Contextual conditions define maximum energy-use threshold in low-carbon controlled environment agriculture for agri-food transformation

Domain: chemistry
Published: 2026-02-02
Editorial outcome: major_revision

Abstract
Controlled Environment Agriculture has the potential to achieve food security and lower carbon emissions in agri-food systems. However, contextual factors such as what is produced and how it is produced determine the feasibility of meeting these goals. Here we show how the use of a Maximum Energy-use Threshold, shaped by these contextual factors, can define, identify and enable low-carbon operations. Results support the potential of low-carbon controlled environment agriculture over international import when growing leafy greens in land-locked countries with low grid emission factors or when substituting air freight of short shelf-life produce. Prospective low-carbon energy scenarios helps but optimising energy use remains critical. As controlled environment agriculture allows intensive farming with a reduced land footprint, controlled environment agriculture of high energy use crops as a lower-carbon alternative can be supported when the potential for agricultural land substitution and restoration for environmental services is considered, along with other contextual condition.

Introduction
Controlled Environment Agriculture (CEA), which encompasses setups of varying degrees of automation, environmental control, and monitoring, holds many advantages over conventional open farming. Production scale and complexity of CEA can vary between different forms, from single storey vertical farms to multi-storey plant factories with fully standardised and automated processes. It allows for intensive farming on non-agricultural land with reduced pesticide and water use, while being less vulnerable to climate change. Against the backdrop of more extreme weather patterns1 and increasing competition for finite habitable land2, CEA is primed to meet the global challenge of feeding an increasing world population.

Despite this, the extensive adoption of CEA faces headwinds due to high upfront capital and operational costs. As a result, the trickle-down cost to be absorbed by consumers limits commercial ventures to leafy greens and higher-value produce3. Unsurprisingly, because of the heavy use of artificial lighting and environmental control, rising energy costs have slowed operators’ growth4. This intense energy use is the top carbon emission contributor of CEA5,6. It illustrates the current trade-off between energy, food security, and climate mitigation. With environmental merits being a key motivator for CEA adoption and perceived sustainability a main driver for consumer acceptance7, lowering key performance indicators (KPIs), such as annual crop energy use-productivity (EUP), kWh/kg, is vital. With CEA-specific policies and standards continuing to take shape8–10, KPIs that measure productivity and efficiency are important for benchmarking and disclosure purposes11.

Climate change is one of the most pressing planetary boundaries that have been transgressed12. To achieve a lower carbon food system and possibly net zero emissions by 205013, environmental assessment of CEA for different siting scenarios is essential14. Often, environmental impact analysis is obtained using life cycle assessment methodologies (LCAs)15 and resource use is benchmarked with KPIs16 (Fig. 1).

As different approaches to quantify sustainability, LCAs and KPIs are sensitive to contextual influences such as what produce is grown, how it is grown, where production is sited, and whether a low-carbon energy supply is available. Particularly, renewable energy availability has been shown to impact feasibility17 and heavily influences eventual environmental impact18. Therefore, detailed LCAs with each siting scenario presenting their unique set of local conditions will conclude differently on whether benefits of common CEA impact proposition to local food systems could be realised. Often, impact propositions include sustainability claims such as CEA’s potential to reduce or eliminate international transport emissions19 and the potential to restore substituted land back into carbon sequestering native vegetation20 as yield can be achieved on non-arable land, often in urbanised environment21,22.

In this work, we develop and calculate a Maximum Energy-use Threshold, or MET, per kilogram of crop, for different countries and different future scenarios. This helps us in identifying, at an aggregate level, favourable scenarios that have the potential to realise the climate mitigation potential of CEA. For CEA ventures operating within this measure of operational prudence, a more detailed LCA evaluating the full environmental impact should then be embarked on as a separate extended sustainability assessment. The MET calculation considers the surrounding context through localised trade data23, emission factors24 and different shared socio-economic pathways25,26. The calculated threshold is fundamentally a balance between the anticipated CEA carbon footprint solely from energy use and the avoided carbon footprint implicit in CEA impact propositions (Fig. 2).

For CEA with estimates lower than this MET, they represent favourable siting scenarios that could accomplish a lower carbon food system if the impact propositions are achieved. LCA should be performed as a separate extension, which can then include emissions from other agriculture-related activities. CEA ventures with EUP higher than this threshold will negate any potential CO2 emission reduction obtained by replacing an existing element within the local food system, even without performing an LCA.

Results
First, for present circumstances considering international import substitution, growing leafy vegetables or short shelf-life fruits using CEA in land-locked, low-grid-emission nations are a few scenarios potentially presenting a favourable energy-food-climate trade-off.

Figure 3 shows the calculated MET for different countries based on their current grid emissions and trade patterns. The nations analysed here import selected produce of interest and report to FAO23. These crops – viable in both semi-closed greenhouse setups and indoor farms with artificial lightings – include commercially favourable leafy greens, vine vegetables, and fruits27. High calorific value crops, such as wheat28,29 and soybean30, are analysed as well because they are extremely important as staple foods.

A large number of the countries have a low MET as a result of either a low-carbon emission from existing imports or high carbon emissions from existing electricity supply. This means that a CEA operation has to be extremely efficient in energy use, per kilogram of crop, to be less carbon intensive than current import. The average EUP for different crops and forms of CEA was extracted from literatures to visualise how current CEA compares to the METs. Countries with MET above these markers denote a favourable scenario for CEA implementation today, given the current operational state of CEA (GH – Greenhouse, or IF – Indoor Farming).

The estimates of indoor farming configurations exceed the MET allowable in growing the analysed produce for almost all countries. The only exceptions to this are a few scenarios for growing lettuce. This signifies that indoor farming, at its current development, will likely not enable a lower carbon food system as compared to current food imports. However, in the case of strawberries, where the produce is assumed to be air freighted due to short shelf life, possible aversion of higher emissions from air transport will allow higher MET of strawberries or other air-freighted produce in CEA.

In this analysis, countries such as Ethiopia, Congo, and the Democratic Republic of Congo are outliers and favourable siting locations for siting indoor farming because of their exceptionally low grid emissions from the high utilisation of hydropower and reliance on traditional biomass (charcoal, crop waste, and other organic waste) as primary energy source31,32. For Paraguay, the cost of being land-locked33 and reliance on distant trade partners position them as favourable for siting indoor farming as well. Resonating with the results here, another study where indoor vertical farming feasibility scores are synthesized from development indicators, Ethiopia scored favourably to host indoor vertical farming in macro indicators such as food security, climate change vulnerability, infrastructure, and others34. Though externalities such as grid stability and capacity can be a concern in Ethiopia, siting CEA can be possible as energy-intensive technology infrastructure like data centres already exists and provides a template for planning high energy use, high cooling load ventures35.

Delving deeper into the literatures that informed the current average energy use of GH and IF here, the energy use estimates of individual literature are presented in Fig. 4.

Interestingly, growing lettuce in a semi-closed greenhouse at locations with very cold climates, such as Minnesota (USA) and Washington (USA), can perform worse than fully indoor farms due to inadequate solar irradiation, high heat loss, and consequently, high heating requirements49. To evaluate the climate mitigation potential of CEA in a particular country, an “Energy Prudence Index” is suggested here. A positive value in the Energy Prudence Index signifies a higher energy use than the MET, and a negative value in the index signifies a lower energy use than the MET.

Based on literature, almost all of the current CEA setups operate at EUP several multiples of the MET. Only in setups where extremely low energy is used and where short shelf life produce such as strawberries are air freighted, their operations hold potential to be a lower carbon option. For example, utilising a hydraulic rotating greenhouse setup in Singapore for CEA of lettuce can be achieved at an EUP of 0.021 kWh/kg40, lower than the MET (Supplementary Data 1). As a land-scarce nation intensifying agri-food industry investments to bolster food security50, it will be beneficial for Singapore to invest in low-energy greenhouses for lettuce cultivation.

Next, when considering prospective, low-carbon energy scenarios, the need for operational prudence as reflected in the MET relaxes but do not negate the need to further optimise CEA efficiency.

As solar power gains momentum to be the dominant form of renewable energy globally51, and energy use emissions can be reduced through other forms of renewable energy, the MET was investigated under two main prospective energy scenarios here: (1) CEA facilities fully powered by current 3kWp photovoltaic panel technology or, (2) with the projected regional grid emissions factor for 2050 as electricity grids incorporate more renewable energy.

For land-locked countries, such as Paraguay, that possessed some of the most ideal energy grid and trade conditions for siting CEA today, the transition to solar energy presents a more stringent operating condition, as reflected in a much lower MET. This is due to photovoltaic technologies possessing a higher emission factor than hydropower (Supplementary Fig. 1). On the other hand, many countries with national grids of higher emission factors can benefit from higher photovoltaic utilisation, as reflected in the widening of the interquartile range and higher global median (Fig. 5a).

In addition to using photovoltaic panels, there are many pathways to achieving a lower emission factor energy supply. As nations pledge to transition to more renewables and towards a lower radiative forcing level in 205052, a myriad of possible grid emission factors are possible. Here, the scenario “SSP2-Base” was assumed, where the shared socio-economic pathway (SSP) was extrapolated from historical patterns and possesses a base emission target causing a change in energy balance equivalent to 6.0 W/m2 25,53. This positioned countries differently from earlier results as outliers favourable for CEA (Fig. 5b). It is important to note that while this approach26 allows estimated emission factors to be better aligned with outputs from climate change integrated assessment models (IAMs), several nations in the adjusted life cycle inventory database can inherit higher grid emission factors as a result of geographical aggregation in regional outputs. As an example, for countries already possessing low grid emissions, such as Paraguay, higher emission factors from the Latin American region in REMIND IAM were inherited54. As a result, the respective MET was lowered, reflecting a more stringent operational requirement. Hence, for these countries, the present is already the best-case scenario. Energy projection scenarios in 2050 can be refined with greater granularity by downscaling IAM results for a better estimate for the individual countries55. The METs considering alternative SSPs and IAMs are appended (Supplementary Fig. 2). To better illustrate the effect of different SSP, a subset of this comparison pertaining to CEA of lettuce is shown in Fig. 5c. Among the alternative SSP scenarios analysed, “SSP2” presented a more conservative MET as compared to other SSP considering different levels of barriers to mitigation and adaptation.

Likewise, as more stringent emission targets are simulated, the prospective grid emission factor reduces and relaxes CEA operation requirements through a higher MET. IMAGE IAM is included here for sensitivity analysis56.

Multiple scenarios of CEA configurations operating in different SSP could contribute to both food security and sustainability objectives. For example, plant factories operating in Massachusetts or Maricopa County could be viable if global climate initiatives limit global warming below 1.5°C as the world “takes the middle road” (SSP2) or Nationally Determined Contribution (NDC) targets are achieved as the world “takes the green road” (SSP1). For other selected countries that have interest in CEA implementation, the METs across different prospective scenarios are appended (Supplementary Fig. 3).

To compare EUP variance of different CEA configurations (plant factories and greenhouses) operating in different climate conditions for selected regions of interest, findings from a previous study were extracted57 (Supplementary Fig. 4). Indoor farming configurations have higher EUP requirements but show less variance in different climate conditions.

