit electricity consumption, enhances labor substitution, and dilutes capital cost. A more robust transition pathway should therefore be built on the synergy among control upgrading, favorable electricity conditions, value-chain upgrading, and policy support. The conclusions of this study are applicable to production-side boundary identification under publicly available data conditions, but should not be directly interpreted as evidence of stable profitability for specific commercial projects.

Key words: plant factories with artificial lighting, lettuce, economic vulnerability, transformation path

CLC Number:

XIE Junhua, WANG Sen, YANG Qichang. Economic Vulnerability Assessment Method and Transition Pathways for Plant Factories[J]. Smart Agriculture, 2026, 8(3): 99-118.

Figures/Tables 16

Fig. 1

Table 1

Variable definitions and mapping relationships for economic vulnerability assessment of plant factories with artificial lighting

模块	核心变量	操作性定义	主要映射关系	植物工厂经济脆弱性诊断中的角色
生物学层	$B$ ， $M$	作物原型与栽培管理	$Y g = f B, M, C$ （1）	决定毛产量上限
生物学层	$α$ ， $Q$	商品率与品质一致性	$Y s = Y g α$ （2）	将产品转化为商品
数字-物理控制层	$C$	控制成熟度	$Y g$ 、 $α$ 、 $e s$ 、 $β$ 均为 $C$ 的函数	连接控制升级与经济结果
数字-物理控制层	$L$ ， $H$ ， $A$ ， $D$	补光、温湿调控、自动化和数字化	$e s = f L, H, C$ （3） $β = f A, D$ （4）	决定能耗与劳动替代
技术经济层	$I$ （元/m²）， $r$ （%）， $T$ （年）， $η m$ （%）	单位资本性支出（capital expenditure， CAPEX）、贴现率、寿命期、维护率	$A c a p = I ∙ C R F r, T$ （5） $C m = η m I$ （6）	构成资本年化压力
技术经济层	$p e$ 元/kWh， $w 1$ （元/（人·年））， $c n$ （元/kg）， $c o$ （元/kg）	电价、工资、耗材与其他运营成本	$C e$ ， $C l$ ， $C v$	构成OPEX主体
技术经济层	$P$ （元/kg）， $U C$ （元/kg）， $π a$ （元/（m²·年））	售价、单位成本、单位面积利润	$π a = R - C t o t$ （7） $P * = U C$ （8）	识别盈亏边界
脆弱性层	$G$ ， $S e$ ， $S c$	盈利缺口、能源暴露度、碳约束暴露度	$E V I = ω 1 G + ω 2 S e + ω 3 S c$ （9）	对情景稳健性排序
输出层	TP	转型路径类型	结果反馈	形成效率优先、能源耦合、价值升级和综合转型四类路径

Table 1

Table 2

Parameter settings and description for the economic vulnerability model of plant factories with artificial lighting lettuce

参数	符号	取值	来源/说明
基础控制下毛产量/（kg/（m²·年））	$Y 0$	80	介于元分析年化代理值（62.8）与先进商业值（115）之间
控制成熟度对毛产量提升系数	$η y$	0.437 5	使C=1时达到115 kg/（m²·年）
基础控制下商品率/%	$α 0$	0.88	闭环智能情景提升至 0.95
控制成熟度对商品率提升系数	$η α$	0.07	模型设定，使C=1时达到0.95
基础控制下单位商品电耗/（kWh/kg）	$e 0$	12.0	位于公开文献当前区间内
控制成熟度对单位商品电耗降低系数	$η e$	0.30	使C=1时 $e s = 8.4$ kWh/kg
基准单位CAPEX/（元/m²）	$I 0$	8 000	位于公开PFAL投资区间内部
控制升级导致CAPEX增加系数	$η I$	0.15	模型设定，反映自动化与控制升级成本
贴现率/%	$r$	5	沿用PFAL规模经济研究设定^［23］
寿命期/年	$T$	15	沿用PFAL规模经济研究设定^［23］
维护费率/%	$η m$	1.5	沿用PFAL规模经济研究设定^［23］
生产制造及有关人员平均工资/（元/（人·年））	$w 1$	78 561	国家统计局2024年数据
基础人工需求系数/（人·年/m²）	$η 0$	1/250	模型设定，人工成本核算
基础自动化替代率	$β 0$	0.10	模型设定，人工替代基准
控制成熟度对自动化提升系数	$η β$	0.35	使C=1时β=0.45
营养液、种子和卫生材料成本/（元/kg）	$c n$	1.6	模型设定
其他可变运营成本	$c o$	0.4元/kg	模型设定