Beyond leafy crops, cultivating higher calorific value crops such as wheat and soybean under current or prospective energy scenarios will not achieve a lower carbon food system as compared to importing. However, there is an additional dimension to conventional agriculture in which CEA provides plausible environmental benefits. Cereal crops require more land than other crops. Growing cereals such as wheat requires more than four times the arable land than tomatoes58. For these crops, the potential restoration of land previously used for agriculture59 or preventing further land use change through CEA can avail originally arable land for climate mitigation purposes60.

When considering such scenarios, CEA of cereal crops can possess climate mitigation potential in a few countries. Just as carbon emission occurs because of direct land use change, carbon sequestration can occur if agricultural land is reverted for native vegetation growth61. Here, a carbon opportunity cost is valued to account for this opportunity of carbon sequestration on land currently occupied to produce wheat and soybean20. The countries analysed are wheat and soybean producing nations as reported to FAO23. The result in Fig. 6 shows the MET of wheat and soybean CEA, if the CEA production is able to substitute agricultural land outputs and that land can now be restored to provide environmental services.

The current average energy use of wheat and soybean CEA is above the maximum threshold for almost all countries, signifying that CEA in its current development will not achieve a key sustainability objective of climate mitigation if such high calorific value crops are grown; However, there are a few outlier countries presenting unique scenarios where CEA could mitigate this trade-off. Tropical countries such as the Democratic Republic of Congo, Honduras, and Colombia possess some of the highest net primary productivity of natural vegetation and lowest grid emissions. They stand to be favourable CEA locations for wheat (Fig. 6a), while countries such as Panama could benefit from CEA of soybean (Fig. 6b).

Extending the technical lifespan of photovoltaic panels is critical, as it places lesser demand on virgin material and produces lesser waste62. Hence, improving the reliability and durability of photovoltaic panels towards a 50-year technical lifespan module is essential63,64. Here, the emission of a photovoltaic panel kept in operation for 50 years was estimated to reflect this technological outlook on photovoltaic panel development. To calculate the emission factor of a 50-year technical lifespan solar panel, the existing life cycle inventory of a 30-year technical lifespan solar panel was extended to continue at a degrading annual yield for another 20 years. Because of the prolonged lifespan, the total yield increased, and its emission factor (kg CO2 eq./kWh) was reduced. This further relaxed the MET as compared to calculations using present day emission factor.

While this article focused on contextual factors, the conclusions will also be influenced by methods for carbon accounting and carbon opportunity cost accounting. In the above calculation, MET is calculated on the accounting basis of “territorial” emission, where the carbon opportunity costs were only attributed to the producing nation. However, as nations do not consume the total production and can import in addition to what is produced, carbon opportunity costs can also be adjusted for trade. To this end, MET calculated on the basis of consumption across different prospective energy scenarios is presented in Supplementary Fig. 5. In the carbon opportunity cost accounting method, in which spared agricultural land is assumed to be restored, a historical accounting perspective can also be taken, where carbon loss from agricultural land expansion occurring between Year 2000 and 2010 was used (Supplementary Fig. 6). From both accounts, outliers can present much more relaxed MET requirements.

Methods
Estimation of Transport Emissions
Agri-food supply chains are heterogenous and can change due to a myriad of factors85, and the associated transport emissions varies due to transport options, year of assessment and surrounding geographical condition. To calculate the Maximum Energy-use Threshold, emissions arising from importing different crops was first calculated.

First, in a given year, every pair of reporting import nation and the partnering export nation were identified with their respective trading amount. This was extracted from the detailed trade matrix published by Food and Agricultural Organisation (FAO), between a time period of 2012 to 202223. Next, the produce were assumed to be transported between the capital of these trading pairs. Bilateral sea distance between the capitals of reporting and partner nations was obtained from publicly available CERDI sea distance database86, while the air freight distances were calculated with geodesic between obtained airport coordinates from public domain87. This included road distances from their capital to respective nearest ports, airports. For trading pairs where both countries are land-locked – without access to ports and directly accessible to each other over land – the shortest road distance between capitals was used instead86. The resulting measure of freight transport, ton.km, for associated land and maritime transport was calculated by multiplying the traded amount and respective transportation distances (Eq. 1).

FreightTransporta→b,crop (tons.km) = 

> Mimport × (d a,cap→a,port + d a,port→b,port + d b,port→b,cap)

> Mimport × d a,cap→b,cap , if both countries are land-locked

(1)

Subsequently, to calculate the carbon emissions, CE a→b , attributed to that trading pair (kg. CO2 eq.), the measure of freight transport, ton.km, for both land, maritime and air freight was multiplied with the emission factors (kg. CO2 eq. per ton.km) associated with the respective mode of transport24.

CE a→b,crop (kgCO2 eq.) = 

> Mimport × (d a,cap→a,port × ef land,crop + d a,port→b,port × ef sea,crop + d b,port→b,cap × ef land,crop)

> Mimport × d a,cap→b,cap × ef land,crop , if both countries are land-locked

(2)

For lettuce, tomato, and strawberry, refrigeration were assumed to be required during freight transportation while wheat and soybean were assumed to be dry goods (Table 1).

Because a single importing nation can have multiple trading partners supplying a produce in a given year, carbon emissions from all associated trading pairs are summed (kg. CO2 eq.). The total imported amount of produce from all associated trading pairs are summed as well (kg) and used to reciprocate total carbon emissions.

CEI = Σn=Partners,year CE a→n,crop (kgCO2 eq.) / Σn=Partners,year import a→n,crop (kg)

(3)

This resulted in the weighted average of carbon emissions per kilogram of imported produce (kg. CO2 eq. per kg produce) for a particular year. This is iterated over all reporting, import nation for all the analysed crops. Because import quantity and partner changes due to a myriad of reasons, the calculation of this weighted average iterated each year, for a time period between 2012 and 2022. To have a representative value over these ten years, the median was taken. To derive the Maximum Energy-use Threshold (kWh per kg), the median carbon emissions per kilogram of imported produce was divided by the current and prospective electricity emission factor (kg. CO2 eq. per kWh).

Due to the unavailability of actual shipping and air freight data, validation of sea freight distance and air freight distance was not feasible here. However, the shortest straight-line approach using a geodesic here is consistent with the shortest straight-line approach using a Great Circle Distance by the International Civil Aviation Organisation in its freight carbon emission calculator88.

Here, it was assumed that even as total produce demand and supply chains can change over time in prospective scenarios, such changes do not cause material change from the calculated value.

Prospective Energy Scenario: Increasing technical lifespan of photovoltaic panels
To estimate the emission factor of a 50-year technical lifespan photovoltaic panel, the life cycle inventory of “Electricity, low voltage {RoW}| electricity production, photovoltaic, 3 kWp flat-roof installation, single-Si| Cut-off, U” was adjusted. In the original inventory, the carbon emission of producing 1 kWh was due to the production of photovoltaic panel. To attribute the emissions to 1 kWh of generated power, the total emissions was reciprocated by the total produced power of photovoltaic panel in its lifetime. There, the panel was assumed to have a 30-year lifetime of 3 kWp capacity and produce power at an average annual yield of 1099 kWh/kWp24.

Here, to simulate a 50-year technical lifespan, it was assumed that following a 30-year lifespan with an average annual yield of 1099 kWh/kWp, continuing use of the same solar panel is projected with a yield degradation rate of 0.5%/year for mono-crystalline silicon for another 20 years89. The emissions attributed per kWh is calculated as the reciprocal of the total yield over its 50 years technical lifespan. The total yield over its 50 years technical lifespan was calculated as:

(30years * 3kWp * 1099kWh * 0.92 + Σn=1→20 (1099 * (0.995)n * 3 * 0.92)) = 148575.72 kWh

(4)

Referencing a study where the actual yield was lower than the report IEA data90, a correction factor of 0.92 was adopted by Ecoinvent24 and used here as well. The resulting emission factor, 0.0517 kg CO2 eq. per kWh, was calculated using the IPCC 2021 GWP100 method on LCA software, SimaPro.

The percentage reduction in emission factor because of extended life cycle is similar to another LCA where End-of-Life scenarios of Photovoltaic Panels in Australia are conducted91. Here, the emission factor is higher than the reported 0.035 kg CO2 eq. per kWh for a 50-year lifespan panel in Australia due to the life cycle inventory of the “Rest of World” region. In the “Rest of World” region, the inventories include contributions from more carbon-emitting countries.

Prospective Energy Scenario: Projected Grid Emissions of REMIND SSP2 Base Scenario
To estimate the projected grid emission factor of different countries, the methodology of premise was replicated to modify the Ecoinvent 3.9.1 database26. Projections in 2050 were taken to follow a “Middle of the road” shared socioeconomic pathway, SSP2-Base, where trends do not change extensively from historical patterns25, and emissions continue to grow at the same rate as the past. With these narratives, Integrated Assessment Models (IAM) IMAGE and REMIND projects what is the composition and efficiency of electrical grids in 2050. The premise open-source code was implemented, in which efficiencies of power generating technologies were updated, new life cycle inventories were created, and regional electricity grids were aligned to IAM geographical definitions. The resulting, regional emission factors were used for all countries aggregated under it. Here, while IMAGE offers a relatively finer geographical aggregation, REMIND outputs was used due to a larger emphasis on photovoltaic energy, with lesser emphasis on hydropower and nuclear power.

Quantifying the carbon opportunity cost of continual agricultural land use
To estimate the carbon opportunity cost incurred by continual agricultural land use, the “carbon gain” method proposed by Searchinger et al. was adopted for wheat yielding and soy yielding land20.

First, to identify global crop-yielding land, SPAM2010 cropland raster data was used92. The open-source software, rasterio was used to open and reproject all other raster data downloads to cropland raster data93. To obtain the wheat or soy yielding land of only selected countries, cultural vector data were downloaded from public domain, Natural Earth94, and geometrical information about national boundaries were extracted using open software, geopandas95, to clip cropland raster data. This was applied to net primary productivity of native vegetation (NPP nat per hectare) raster data and carbon stocks of potential natural vegetation (ton C per hectare) raster data20, as well as terrestrial biome raster data96.

In the “carbon gain” approach, to calculate the carbon sequestration by native vegetation of potentially, restored agricultural land; considering every cell/area in each producing country was divided by their respective average yield, resulting in a NPP nat per crop yield for each country. Referencing the calculation done by Searchinger et al., 0.5 tonne C per hectare per year of carbon sequestration was assumed to be generated for every one tonne of NPP nat available. For comparison, as suggested by Searchinger et al., 0.42 tonne C per hectare per year of carbon sequestration could be assumed for the tropics by dividing the average NPP nat of tropical croplands by the average carbon flux in tropics over 100 years. To convert kg C/kg crop yield to kg CO2/kg crop yield, the result was multiplied by a factor of 3.67, which represents the molecular weight ratio of carbon dioxide to carbon.

Carbon Sequestration = (Σ NPP nat (ton C) / Yield (ton) * Area (ha)) * 0.5 ton C * 44/12 * 10^3 (kg CO2 eq.)

(5)

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Controlled Environment Agriculture (CEA) production systems represent the most intensive form of food production. Thanks\nto their controlled and protected conditions, they offer a promising solution for food production, particularly in the face of\nclimate change, water scarcity, and regions with degraded soils (soilless systems). However, these systems require\nsignificant energy and resource inputs, which could pose a sustainability challenge.\nI found the concept, application, and analysis of the energy use-productivity threshold (MET) as a key performance indicator\nfor CEA systems particularly interesting. This approach provides a valuable framework for assessing the sustainability of\nCEA systems in terms of energy and resource efficiency. The manuscript is well-written and well-structured. Attached to the\nrevised/annotated PDF are some comments that may help enhance its quality and clarify certain points", "reviewer_2": "First, thanks to the authors and the editor for the opportunity to review this manuscript. It was interesting and presented a\nnovel way of thinking about spatio-temporal optimization for CEA. As I understand it, the manuscript aims to explore the\ntradeoffs between CEA energy use (and associated carbon footprint) and the limitations of traditional ag, including long-\ndistance travel and expanding land use. Overall, I think this is a useful study for the Nature Comms audience, since it\nencourages folks to think spatially about this particular environmental challenge, and it offers general guidance on the types\nof situations that might justify extensive investment in CEA.\nThat said, I do have some comments that I think are important to address before the manuscript is ready to see publication.\nOverall, I think the article overstates the conclusions that can be drawn from these methods. This is not a test of low-carbon\nCEA – that would demand an LCA or other methods that includes inputs and infrastructure. I also think the human\ndimensions of the proposed interventions are largely overlooked and should be considered with more care. Finally, I think the\nfigures and phrasing of some of the methods are difficult to interpret, and the authors should endeavor to make clearer\nconnections between the actual numbers they calculated and the conclusions they draw. I would be happy to re-review it\nonce these changes have been made. I list specific versions of these comments in no particular order for consideration:\n1. “The calculated threshold is fundamentally a balance between anticipated CEA carbon footprint solely from energy use\nand avoided carbon footprint implicit in CEA impact propositions (Fig. 1b).” - I am concerned about the framing of CEA as\nenvironmentally friendly prior to any LCA work. As the authors themselves point out, there are a number of infrastructural\ninvestments that must be amortized as carbon per kilogram product – and other recent work in this family of journals has\nshown that infrastructure is perhaps the most important factor in LCA of urban ag (Hawes et al., 2024). There were similar\nfindings in other, older CEA work (Goldstein et al., 2017). It might be more accurate to say that the operation of CEA could\nmitigate tradeoffs under confluent conditions, but I don’t think that the authors demonstrate clearly that CEA is low-carbon\neven if its operation is. This is ultimately a phrasing and framing issue, I certainly don’t expect the authors to conduct a full-\nblown LCA to address this.\nHawes, Jason K., Benjamin P. Goldstein, Joshua P. Newell, Erica Dorr, Silvio Caputo, Runrid Fox-Kämper, Baptiste Grard,\net al. “Comparing the Carbon Footprints of Urban and Conventional Agriculture.” Nature Cities, January 22, 2024, 1–10.\nhttps://doi.org/10.1038/s44284-023-00023-3.\nGoldstein, Benjamin, Michael Hauschild, John Fernández, and Morten Birkved. “Testing the Environmental Performance of\nUrban Agriculture as a Food Supply in Northern Climates.” Journal of Cleaner Production 135 (November 2016): 984–94.\nhttps://doi.org/10.1016/j.jclepro.2016.07.004.\n2. The manuscript should be edited once more to remove odd words or grammatical tricks here and there, like an extra “be”\nat line 77. “Transiting” at line 156. Sentence at 247. Etc.\n3. In general, the figures could use some work. Aesthetically, they are quite nice, and they communicate a simple message\nquite well. But they fail to communicate the key message of the results section, which is where and under what\ncircumstances the EUP exceeds or is below the MET. I don’t know that I think the current figures need removed or changed,\nbut an additional panel that shows the EUP divided by MET relative to 1 might be helpful.\n4. At line 135, there is discussion of the low carbon impacts of electricity in certain developing countries. I have to wonder\nabout the externalities associated with siting high energy-use crop growing systems in places where the grid may be\nunstable or unprepared for a new, large user. While I can appreciate the idea, it seems a bit short-sighted to me.\n5. Related to this, I do not understand this statement – why is Ethiopia better than the others named? -- “Resonating with a\nprevious study that assessed macro indicators for indoor vertical farming’s feasibility and sustainability in Africa, Ethiopia\nstands out as the African nation suitable for CEA.”\n6. I am very unclear on what type of food is being offset in the agricultural land substitution scenario. Is this local food\nproduction replaced by CEA? Or imported food? If imported food, the explanation doesn’t seem to make sense to me. The\nconclusion seems to indicate that this section is focused on how exporting countries might reduce their footprint – this is\ninteresting and perhaps more internally consistent, but I have some concerns as discussed in my next point.\n7. Overall, I remain a bit unconvinced of the idea that CEA inherently reduces the land use for agriculture in other locations.\nThese sorts of telecoupling effects are unpredictable at best, and things like local labor markets are unlikely to simply vanish\nif the demand from a particular export partner is reduced. For that matter, the authors still need to talk about the in-migration\nto cities that would be required, the upskilling of agricultural labor, etc. If CEA requires less employees per kg of produce, it\nseems unlikely that the land use transition would be dramatic, since the original farmers still need a source of income.\n8. The authors talk about an expanded technical lifespan for solar, but I don’t think they ever say what 50 years is an\nalternative to. What is the technical lifespan in EcoInvent? This should be in the main text (I see it in the methods, but this is a\nNature Comms piece, not all readers will keep going to find the methods).\n9. I’m a bit confused in the methods section with Eqn. 1. If the country is land-locked, all food is assumed to be ground-\ntransported? This doesn’t make sense for, for example, import of bananas from Colombia to Switzerland. Some of that\ndistance is, of course, by sea.\n10. I appreciate that the authors try to differentiate between cooled lorry use and dry goods, but does Table 1 in the methods\nsuggest that the carbon footprint of a refrigerated lorry is lower than that of an unspecified lorry? This might indicate that the\n“unspecified” option includes some combination of a variety of lorries, which wouldn’t really be achieving the authors’ goals.\n11. The authors should be more specific about the programming languages used."}, "author_response": "General Response To Both Reviewers\nWe would like to thank both reviewers for their valuable comments and for\nproviding us an opportunity to enhance the clarity of this work.\nFirst, we are pleased that the work resonated with Reviewer 1 as a valuable\nframework for assessing CEA sustainability. Indeed, we hope this work informs the\naudience of a complementary way to think about evaluating CEA sustainability. The\namended manuscript benefited greatly from Reviewer 1’s comments to enhance on its\nclarity, particularly in the first results and discussion section.\nNext, we thank Reviewer 2 for their valuable feedback and for giving many\ncomments that has significantly improved the clarity and positioning of the paper. The\nreviewer has rightly distilled the spatial-temporal perspective that CEA innovators and\npolicymakers should approach with, and that which we are communicating here. Without\nsuch a nuanced perspective to CEA, one might be quick to dismiss or overstate the\npotential in which it brings to transform the agrifood supply chain.\nFor ease of perusal, specific responses to reviewer 1 comments can be found\nfrom HERE, and specific responses to reviewer 2 comments can be found from HERE.\nWe thank both reviewers again for their insightful comments and welcome any\nfurther clarifications.\nPoint-by-point response to the reviewers’ comments\nResponse to comments by Reviewer 1\n Comment:\nPage 1, Line 14 – Amend “the feasibility to meet these goals” to “the feasibility of\nmeeting these goals”\nResponse:\nThank you, done.\n Comment:\nPage 1, Line 43 – Amend “efficiency” to “efficient”\nResponse:\nIn the original manuscript, the author used the wording to refer to the type of KPIs\ninstead of the nature of the KPIs. To avoid confusion, the revised manuscript now reads:\n“With CEA-specific policies and standards continuing to take shape, KPIs that measure\nproductivity and efficiency are important for benchmarking and disclosure purposes”\n Comments:\nPage 2, Figure 1, Panel A: “how different are vertical farms with artificial lights and\nplant factories with artificial light? Please specify the different components that make\nthese CEA systems differ”\nResponse:\nThough indoor vertical farms and plant factories are both closed environments\nwith artificial lighting and environmental control, a subtle difference lies in the scale and\nsystem complexity. One of the ways to depict the difference in scale and complexity, as\nillustrated in Figure 1, is that plant factories often refer to facilities with multiple storeys\nof growing area, and with high level of automation and standardised process such as the\nuse of conveyors and robotics. Currently, there is yet a formal classification that\ndifferentiates vertical farm from plant factories in terms of components in these two\nsystems; also, the use of certain technology are rarely exclusive between the two forms\nof CEA. We do agree that this subtle difference is hard to pick up from the illustration. We\nadded this clarification as the second line of the introduction right as we introduce CEA.\nIt reads as:\n“Production scale and complexity can vary between different forms, from single storey\nvertical farms to multi-storeys plant factories with fully standardised and automated\nprocess.“\n Comment:\nPage 2, Figure 1, Panel B: “in Figure 1b, I believe the first part of the graph is\nunnecessary since you present the impacting conditions and their relationship in the\nsecond part of the graph. The first part is a little confusing”\nResponse:\nWe thank the reviewer for pointing out the potential difficulty in following the train\nof thought in the illustration.\nThe first part of the illustration shows the underlying common environmental impact\nproposition of CEA and how that benefit can be quantified in terms of global warming\npotential. The second part then rearranged the necessary balance between a CEA facility\noperation and the quantifiable benefits, in order for those propositions to be true. Hence,\nit shows the train of thought deriving the MET calculation. We considered the reviewer’s\nconcern and opted to keep the first part of the figure for the reason above.\nWe do agree that there is an opportunity for clarification within the Figure caption such\nthat readers can understand the whole thought process behind the reason behind the\nequation. We added the following explanation in the caption for Figure 1(b):\n“Common CEA impact proposition and the respective quantifiable climate mitigation\npotential are shown in purple and beige. Potential global warming potential benefits from\ncommon CEA impact proposition can be respectively quantified as the first step. The MET\nis a measure of operational prudence calculated by dividing the quantifiable climate\nmitigation potential with the respective electricity emission factor.”\n Comment:\nPage 3, Line 63 – Amend “influence” to “influences”\nResponse:\nThank you, done.