Table 2

Table 3

Control-Energy-Market scenario combinations and value basis for PFAL lettuce

维度	符号	取值	含义	设定依据
控制成熟度	C0	0.0	基础控制	规则控制 $+$ 有限自动化
控制成熟度	C1	0.5	增强控制	传感、执行与调度增强
控制成熟度	C2	1.0	闭环智能	闭环调控 $+$ 较高自动化
能源情景	E1	$p e = 0.55$ 元/kWh $g e = 0.133 3$ kg CO₂/kWh	能源富裕型	低碳低价电力锚点
能源情景	E2	$p e = 0.75$ 元/kWh； $g e = 0.530 6$ kg CO₂/kWh	常规电网型	全国平均电网碳因子锚点
能源情景	E3	$p e = 0.95$ 元/kWh； $g e = 0.679 6$ kg CO₂/kWh	高价高碳型	不利能源条件
市场情景	M1	$P = 6$ 元/kg	常规鲜食市场	官方周均蔬菜批发价上沿
市场情景	M2	$P = 15$ 元/kg	品牌溢价市场	中等价格阈值情景
市场情景	M3	$P = 24$ 元/kg	高值直供市场	高价格阈值情景

Table 3

Table 4

Yield， cost， profit and carbon intensity under different control maturity levels in the E2+M3 Scenario

控制情景

毛产量 $Y g$

/（kg/（m²·年））

商品率

α

商品产量

Y s

/（kg/（m²·年））

单位商品电耗

e s

/（kWh/kg）

（元/kg）

单位成本 /

利润

π a

/（元/（m²·年））

碳强度

C I

/（kg CO₂/kg）

基础控制

80.0

0.880

70.40

12.0

27.67

-258.36

6.37

增强控制

97.5

0.915

89.21

10.2

22.94

94.83

5.41

闭环智能

115.0

0.950

109.25

8.4

19.26

518.04

4.46

Table 4

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 5

Table 6

Table 7

Appendix

Table 1 Robustness under ±10% perturbations of key mapping coefficients

扰动对象	扰动幅度	正利润情景数	最佳EVI情景	最差EVI情景	最高利润情景
$η y$	-10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η y$	10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η α$	-10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η α$	10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η e$	-10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η e$	10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η β$	-10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η β$	10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η I$	-10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3
$η I$	10%	5	C2-E1-M3	C0-E3-M1	C2-E1-M3

Appendix

Appendix

Table 2 Main computational results under 27 scenario combinations

情景	$Y s$	$e s$	$U C = P *$	$π$	$P e *$	$C I$	$G$	$S e$	$S c$	$E V I$
C0-E1-M1	70.4	12.0	25.27	-1 356.60	-1.06	1.60	0.763	0.261	0.068	0.364
C0-E1-M2	70.4	12.0	25.27	-723.00	-0.31	1.60	0.406	0.261	0.068	0.245
C0-E1-M3	70.4	12.0	25.27	-89.40	0.44	1.60	0.050	0.261	0.068	0.127
C0-E2-M1	70.4	12.0	27.67	-1 525.56	-1.06	6.37	0.783	0.325	0.746	0.618
C0-E2-M2	70.4	12.0	27.67	-891.96	-0.31	6.37	0.458	0.325	0.746	0.510
C0-E2-M3	70.4	12.0	27.67	-258.36	0.44	6.37	0.133	0.325	0.746	0.401
C0-E3-M1	70.4	12.0	30.07	-1 694.52	-1.06	8.16	0.800	0.379	1.000	0.727
C0-E3-M2	70.4	12.0	30.07	-1 060.92	-0.31	8.16	0.501	0.379	1.000	0.627
C0-E3-M3	70.4	12.0	30.07	-427.32	0.44	8.16	0.202	0.379	1.000	0.527
C1-E1-M1	89.21	10.2	20.9	-1 329.00	-0.91	1.36	0.713	0.268	0.034	0.338
C1-E1-M2	89.21	10.2	20.9	-526.09	-0.03	1.36	0.282	0.268	0.034	0.195
C1-E1-M3	89.21	10.2	20.9	276.82	0.85	1.36	0.00	0.268	0.034	0.101
C1-E2-M1	89.21	10.2	22.94	-1 511.00	-0.91	5.41	0.738	0.334	0.610	0.561
C1-E2-M2	89.21	10.2	22.94	-708.08	-0.03	5.41	0.346	0.334	0.610	0.43
C1-E2-M3	89.21	10.2	22.94	94.83	0.85	5.41	0.000	0.334	0.610	0.315
C1-E3-M1	89.21	10.2	24.98	-1 692.99	-0.91	6.93	0.760	0.388	0.826	0.658
C1-E3-M2	89.21	10.2	24.98	-890.08	-0.03	6.93	0.399	0.388	0.826	0.538
C1-E3-M3	89.21	10.2	24.98	-87.16	0.85	6.93	0.039	0.388	0.826	0.418
C2-E1-M1	109.25	8.4	17.58	-1 264.92	-0.83	1.12	0.659	0.263	0.000	0.307
C2-E1-M2	109.25	8.4	17.58	-281.67	0.24	1.12	0.147	0.263	0.000	0.136
C2-E1-M3	109.25	8.4	17.58	701.58	1.31	1.12	0.000	0.263	0.000	0.088
C2-E2-M1	109.25	8.4	19.26	-1 448.46	-0.83	4.46	0.688	0.327	0.474	0.497
C2-E2-M2	109.25	8.4	19.26	-465.21	0.24	4.46	0.221	0.327	0.474	0.341
C2-E2-M3	109.25	8.4	19.26	518.04	1.31	4.46	0.000	0.327	0.474	0.267
C2-E3-M1	109.25	8.4	20.94	-1 632.00	-0.83	5.71	0.713	0.381	0.652	0.582
C2-E3-M2	109.25	8.4	20.94	-648.75	0.24	5.71	0.284	0.381	0.652	0.439
C2-E3-M3	109.25	8.4	20.94	334.50	1.31	5.71	0.000	0.381	0.652	0.344