\n Comment:\nPage 3, Line 71 – Amend “use-productivity” to “.”\nResponse:\nWe thank the reviewer for pointing out the need to maintain better consistency in\nthe terminology suggested and its abbreviation. We have amended the MET as the\nabbreviation for Maximum Energy-use Threshold, instead of Maximum Energy use-\nproductivity Threshold. The application of this threshold on a basis of per kilogram of crop\nwas highlighted in throughout the text.\n Comment:\nPage 3, Line 88: “In terms of paper writing and presentation of scientific conducted\nwork it would be preferable to include a separate chapter concerning the methodology\nthat was followed for data collection. the system boundaries of your research, the data\ncollection and the examined scenarios, possible hypotheses and cases that were applied”\nResponse:\nWe thank the reviewer for highlighting the need to clarify on the methodology\nwithin the writing.\nWe have detailed our methods separately in the “Methods” section of the article. We\nbelieve this suggestion might be linked to the reviewer’s later comment at Page 8, Line\n285 where the reviewer commented: “why method is after the Conclusions? It should be\nunder the Introduction and before Results”.\nHence, we believe this suggestion might have been raised due to the order of reading the\nseparate chapters and does not necessitate a new methodology chapter. With regards to\nthe order “Methods” being after “Conclusions”, we have chosen to keep the article format\nconsistent with Nature Communications Section order.\nWe welcome further clarification on this suggestion, if we have misunderstood the\nsuggested amendments required.\n Comment:\nPage 3, Line 92 to Line 103 – “all these information on data selection and case\nscenario development belong to a Materials and Method chapter”\nResponse:\nWe thank the reviewer for suggesting the highlighted paragraph to be reordered for\nbetter structure and reading flow to the audience.\nThe original intent of the paragraph was to inform the audience the selection of crops in\nour results early and why we did so. We can understand the reviewer’s perspective on\nhow the paragraph can potentially be distracting before the results and figures were\nintroduced and hence could be shifted to the materials and methods chapter.\nWe have now repositioned the explanation after introducing the first result so that it\nexplains the selection of crop within the figure. We have also swapped the order of Panel\nA and B, such that the audience’s first focus will be on our new results, and the relevance\nof the result in context of current energy use performance from literature will come after.\n Comment:\nPage 4, Figure 2, Panel B – “the MET calculated in this figure is under which CEA\nsystem? MET can significantly vary depending on the equipment used for production, the\nproductivity rate etc. Is it uniform for every crop species presented? please specify.”\nResponse:\nWe thank the reviewer for the clarification and agree that the relevance for MET to\nwhich form of CEA can be better clarified.\nBecause the MET is calculated based on external contextual factors such as emissions\nfrom current supply chain (the incumbent option) and local electricity grid, the\napplicability of the MET is not constrained to a specific form of CEA and does not vary\ndepending on the equipment used.\nWe have added the above explanation, verbatim, in the introduction of MET on Page 3, 2nd\nParagraph.\nOn a practical level, the different forms of CEA will find the challenge of meeting the target\ndifferently. A greenhouse operator will find meeting the MET to be easier than a plant\nfactory operator. The MET being technology agnostic is an attractive proposition to policy\nmakers because they form a minimum basis for CEA industry to rally towards.\nWe have added the above explanation, verbatim, in the final paragraph of Discussion,\nPage 10, last Paragraph.\nThe MET does vary for different crop because the existing trade options will change, and\nhence we presented separate MET for separate crops.\n Comment:\nPage 5, Line 162 – “please explain the abbreviation”\nResponse:\nWe thank for reviewer for highlighting the importance of explaining the\nabbreviation, “SSP2-Base”.\nWe agree and have done so in the original sentence containing that abbreviation. The\noriginal sentence is: “Here, with a shared socioeconomic pathway (SSP) extrapolated\nfrom historical patterns and a target equivalent to 6.0 W/m2, the scenario “SSP2-Base”\nwas assumed”.\nWe understand that the phrasing of the explanation can be improved and have\nrestructured it to:\n“Here, the scenario “SSP2-Base” was assumed, where the shared socioeconomic\npathway (SSP) was extrapolated from historical patterns and possesses a base emission\ntarget causing a change in energy balance equivalent to 6.0 W/m2”.\nThe explanation and reference to SSP2-Base was reiterated again in the Methods chapter.\n Comment:\nPage 7, Line 236 – “Add citation please” for the term FAO reporting.\nResponse:\nConsistent with prior section where a citation was added to reference the FAO\ndatabase where reporting nations importing other produce can be found, we have added\nthe same citation here to point towards the right resource.\nWe thank the reviewer for the keen observation.\n Comment:\nPage 8, Line 270 – Amend “all that is produced” to “the total production” .\nResponse:\nThank you, done.\n Comment:\nPage 8, Line 285 - ”why method is after the Conclusions? It should be under the\nIntroduction and before Results”\nResponse:\nAs explained in response to reviewer’s previous comment at Pg 3, line 88, we have\npositioned the “Methods” section after “Conclusion” to be consistent with Nature\nCommunications article format.\n Comment:\nPage 9, Line 302 - ”please number the equations”\nResponse:\nWe thank the reviewer for the keen observation. We have numbered all the\nequations here.\nResponse to comments by Reviewer 2\n Introductory Comment:\nFirst, thanks to the authors and the editor for the opportunity to review this\nmanuscript. It was interesting and presented a novel way of thinking about spatio-\ntemporal optimization for CEA. As I understand it, the manuscript aims to explore the\ntradeoffs between CEA energy use (and associated carbon footprint) and the limitations\nof traditional ag, including long-distance travel and expanding land use.\nOverall, I think this is a useful study for the Nature Comms audience, since it encourages\nfolks to think spatially about this particular environmental challenge, and it offers general\nguidance on the types of situations that might justify extensive investment in CEA. “\n“That said, I do have some comments that I think are important to address before the\nmanuscript is ready to see publication. Overall, I think the article overstates the\nconclusions that can be drawn from these methods. This is not a test of low-carbon CEA\n– that would demand an LCA or other methods that includes inputs and infrastructure.\nResponse:\nThe reviewer is right and that the LCA methodology is the appropriate tool for\nquantifying detailed environmental impacts. We are keenly aware of the key differences\nbetween our approach and LCA, and we have differentiated the two approaches in our\noriginal manuscript.\nMore detailed clarifications and amendments in the manuscript are elaborated below in\nresponse to the specifics. We thank the reviewers for highlighting the need to be cautious\nabout the framing of the results.\n Introductory Comment:\nI also think the human dimensions of the proposed interventions are largely\noverlooked and should be considered with more care. Finally, I think the figures and\nphrasing of some of the methods are difficult to interpret, and the authors should\nendeavour to make clearer connections between the actual numbers they calculated\nand the conclusions they draw. I would be happy to re-review it once these changes have\nbeen made.\nI list specific versions of these comments in no particular order for consideration:\nResponse:\nWe thank the reviewer for highlighting the need to consider human dimensions of\nCEA concerning significant import and agricultural land use. Indeed, human and social\ndimension are an important aspect of a holistic assessment, and opportunities exists for\nfuture work using holistic methodologies such as food sovereignty framework1–3. We have\nalso improved our figures to make interpretation easier.\nMore detailed clarifications and amendments in the manuscript are elaborated below in\nresponse to the specifics.\n Itemised Comment:\n1. “The calculated threshold is fundamentally a balance between anticipated CEA\ncarbon footprint solely from energy use and avoided carbon footprint implicit in CEA\nimpact propositions (Fig. 1b).” - I am concerned about the framing of CEA as\nenvironmentally friendly prior to any LCA work.\nAs the authors themselves point out, there are a number of infrastructural investments\nthat must be amortized as carbon per kilogram product – and other recent work in this\nfamily of journals has shown that infrastructure is perhaps the most important factor in\nLCA of urban ag (Hawes et al., 2024).\nThere were similar findings in other, older CEA work (Goldstein et al., 2017). It might be\nmore accurate to say that the operation of CEA could mitigate trade-offs under confluent\nconditions, but I don’t think that the authors demonstrate clearly that CEA is low-carbon\neven if its operation is.\nThis is ultimately a phrasing and framing issue, I certainly don’t expect the authors to\nconduct a full-blown LCA to address this.\nHawes, Jason K., Benjamin P. Goldstein, Joshua P. Newell, Erica Dorr, Silvio Caputo,\nRunrid Fox-Kämper, Baptiste Grard, et al. “Comparing the Carbon Footprints of Urban\nand Conventional Agriculture.” Nature Cities, January 22, 2024, 1–10.\nhttps://doi.org/10.1038/s44284-023-00023-3.\nGoldstein, Benjamin, Michael Hauschild, John Fernández, and Morten Birkved. “Testing\nthe Environmental Performance of Urban Agriculture as a Food Supply in Northern\nClimates.” Journal of Cleaner Production 135 (November 2016): 984–94.\nhttps://doi.org/10.1016/j.jclepro.2016.07.004.\nResponse:\nWe have amended the manuscript to make clear to the readers at multiple points\nthe need for a further LCA, especially for certain forms of CEA where infrastructure can\nbe the primary contributor of emissions.\nSpecifically:\n(a) The need for LCA is highlighted at –\n Page 3, Paragraph 2 – “In this work, we developed and calculated a\nMaximum Energy-use Threshold, or MET, per kilogram of crop, for different\ncountries and different future scenarios. This helps us in identifying at an\naggregate level, favourable scenarios which have the potential to realise\nthe climate mitigation potential of CEA. For CEA ventures operating within\nthis measure of operational prudence, more detailed LCA evaluating the\nfull environmental impact should then be embarked on.“\n Parge 3, Paragraph 3 – “In doing so, the aim of this article is to determine\nthe confluence of conditions that could potentially allow CEA to mitigate\nEnergy-Food-Climate Trade-off, before heavy investment of time and\nresources into an LCA.“\n(b) Especially for forms of CEA where infrastructure can be key (including citation\nsuggested by reviewer) –\nPage 9, Paragraph 1 of Discussion – “Here, the Maximum Energy-use-\nThreshold (MET) is introduced as a resource use metric balancing energy-food-\nclimate consideration. It is a tool complementary to environmental impact\nassessment methodology such as LCA, where LCA could include impacts from\nother sources, such as infrastructure. LCA remains important as some form\nand scale of CEA might require little energy but with most of its emissions\nstemming from infrastructure – such as a small-scale urban farm4,5. Here,\nbecause energy use is a key carbon emissions contributor in larger scale CEA,\nthe MET provides a measure of prudence where operation exceeding it will not\nposition CEA to achieve a lower carbon food system, even without conducting\nan LCA.Operating below this measure of prudence places a CEA scenario in\ngood standing for a more detailed analysis using LCA to confirm its full\nenvironmental impact profile.”\n Itemised Comment:\n2. The manuscript should be edited once more to remove odd words or\ngrammatical tricks here and there, like an extra “be” at line 77. “Transiting” at line 156.\nSentence at 247. Etc\nResponse:\nThank you, done.\n Itemised Comment:\n3. In general, the figures could use some work. Aesthetically, they are quite nice,\nand they communicate a simple message quite well. But they fail to communicate the\nkey message of the results section, which is where and under what circumstances the\nEUP exceeds or is below the MET. I don’t know that I think the current figures need\nremoved or changed, but an additional panel that shows the EUP divided by MET relative\nto 1 might be helpful.\nResponse:\nFor better clarity, we have split the two panels of the original Figure 2 and added a\nnew feature to the original panel of Figure 2b, which is now presented in the article as\nFigure 3. Specifically, we have enhanced all the figures in the following manner:\na) Page 4, Figure 2: MET considering combinations of contextual parameters –\n The average energy use-productivity for indoor farming and greenhouses\nwere extracted from literatures and illustrated in the panel as a line marker.\nCountries with MET higher than this average denotes a favourable scenario.\nWe have clarified this new addition in the text (Page 4, Paragraph 1) as well:\n“The average EUP for different crops and form of CEA was extracted from\nliteratures to visualise how current CEA compare to the METs. Countries\nwith MET above these markers denote a favourable scenario for CEA\nimplementation today, given the current operational state of CEA (GH –\nGreenhouse, or IF – Indoor Farming).“\nb) Figure 3:EUP estimates from literature and energy prudence index–\n An “Energy Prudence Index” was added where a positive displacement to\nthe right of the line denotes higher energy use than the MET and that\nliterature would not be sustainable. A negative displacement to the left of\nthe line denotes lower energy use than the MET and hence further time and\nresources should be invested in a LCA to study the set-up in that literature\nsource.\nWe have clarified this new addition in the text (Page 6, Paragraph 1): “To\nevaluate the climate mitigation potential of CEA in particular country, a\n“Energy Prudence Index” is suggested here. This represents the ratio\nbetween the difference in reported energy use and MET. A positive value in\nthe Energy Prudence Index signifies a higher energy use than the MET, and\na negative value in the index signifies a lower energy use than the MET.”\nc) Page 7, Figure 4a and Figure 4b; Page 8, Figure 5a and Figure 5b – The average\nenergy use extracted from current literatures are added into the figures. Similar to\nFigure 2a, this will help the reader understand that if the current average is already\nhigher than the METs, it is unlikely those countries will provide a favourable\nscenario.\n Itemised Comments:\n4. At line 135, there is discussion of the low carbon impacts of electricity in certain\ndeveloping countries. I have to wonder about the externalities associated with siting high\nenergy-use crop growing systems in places where the grid may be unstable or unprepared\nfor a new, large user. While I can appreciate the idea, it seems a bit short-sighted to me.\n5. Related to this, I do not understand this statement – why is Ethiopia better\nthan the others named? -- “Resonating with a previous study that assessed macro\nindicators for indoor vertical farming’s feasibility and sustainability in Africa, Ethiopia\nstands out as the African nation suitable for CEA.\nResponse:\nWe thank the reviewer for highlighting the externalities that prospective CEA\noperators or planner should take note of. We fully agree that beyond the consideration of\nrenewable energy availability and current agrifood supply chain, there will be non-\necological related factors.\nWhile we cannot confirm specific grid development that will resolve stability and\ncapacity issues definitively in the future, we believe the mentioned developing countries\ncan accommodate CEA because large energy users that cannot tolerate grid instability\nalready exists at the mentioned developing countries. Specifically, data centers. For\nexample, in Ethiopia, Raxio, a Tier III certified data centre with less than 1.6 hours\ndowntime per year has been operating since November 20226. In Congo, the National\nData Center is anticipated to be inaugurated in end of 20257. With regards to the\navailability of energy resources to accommodate new users, for countries such as\nEthiopia, renewable energy resources remain largely unexploited8.\nIn a separate study, development indicators of African nations were extracted from World\nBank as well as FAO and synthesized into a feasibility index. The index include\ndevelopment indicators in macro areas such as food security, climate change\nvulnerability, infrastructure and others. In that work, Ethiopia has a synthetic feasibility\nindex that is favourable for indoor vertical farming with artificial lighting in Africa9. On the\nother hand, for Congo and Democratic Republic of Congo, they have been classified as\n“not favourable” when considering the fourteen macro-indicators. Ethiopia scored seven\nfavourable classifications while Congo and Democratic Republic of Congo scored four\nfavourable classifications each.\nIn conjunction with our results presented here, where Ethiopia has one of the cleanest\nelectricity grids in Africa and thus allowing a higher MET, there is high confidence that\nEthiopia is a standout African nation for CEA.\nWe thank the reviewer for highlight this opportunity to clarify this statement. We have now\nadded this clarification in Page 5, Paragraph 1 – “Resonating with the results here, another\nstudy where indoor vertical farming feasibility scores are synthesized from development\nindicators, Ethiopia scored favourably to host indoor vertical farming in macro indicators\nsuch as food security, climate change vulnerability, infrastructure, and others9. Though\nexternalities such as grid stability and capacity can be a concern in developing countries\nsuch as Ethiopia, siting CEA would be possible as energy intensive, technology\ninfrastructure like data centres already exists and provides a template for planning high\nenergy use, high cooling load ventures6.”\n Itemised Comment:\n6. I am very unclear on what type of food is being offset in the agricultural land\nsubstitution scenario. Is this local food production replaced by CEA? Or imported food?\nIf imported food, the explanation doesn’t seem to make sense to me. The conclusion\nseems to indicate that this section is focused on how exporting countries might reduce\ntheir footprint – this is interesting and perhaps more internally consistent, but I have\nsome concerns as discussed in my next point.\nResponse:\nWe thank the reviewer for highlighting this point and providing the opportunity to\nclarify. In that section, the carbon opportunity cost from using currently occupied land to\nproduce food locally was used to calculate the MET. Hence, it is taken to substitute local\nfood production and with the assumption that the agricultural land is converted to\nprovide environmental services (carbon sequestration). We acknowledge that the short\nmention of “wheat and soybean producing nations.” at Page 7, Line 236 of the original\nmanuscript can be elaborated to eliminate any confusion.\nThe paragraph introducing the results has been expanded to make clear of our\nassumptions and what the result implies. It now reads at Page 8, Paragraph 3:\n“Here, a carbon opportunity cost was valued for land currently occupied to produce\nwheat and soybean in order to account for this opportunity of carbon sequestration10. The\ncountries analysed include wheat and soybean producing nations as reported to FAO11.\nThe result in Figure 4 shows the MET of wheat and soybean CEA if the CEA production\nnow substitutes agricultural land outputs and the agricultural land can now be restored\nto provide environmental services.”\n Itemised Comment:\n7. Overall, I remain a bit unconvinced of the idea that CEA inherently reduces the\nland use for agriculture in other locations. These sorts of telecoupling effects are\nunpredictable at best, and things like local labor markets are unlikely to simply vanish if\nthe demand from a particular export partner is reduced. For that matter, the authors still\nneed to talk about the in-migration to cities that would be required, the upskilling of\nagricultural labor, etc. If CEA requires less employees per kg of produce, it seems unlikely\nthat the land use transition would be dramatic, since the original farmers still need a\nsource of income.\nResponse:\nWe thank the reviewer for highlighting the social and economic dimensions that\nhave to be considered for CEA to be a successful in transforming the agrifood system.\nCEA inherently increases the yield output (kilogram crop per unit of land area) on an\narable land or non-arable land in an urbanised environment. Hence, the ability of CEA to\nreduce land use is not dissimilar to investigations of the link between agricultural\nintensification and land-sparing12. As exploitable gap between farm yields and genetic\nyields potential closes13, CEA provides a form of precision agriculture that can increase\nyield output, but without land constraint as more storeys can be built upon.\nGlobally, there are observations supporting the general trend of increasing yield output\nwith slowing land use, and more land will be needed without agricultural intensification\n14. However, the link of agriculture intensification and land sparing remains highly context\nspecific15,1617. This lends to the highly context dependent nature of CEA to mitigate\nenergy-food-climate trade-off. Here, a focus on balancing energy-food-climate trade off\nwas placed because of the urgency to balance the demand and impact on natural\nresources by agrifood systems. Nonetheless, we agree that the social-economic\nconditions should be elaborated in the discussion to prompt readers to think beyond the\nscope of this work and also provide opportunities for future work.\nPage 9, Paragraph 2 of Discussion reads:\n“It is worth highlighting that a particular CEA system operating below the MET does not\nconfirm the accomplishment of CEA impact proposition such as international import\nelimination or agricultural land restoration. These agrifood system transformation will\nrequire not just technical innovation in CEA operation, but also confluent social-\neconomical and policy conditions involving diverse stakeholders18. Particularly for the\nlink between agricultural intensification through CEA and land sparing, stimulative\nconditions are required to promote land sparing for environmental services15–17. They\ninclude policies for conservation-set aside programs with financial instruments to\nremunerate landowner for the alternative use of rural lands19,20, and potentially increasing\ngrain imports at low commodity price12. The holistic evaluation of sustainability\nencompassing ecological, social and economic aspect would require other conceptual\nframeworks1–3, and presents opportunities for future work. The MET provides a measure\nof operational prudence in order to truly realise the climate mitigation benefit of these\nsustainability propositions. Here, there are a few applications in which the MET can\nalready be valuable.”\n Itemised Comment:\n8. The authors talk about an expanded technical lifespan for solar, but I don’t think\nthey ever say what 50 years is an alternative to. What is the technical lifespan in EcoInvent?\nThis should be in the main text (I see it in the methods, but this is a Nature Comms piece,\nnot all readers will keep going to find the methods).\nResponse:\nThe technical lifespan of a solar panel is 30 years. This is now included in Page 9,\nParagraph 2 where it reads:\n“To calculate the emission factor of a 50-year technical life span solar panel, the existing\nlife cycle inventory of a 30-year technical life span solar panel was extended to continue\nat a degrading annual yield for another 20 years.“\nWe thank for reviewer for highlighting the need for this information to be presented earlier.\n Itemised Comment:\n9. I’m a bit confused in the methods section with Eqn. 1. If the country is land-\nlocked, all food is assumed to be ground-transported? This doesn’t make sense for,\nfor example, import of bananas from Colombia to Switzerland. Some of that\ndistance is, of course, by sea.\nResponse:\nWe thank the reviewer for the opportunity to clarify in this detail.\nEqn.1 relates to pairs of importing and exporting nation, and only when both the countries\nare land-locked, will the food be assumed to be ground transported. For example, while\nSwitzerland is a land-locked country, only trading between another land-locked country\nsuch as Austria would use ground-transportation. Colombia is not a land-locked country;\nhence it will involve maritime transport.