Appendix

Appendix Table 3

Robustness test of EVI weighting schemes

权重方案	权重设置（ $G$ ， $S e$ ， $S c$ ）	最佳情景	最差情景	前5重合数	后5重合数	Spearman $ρ$	Kendall $τ$
等权重	0.333， 0.333， 0.333	C2-E1-M3	C0-E3-M1	5	5	1.000	1.000
盈利缺口加权	0.500， 0.250， 0.250	C2-E1-M3	C0-E3-M1	4	4	0.944	0.829
能源暴露加权	0.250， 0.500， 0.250	C2-E1-M3	C0-E3-M1	5	5	0.999	0.989
碳约束加权	0.250， 0.250， 0.500	C2-E1-M3	C0-E3-M1	5	4	0.976	0.886
剔除 $G$	0.000， 0.500， 0.500	C2-E1-M1/M2/M3并列	C0-E3-M1	3	3	0.842	0.682
剔除 $S e$	0.500， 0.000， 0.500	C2-E1-M3	C0-E3-M1	5	5	0.991	0.943
剔除 $S c$	0.500， 0.500， 0.000	C2-E1-M3	C0-E3-M1	3	4	0.782	0.647

Appendix Table 3

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典型情景	结果特征	主导约束	对应路径	优先措施
增强控制+常规电网型+高值直供市场	利润已转正但边际有限，EVI中等	商品产量、资本强度	效率优先型	提高商品率、降低单位CAPEX、优化作业节拍
闭环智能+高价高碳型+高值直供市场	利润为正但能源暴露度和碳约束暴露度偏高	电价、电网碳因子	能源耦合型	分时电价响应、绿电采购、储能或源网荷储协同
闭环智能+任意能源+品牌/常规市场	技术成熟但售价不足以覆盖总成本	市场实现	价值升级型	品牌化、净菜化、活体供应、合同直供
基础控制+常规鲜食市场+常规/高价高碳电网	利润和EVI均处于不利区间	控制、能源、市场三重约束	综合转型型	同步升级控制、能源获取与销售模式

扩展情景	基础控制C0/（元/（m²·年））	增强控制C1/（元/（m²·年））	闭环智能C2/（元/（m²·年））
基准情景	-258.36	94.83	518.04
投资补贴20%	-104.21	260.54	695.31
电价优惠20%	-131.64	231.32	655.70
投资补贴20%+电价优惠20%	22.51	397.03	832.97
渠道成本增加1元/kg	-328.76	5.62	408.79
渠道成本增加2元/kg	-399.16	-83.60	299.54
渠道成本增加3元/kg	-469.56	-172.81	190.29
投资补贴20%+电价优惠20%+渠道成本增加1元/kg	-47.89	307.82	723.72

案例	公开资料显示的主要特征	对应模型路径	对本研究结论的启示
京东植物工厂	自有品牌“京觅”；线上京东生鲜与线下七鲜销售；冷链覆盖区域市场；年产量约300 t；强调安全、标准化和渠道能力	价值升级型；综合转型型	高值直供市场需要品牌、冷链、销售渠道和终端信任支撑；高价格情景不能只看售价，也要计入渠道成本
中科三安植物工厂	LED光谱、环境控制和智能装备；叶菜、药用植物、茄果类等多品类布局；规模化产业基地；生产周期缩短	效率优先型；能源耦合型	控制升级和规模化可提高单位面积产出，但同时带来资本强度和能源依赖，需与能源条件和高附加值作物组合协同