\nWe have taken the opportunity to clarify this in the methods section (Page 11, Paragraph\n2), and the equation itself:\n“For trading pairs where both countries are land-locked – without access to ports and\ndirectly accessible to each other overland – the shortest road distance between capitals\nwas used instead”\n Itemised Comment:\n10. I appreciate that the authors try to differentiate between cooled lorry use and\ndry goods, but does Table 1 in the methods suggest that the carbon footprint of a\nrefrigerated lorry is lower than that of an unspecified lorry? This might indicate that the\n“unspecified” option includes some combination of a variety of lorries, which wouldn’t\nreally be achieving the authors’ goals.\nResponse:\nWe thank the reviewer for the opportunity to clarify in this detail.\nBased on Ecoinvent 3.9.1 datasets, the impact score/ carbon footprint of a refrigerated\nlorry is indeed lower than that of an unspecified lorry. Here, the “unspecified” option\nrefers to an aggregate of different lorry class (metric ton) and EURO emission standard.\nWhile the distribution of EURO emission standard lorry for both datasets are similar, the\nsmall difference in assumed lorry class (3.5-7.5, 7.5-16, 16-32, >32 metric ton)\ncontributed to a higher impact score for unrefrigerated lorry dataset. In the refrigerated\nlorry dataset, only 16-32 and >32 metric ton lorry class was considered. However, in the\nunrefrigerated lorry dataset, smaller lorry class were included. As compared to >32\nmetric ton lorry class, smaller lorry class can have up to 5.31 times the emissions.\nWe selected to use an unspecified option as we do not have granular information about\nthe specific lorry/EURO class used in a specific leg of a logistic chain. While it is not ideal,\nwe have opted to use this instead of assuming a specific class for countries in a specific\nregion.\n Itemised Comment:\n11. The authors should be more specific about the programming languages\nused.\nResponse:\nWe have rephrased the explanation under “Code Availability”:\n“Computational steps are described in the Methods, as well as in original works\nreferenced in this study. Scripts performing the computation are written in Python\nprogramming language and available from the corresponding author upon reasonable\nrequest. The custom code that support the findings of this study are also available on\nCode Ocean (10.24433/CO.2717777).”\nWe thank the reviewer for the opportunity to clarify in this detail.\n Itemised Comment:\nI have not had time to replicate the code, but assume that the authors were careful\nand thorough.\nResponse:\nWe understand that the reviewer may not have time to replicate the code.\nNonetheless, we are appreciative of the reviewer’s comment and time in reviewing the\nmanuscript. Thank you again.\nReferences\n1. Wittman, H. Food sovereignty: An inclusive model for feeding the world and\ncooling the planet. One Earth6, 474–478 (2023).\n2. Oteros-Rozas, E., Ruiz-Almeida, A., Aguado, M., González, J. A. & Rivera-Ferre, M.\nG. A social–ecological analysis of the global agrifood system. Proceedings of the\nNational Academy of Sciences116, 26465–26473 (2019).\n3. Ruiz-Almeida, A. & Rivera-Ferre, M. G. Internationally-based indicators to\nmeasure Agri-food systems sustainability using food sovereignty as a conceptual\nframework. Food Secur11, 1321–1337 (2019).\n4. Hawes, J. K. et al. Comparing the carbon footprints of urban and conventional\nagriculture. Nature Cities1, 164–173 (2024).\n5. Goldstein, B., Hauschild, M., Fernández, J. & Birkved, M. Testing the\nenvironmental performance of urban agriculture as a food supply in northern\nclimates. J Clean Prod135, 984–994 (2016).\n6. Raxio Group. Raxio Continues Support of Africa’s Digital Transformation and\nDigital Economy with Launch of New Flagship Data Centre in Ethiopia.\nhttps://www.raxiogroup.com/raxio-continues-support-of-africas-digital-\ntransformation-and-digital-economy-with-launch-of-new-flagship-data-centre-\nin-ethiopia/ https://www.raxiogroup.com/raxio-continues-support-of-africas-digital-\ntransformation-and-digital-economy-with-launch-of-new-flagship-data-centre-\nin-ethiopia/ (2023).\n7. African Development Bank. Congo: New data centre funded by African\nDevelopment Bank will cement national and subregional digital sovereignty.\nhttps://www.afdb.org/en/news-and-events/congo-new-data-centre-funded-\nafrican-development-bank-will-cement-national-and-subregional-digital-\nsovereignty-70847 https://www.afdb.org/en/news-and-events/congo-new-data-centre-funded-\nafrican-development-bank-will-cement-national-and-subregional-digital-\nsovereignty-70847 (2024).\n8. Yalew, A. W. The Ethiopian energy sector and its implications for the SDGs and\nmodeling. Renewable and Sustainable Energy Transition2, 100018 (2022).\n9. Paucek, I. et al. A methodological tool for sustainability and feasibility\nassessment of indoor vertical farming with artificial lighting in Africa. Sci Rep13,\n2109 (2023).\n10. Searchinger, T. D., Wirsenius, S., Beringer, T. & Dumas, P. Assessing the efficiency\nof changes in land use for mitigating climate change. Nature564, 249–253 (2018).\n11. Food and Agriculture Organization. Food and Agriculture Organization Corporate\nStatistical Database. https://www.fao.org/faostat/en/#data/TM (2022).\n12. Rudel, T. K. et al. Agricultural intensification and changes in cultivated areas,\n1970–2005. Proceedings of the National Academy of Sciences106, 20675–20680\n(2009).\n13. Cassman, K. G. Ecological intensification of cereal production systems:\nYield potential, soil quality, and precision agriculture. Proceedings of the National\nAcademy of Sciences96, 5952–5959 (1999).\n14. Hannah Ritchie. Yields vs. land use: how the Green Revolution enabled us to feed\na growing population. Our World in Data (2017).\n15. Pratzer, M. et al. Agricultural intensification, Indigenous stewardship and land\nsparing in tropical dry forests. Nat Sustain6, 671–682 (2023).\n16. Lin, M. & Huang, Q. Exploring the relationship between agricultural intensification\nand changes in cropland areas in the US. Agric Ecosyst Environ274, 33–40 (2019).\n17. Ceddia, M. G., Bardsley, N. O., Gomez-y-Paloma, S. & Sedlacek, S. Governance,\nagricultural intensification, and land sparing in tropical South America.\nProceedings of the National Academy of Sciences111, 7242–7247 (2014).\n18. Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat\nSustain3, 974–976 (2020).\n19. Barrett, C. B. Overcoming Global Food Security Challenges through Science and\nSolidarity. Am J Agric Econ103, 422–447 (2021).\n20. Xu, Z. et al. Grain for Green versus Grain: Conflict between Food Security and\nConservation Set-Aside in China. World Dev34, 130–148 (2006)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "General comments:\n1. The MET concept is novel, but its relationship to existing environmental performance metrics (e.g., specific LCA\nindicators, energy use productivity benchmarks) could be more explicitly articulated. How does MET add actionable value\nbeyond existing LCA thresholds?\n2. The assumption that CEA systems meeting MET will inherently achieve climate benefits seems optimistic. A discussion\non potential rebound effects or partial achievement of impact propositions would be helpful.\n3. Figures 2–5 are rich in data but can be challenging to interpret. Consider simplifying axis labels, highlighting key outlier\ncountries more consistently, and integrating policy-relevant thresholds directly into visuals\n4. For trade distance assumptions: was any validation done to assess the realism of capital-to-capital distances vs. actual\nsupply chains?\n5. How were seasonality and perishability handled in emissions calculations (especially for leafy greens and strawberries)?\nSpecific comments:\n- L3. Confluent: Maybe the term “contextual conditions” fits better\n- L72: MET is an excellent construct, but its definition and derivation process could be clearer. Consider including a visual\nflowchart summarising its components (inputs, calculations, thresholds).\n- L86-89: Have you deliberately excluded non-energy impacts in the MET (e.g., materials, embodied carbon of\ninfrastructure)?\n- L106 (Fig.3 ): The interpretation of MET vs. actual EUP is clear in figures, but some textual explanations could be more\nconcise and better linked to key implication\n- L126-128: Could the authors clarify how air freight assumptions for strawberries were verified across the sample countries?\n- L132: In the discussion of favourable locations (e.g., Ethiopia, Paraguay), have you considered local grid reliability and\ncold-chain logistics as potential limiting factors?\n- L210 (Fig. 4): How sensitive are your MET results to PV efficiency improvements vs. emission factor assumptions?\n- L293: In the discussion of favourable locations (e.g., Ethiopia, Paraguay), have you considered local grid reliability and\ncold-chain logistics as potential limiting factors? Other interesting issues that can be explored in the discussion section are:\na) Can the authors reflect more on how MET could be institutionalised — e.g., integrated into CEA certification, subsidies, or\nsiting decisions? b) What would be required to transition MET from research to policy use — what validation or consensus\nbuilding is needed?", "reviewer_2": "Thanks again to the authors, this time for conducting a thorough and effective revision. I remain convinced that this is a\nuseful and interesting article, and I have very few comments to share. These are minor corrections that should not require\nfurther peer review. I look forward to citing this article soon.\n1. I think the authors need to make a bit clearer in the main text what is considered in the MET. Specifically, I’d add a few\nsentences in the introduction or early results explaining how the carbon emissions per imported produce were found.\n2. At line 77, “The calculation” should be clarified. Which calculation? MET?\n3. A number of places need another grammar check. For example, “resource use are” at line 58 and “the” is missing in\n“within local food system” at line 85. There are other examples, just needs a careful copy-edit.\n4. I think Fig 4 is missing some of the orange annotations that indicate where IF and GH fall on the MET spectrum."}, "author_response": "General Response To Both Reviewers\nWe would like to thank both reviewers again for their valuable comments and for\nproviding us an opportunity to enhance the clarity of this work.\nSpecifically, we thank Reviewer 1 for highlighting the need to enhance clarity in the\nmethodological assumptions as well as adding relevant discussion issues. The amended\nmanuscript has benefited greatly from these suggestions, with significant additions to\nthe discussion and two additional figures. One as a visual flowchart to help readers\nunderstand the method better, and the other to illustrate an example of how MET could\nbe institutionalised.\nWe also thank Reviewer 2 for the careful reading, spotting minor grammatical\ncorrections in the manuscript and minor annotation correction in one of the figures.\nAdding to the improvements from previous round of revision, the quality of this article has\nbenefited significantly from these suggestions.\nFor ease of perusal, specific responses to Reviewer 1 can be found from HERE,\nand specific responses to Reviewer 2 can be found from HERE.\nWe thank both reviewers again for their insightful comments and welcome any\nfurther clarification.\nResponse to comments by Reviewer 1\n Comment:\nThe MET concept is novel, but its relationship to existing environmental\nperformance metrics (e.g., specific LCA indicators, energy use productivity benchmarks)\ncould be more explicitly articulated. How does MET add actionable value beyond existing\nLCA thresholds?\nResponse:\nWe thank the reviewer for the opportunity to clarify the value of MET.\nThe MET adds value by allowing operators to act on improving their operational prudence\ntowards an objective measure and allow policymakers to objectively form policies that\ncould incentivise operational prudence. Elaborating on the reason for why LCA results\nand industry disclosed performance is unsuitable as an objective benchmark:\nFirst, as LCA results are highly specific to a scenario, the associated energy use-\nproductivity cannot be used as a threshold for other scenarios. Even when a scenario\ncould have been a lower carbon option in its comparison, it does not translate to another\ncontext. Hence, the need for a threshold complementary to highly granular LCAs.\nNext, current energy use-productivity based on disclosed operations cannot be used as\na benchmark because it might not be fully transparent in its internal methodology. More\nimportantly, an industry average based on disclosed operations cannot be used because\ntheir relative performance is not grounded in a quantitative evaluation considering\nenvironmental impact parity to local, existing options.\nHere, the MET has the advantage of being objective in its calculation independent of CEA\noperator’s potential bias and is transparent in its data as well as approach. In the first\ndraft, two ways in which the MET could be valuable was elaborated. In this revision, line\n305 to 310, we highlighted the value of an objective benchmark before elaborating them.\nReproduced verbatim:\n“An objective and transparent benchmark is necessary to guide the development of CEA.\nObjective in being independent of a CEA operator’s potential bias during its calculation\nand being transparent in its data and approach. The MET adds value here by informing\noperators of an objective benchmark to optimise towards and for policymakers to\nobjectively form policies that incentivise sustainable operation. Elaborating further, here\nare two ways in which the MET can already be valuable.“\n2\n Comment:\nThe assumption that CEA systems meeting MET will inherently achieve climate\nbenefits seems optimistic. A discussion on potential rebound effects or partial\nachievement of impact propositions would be helpful.\nResponse:\nWe thank the reviewer for the suggestion to discuss on potential rebound effects or\npartial achievement of impact propositions. Combining with a paragraph in the\ndiscussion where other supporting social-economic and policy conditions have to be\npresent for CEA to achieve climate benefits, we have restructured the discussion into\nthree main sections. In the last section, a further discussion on the potential rebound\neffects is now included. The section (with italic font being new explanation) reads:\n“ Nuances of sustainability assessment of CEA and emerging technologies.\nIt is worth highlighting that a particular CEA system operating below the MET does not\nconfirm the accomplishment of CEA impact proposition such as international import\nelimination or agricultural land restoration. These agrifood system transformation will\nrequire not just technical innovation in CEA operation, but also supporting social-\neconomical and policy conditions involving diverse stakeholders1. Particularly for the link\nbetween agricultural intensification through CEA and land sparing, stimulative\nconditions are required to promote land sparing for environmental services2–4. They\ninclude policies for conservation-set aside programs with financial instruments to\nremunerate landowner for the alternative use of rural lands5–7. The holistic evaluation of\nsustainability encompassing ecological, social and economic aspect would require\nother conceptual frameworks8–10, and presents opportunities for future work.\nThe sustainability assessment of CEA, as well as other emerging technologies, are highly\nnuanced and can often cause unintended rebound consequences. For example, as CEA\nreduces agricultural land use, there can be more arable land available, given the\nassumption that stimulative conditions for land sparing are present. These stimulative\nconditions will include financial incentives that can be more attractive than the gains\nfrom using the land for food production. This can cause an increase in cost of goods for\nmany sectors, threatening the promise of enhancing food security.\nAlternatively, if CEA were to be hosted on non-arable land, social rebound effects can\nemerge. For example, to reduce CEA thermal cooling load and the cost of operation, CEA\noperators favours locations with cool, temperate climates and low electricity costs. Often,\nthese places would already have communities settled there. A parallel to this rebound\neffect can be drawn from the dense siting of data centres in North Virginia, USA11. Quality\nof life impact, rising local utilities bills, water scarcity, decreasing property value,\ndeclining local community support have all been reported.\nproducts demands a separate and far more complex analysis. Consequential life cycle\nassessment which considers the implications on marginal suppliers due to a change in\nsupply and demand can be a first step12. Modelling stakeholders as agents and integrating\nemergent behaviours from their interaction to life cycle assessment models can be the\nnext step13–15. The environmental impact and benefits of CEA as well as emerging\ninnovations are highly nuanced. Advanced modelling tools such as LCA will no doubt be\nnecessary to aid decision making16,17. Here, the MET provides a complementary\nbenchmark in CEA before such detailed modelling and analysis is embarked on.”\n Comments:\nFigures 2–5 are rich in data but can be challenging to interpret. Consider simplifying axis\nlabels, highlighting key outlier countries more consistently, and integrating policy-\nrelevant thresholds directly into visuals\nResponse:\nWe thank the reviewer for the suggestion to make the figures’ interpretation easier.\nWith regards to simplifying the axis labels, we have chosen to retain the current version\nto help readers understand the specific CEA impact propositions, crop and scenarios for\nwhich the MET is calculated. On highlighting key outliers more consistently, the top five\nkey outliers have been annotated consistently, except in cases where there are only four\noutliers. Lastly, with regards to integrating policy relevant thresholds directly into visuals,\nit resonates with the reviewer’s latter comment on how to further institutionalise MET and\npossible work to transition MET for policy use. This is addressed below here (under\nresponse to comment pertaining to L293).\n4\n Comment:\nFor trade distance assumptions: was any validation done to assess the realism of\ncapital-to-capital distances vs. actual supply chains?\nResponse:\nFor trade distance assumptions, Bertoli et al. calculated the capital-to-capital\ndistances based on relevant ports (the one with the most shipping lanes) and existing\nmaritime routes18. This approach was referenced as it provides a better estimate than a\nshortest straight-line estimation. The validation of the referenced work with actual supply\nchains is difficult because there can be many routes from more than one port between\ntwo countries, with multiple freight operators and the individual choice of freight routes\nnot readily available.\n Comment:\nHow were seasonality and perishability handled in emissions calculations\n(especially for leafy greens and strawberries)?\nResponse:\nAs seasonality and perishability affects the supply chain choices of an importing\ncountry through fluctuating commodity prices and transportation costs, a country often\nhave more than one trading partner for a food product. This is incorporated into the MET\nas it takes the weighted average trade distance considering all its trading partner in a year.\nThe MET calculation was repeated with FAO data from 2012 to 2022 and the median of\nthe calculations over 10 years was taken to also capture short-term supply chain\nchanges due to unexpected events.\nIn addition, because leafy greens and strawberries are perishable and requires cooling\nduring transportation, the emission factor used in the analysis of leafy greens relate to\nsea freight container with reefer cooling, and in the case of strawberries, the emission\nfactor correspond to freight aircraft transport with reefer cooling.\n5\n Comment:\nL3. Confluent: Maybe the term “contextual conditions” fits better\nResponse:\nWe thank the reviewer for the suggestion to clarify the title. We now propose a\nmore fitting title after considering the emphasis over the two revisions and novelty of MET\nthat resonated with both reviewers. The new title for this article is:\n”Contextual Conditions Define Maximum Energy-use Threshold in Low-Carbon\nControlled Environment Agriculture for Agri-Food Transformation.”\n Comment:\nL72: MET is an excellent construct, but its definition and derivation process could\nbe clearer. Consider including a visual flowchart summarising its components (inputs,\ncalculations, thresholds).\nResponse:\nWe thank the reviewer for highlighting the need to clarify on the definition and\nderivation process. We have separated out this definition from the original Figure 1 and\nnow created a new Figure 2 where a visual flowchart summarising the inputs,\ndatabase and brief description of the calculation steps.\n6\n Comment:\nL86-89: Have you deliberately excluded non-energy impacts in the MET (e.g.,\nmaterials, embodied carbon of infrastructure)?\nResponse:\nWe thank the reviewer for the clarification. Yes, we have excluded non-energy\nimpacts from materials and embodied carbon of infrastructure in the MET. This is\nbecause in most large-scale CEA facilities, energy use is the key emissions contributor.\nHowever, there can be scenarios where low energy is required in a farm and embodied\ncarbon of infrastructure will be significant, for example in small-scale urban farms. This\nwas highlighted in the discussion section, where the need for LCA remains as more\nemissions contributors are taken into consideration. The exclusion of materials and\nembodied carbon of infrastructure lends to the intent of MET being a technology agnostic\nmeasure, complementary to LCAs.\n Comment:\nL106 (Fig.3 ): The interpretation of MET vs. actual EUP is clear in figures, but some\ntextual explanations could be more concise and better linked to key implication\nResponse:\nWe agree and thank the reviewer for the suggestion. We have rephrased and made\nthe related explanation from line 164 to 170 more concise. Reproduced verbatim:\n“Based on literature, almost all of the current CEA setups operate at EUP several\nmultiples of the MET. Only in setups where extremely low energy is used and where short\nshelf life produce such as strawberries are air freighted, their operations hold potential to\nbe a lower carbon option. For example, utilising a hydraulic rotating greenhouse setup in\nSingapore for CEA of lettuce can be achieved at an EUP of 0.021 kWh/kg19, lower than the\nMET (Supplementary Data 1). As a land-scarce nation intensifying agri-food industry\ninvestments to bolster food security20, it would be beneficial for Singapore to invest in low-\nenergy greenhouses for lettuce cultivation.”\n7\n Comment:\nL126-128: Could the authors clarify how air freight assumptions for strawberries\nwere verified across the sample countries?\nResponse:\nWe thank the reviewer for the clarification. This has been a challenge, as with the\nprevious validation of the shipping route. While we cannot verify the actual air freight\ndistances due to the lack of available data, our approach here of using the shortest\nstraight-line distance is aligned with the Great Circle Distance approach used by the\nInternational Civil Aviation Organization’s (ICAO) air freight carbon emission calculation\nmethodology21. In the referenced methodology, the verification of the actual flown\ndistance was determined to be not feasible for the time being as well. Combining with\nthe previous clarification on shipping distance validation, we have now added a short\nexplanation in the Methods section to highlight this challenge and assumption.\nReproduced verbatim:\n“Due to the unavailability of actual shipping and air freight data, validation of sea freight\ndistance and air freight distance was not feasible here. However, the shortest straight-\nline approach using a geodisc here is consistent with the shortest straight-line approach\nusing a Great Circle Distance by the International Civil Aviation Organisation in its freight\ncarbon emission calculator21“\n Comment:\nL132: In the discussion of favourable locations (e.g., Ethiopia, Paraguay), have you\nconsidered local grid reliability and cold-chain logistics as potential limiting factors?\nResponse:\nWe thank the reviewer for highlighting the two potential limiting factors. For the\nfirst factor of local grid reliability, it was addressed in the previous revision, where grid\nstability and capacity could indeed be a concern. However, with data centres that are\nhighly energy demanding and does not tolerate grid instability already existing in\ndeveloping countries such as Ethiopia, the energy intensive technology infrastructures\nfor data centres are a template for siting CEA. With regards to cold-chain logistics, the\ndeveloping cold-chain capabilities would be a potential driving factors for CEA to be sited\nclosely to demands or ports to maximise perishable produce shelf life.\n8\n Comment:\nL210 (Fig. 4): How sensitive are your MET results to PV efficiency improvements vs.\nemission factor assumptions?\nResponse:\nIn the sensitivity analysis of MET, the results were directly affected by changes in\nemission factors due to several technological factors. Namely, (1) full reliance on PV,\n(2) full reliance on PV and extended life span of PV, and (3) in projected grid mix under\ndifferent SSP. PV efficiencies were improved in the third scenario using prospective\nenergy grid emission factors but its effect not isolated from other energy grid\ndevelopments. There, PV efficiency were assumed in premise22 to increase at different\ntime points:\n% module micro-Si single- multi-Si CIGS CIS CdTe\nefficiency Si\n2010 10 15.1 14 11 11 10\n2020 11.9 17.9 16.8 14 14 16.8\n2050 12.5 26.7 24.4 23.4 23.4 21\nWhile a separate sensitivity analysis of only PV efficiency improvements was not\nconducted, parallels can be drawn from sensitivity scenario two, where emission factors\nwere reduced due to increased power generation over the course of a longer service life\n(148,575.72 kWh over 50 years). Initially, the power generated over a 30-year PV lifespan\nwas calculated to be 90,997.2 kWh at ~15.1% efficiency. Assuming a 76% efficiency\nincrease to 26.7% panel efficiency, the total power generated over a 30-year lifespan\nwould be around 160,115.1 kWh. This is a ~7% increase in the total power generated over\na 50-year lifespan PV panel with lower panel efficiency. Consequently, the MET would\nincrease by the same percentage.\nHence, we could assume the MET is sensitive to PV efficiency increase, as it is to PV\ntechnical lifespan increase, both increasing the total power generated by a PV unit.\n Comment:\nL293: In the discussion of favourable locations (e.g., Ethiopia, Paraguay), have you\nconsidered local grid reliability and cold-chain logistics as potential limiting factors?\nOther interesting issues that can be explored in the discussion section are: a) Can the\nauthors reflect more on how MET could be institutionalised — e.g., integrated into CEA\ncertification, subsidies, or siting decisions? b) What would be required to transition MET\nfrom research to policy use — what validation or consensus building is needed?\nWe thank the reviewer for suggesting additional issues that could be of interest in\nthe discussion. We have added a new subsection in the discussion. Reproduced\nverbatim for easy reference:\n“ Next Steps for MET\nBeyond this first calculation of MET for different countries, more granular data will be\nneeded to quantify MET more precisely and avail more specific policy use cases.\nValidation with national, state, or province-level trade data obtained through local\nagencies could be feasible, instead of relying on aggregated data, thereby producing a\nmore precise quantification of the MET at a regional level. While MET cannot be used to\ncertify the sustainability of a CEA operation, policymakers can use MET to promote\nsustainable practice of CEA.\nFigure 1 Possible ways to institutionalise MET to encourage sustainable controlled environment agriculture. The\ntwo-colour banners denote operational range where MET or LCA is more relevant. The asterisk symbol denotes a\npercentage that policymakers can adjust or add more increments in between. Some of the suggested policy levers are\nlisted below each milestone.\nFigure 7 illustrates one possible way to do this. For operations above the MET, where\nlarge-scale CEA is highly unlikely to be sustainable even without performing an LCA,\nsuch\nunsustainable practices should be limited in production volume, allowing CEA operators\nto scale up in production volume only upon demonstrating an improvement. Incentives\ncan come in the form of co-funded research grants, one off sustainability practice grants,\nor subsidies to perform full LCA. Only when operators are below the MET and certified\nthrough an LCA that it is a lower carbon option, preferential electricity rate or access to\ncarbon trading as a seller can then be made available. Consensus building with operators\nwill be critical in determining parameters such as the appropriate MET milestones and\nproduction volume limitations to not deter new entrants or stifle small start-ups and\ninstead motivate sustainable innovation practices. Here, the approach and formulation\nto calculate a MET based on contextual conditions paves the way forward for such\nstrategies.”\n Comment:\nI think the authors need to make a bit clearer in the main text what is considered in the\nMET. Specifically, I’d add a few sentences in the introduction or early results explaining\nhow the carbon emissions per imported produce were found.\nResponse:\nWe thank the reviewer for highlighting the need to clarify what is included in the\nMET. This resonates with a previous comment by Reviewer 1 as well. We have now\nincluded a new figure (Figure 2) as a visual aid summarising the inputs and brief\ndescription of the calculation steps. This will allow readers to understand the MET\nconceptualisation and calculation without necessarily referring to the Methods.\n Comment:\nAt line 77, “The calculation” should be clarified. Which calculation? MET?\nResponse:\nWe thank the reviewer for the clarification and have added “MET” in line 77 to\nspecify that it means “MET calculation”.\n Comment:\nA number of places need another grammar check. For example, “resource use are” at\nline 58 and “the” is missing in “within local food system” at line 85. There are other\nexamples, just needs a careful copy-edit\nResponse:\nWe thank the reviewer for the careful reading. Indeed, we have also spotted a\nnumber of places that require grammatical corrections. We have now corrected the\ngrammar within the entire manuscript to the best of our knowledge.\n11\n Comment:\nI think Fig 4 is missing some of the orange annotations that indicate where IF and GH fall\non the MET spectrum.\nResponse:\nWe thank the reviewer for their sharp observation. We have amended the figure.\nIn Figure 4, the MET for Strawberry does not have an orange annotation as it does not have\nan average estimate for indoor farming based on current literature (as noted in the figure\ncaption). However, the reviewer is right that we have missed out two orange arrow\nannotation for Wheat and Soybean, denoting that from current literatures, average\nestimates are not capture within the figure’s displayed range. We have checked the other\nfigures, and they are correct. We thank the reviewer for highlighting this.\n1. Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat\nSustain3, 974–976 (2020).\n2. Ceddia, M. G., Bardsley, N. O., Gomez-y-Paloma, S. & Sedlacek, S. Governance,\nagricultural intensification, and land sparing in tropical South America.\nProceedings of the National Academy of Sciences111, 7242–7247 (2014).\n3. Lin, M. & Huang, Q. Exploring the relationship between agricultural intensification\nand changes in cropland areas in the US. Agric Ecosyst Environ274, 33–40 (2019).\n4. Pratzer, M. et al. Agricultural intensification, Indigenous stewardship and land\nsparing in tropical dry forests. Nat Sustain6, 671–682 (2023).\n5. Barrett, C. B. Overcoming Global Food Security Challenges through Science and\nSolidarity. Am J Agric Econ103, 422–447 (2021).\n6. Xu, Z. et al. Grain for Green versus Grain: Conflict between Food Security and\nConservation Set-Aside in China. World Dev34, 130–148 (2006).\n7. Rudel, T. K. et al. Agricultural intensification and changes in cultivated areas,\n1970–2005. Proceedings of the National Academy of Sciences106, 20675–20680\n(2009).\n8. Oteros-Rozas, E., Ruiz-Almeida, A., Aguado, M., González, J. A. & Rivera-Ferre, M.\nG. A social–ecological analysis of the global agrifood system. Proceedings of the\nNational Academy of Sciences116, 26465–26473 (2019).\n9. Ruiz-Almeida, A. & Rivera-Ferre, M. G. Internationally-based indicators to\nmeasure Agri-food systems sustainability using food sovereignty as a conceptual\nframework. Food Secur11, 1321–1337 (2019).\n10. Wittman, H. Food sovereignty: An inclusive model for feeding the world and\ncooling the planet. One Earth6, 474–478 (2023).\n11. Robert Leslie et al. Inside the secretive world of America’s AI data centers.\nBusiness Insider https://www.businessinsider.com/inside-the-secretive-world-\nof-americas-ai-data-centers-2025-9 (2025).\n12. Schaubroeck, T. Relevance of attributional and consequential life cycle\nassessment for society and decision support. Frontiers in SustainabilityVolume\n4-2023, (2023).\n13. Hicks, A. Seeing the people in LCA: Agent based models as one possibility.\nResources, Conservation & Recycling Advances15, 200091 (2022).\n13\n14. Larrea-Gallegos, G., Marvuglia, A., Navarrete Gutiérrez, T. & Benetto, E. A\ncomputational framework for modeling socio-technical agents in the life-cycle\nsustainability assessment of supply networks. Sustain Prod Consum46, 641–654\n(2024).\n15. Lan, K. & Yao, Y. Integrating Life Cycle Assessment and Agent-Based Modeling:\nA Dynamic Modeling Framework for Sustainable Agricultural Systems. J Clean\nProd238, 117853 (2019).\n16. Bluhm, H. et al. Understanding digitalization’s environmental impact: why LCA is\nessential for informed decision-making. npj Climate Action4, 41 (2025).\n17. Hellweg, S., Benetto, E., Huijbregts, M. A. J., Verones, F. & Wood, R. Life-cycle\nassessment to guide solutions for the triple planetary crisis. Nat Rev Earth\nEnviron4, 471–486 (2023).\n18. Bertoli, S., Goujon, M. & Santoni, O. The CERDI-seadistance database. Preprint at\nhttps://doi.org/10.5281/zenodo.46822 (2016).\n19. Song, S. et al. Comparison of vegetable production, resource-use efficiency and\nenvironmental performance of high-technology and conventional farming\nsystems for urban agriculture in the tropical city of Singapore. Science of The\nTotal Environment807, 150621 (2022).\n20. Singapore Food Agency. A sustainable food system for Singapore and beyond.\nFood for Thought, a digital publication by Singapore Food Agency\nhttps://www.sfa.gov.sg/food-for-thought/article/detail/a-sustainable-food-\nsystem-for-singapore-and-beyond (2022).\n21. International Civil Aviation Organization. ICAO Carbon Emissions Calculator\nMethodology Version 2.\n22. Sacchi, R. et al. PRospective EnvironMental Impact asSEment (premise): A\nstreamlined approach to producing databases for prospective life cycle\nassessment using integrated assessment models. Renewable and Sustainable\nEnergy Reviews160, 112311 (2022).\n14"}, {"round": 3, "reviewer_comments": {"reviewer_1": "General Response To Both Reviewers\nWe would like to thank both reviewers for their valuable comments and for\nproviding us an opportunity to enhance the clarity of this work.\nFirst, we are pleased that the work resonated with Reviewer 1 as a valuable\nframework for assessing CEA sustainability. Indeed, we hope this work informs the\naudience of a complementary way to think about evaluating CEA sustainability. The\namended manuscript benefited greatly from Reviewer 1’s comments to enhance on its\nclarity, particularly in the first results and discussion section.\nNext, we thank Reviewer 2 for their valuable feedback and for giving many\ncomments that has significantly improved the clarity and positioning of the paper. The\nreviewer has rightly distilled the spatial-temporal perspective that CEA innovators and\npolicymakers should approach with, and that which we are communicating here. Without\nsuch a nuanced perspective to CEA, one might be quick to dismiss or overstate the\npotential in which it brings to transform the agrifood supply chain.\nFor ease of perusal, specific responses to reviewer 1 comments can be found\nfrom HERE, and specific responses to reviewer 2 comments can be found from HERE.\nWe thank both reviewers again for their insightful comments and welcome any\nfurther clarifications.\nPoint-by-point response to the reviewers’ comments\nResponse to comments by Reviewer 1\n Comment:\nPage 1, Line 14 – Amend “the feasibility to meet these goals” to “the feasibility of\nmeeting these goals”", "reviewer_2": "General Response To Both Reviewers\nWe would like to thank both reviewers for their valuable comments and for\nproviding us an opportunity to enhance the clarity of this work.\nFirst, we are pleased that the work resonated with Reviewer 1 as a valuable\nframework for assessing CEA sustainability. Indeed, we hope this work informs the\naudience of a complementary way to think about evaluating CEA sustainability. The\namended manuscript benefited greatly from Reviewer 1’s comments to enhance on its\nclarity, particularly in the first results and discussion section.\nNext, we thank Reviewer 2 for their valuable feedback and for giving many\ncomments that has significantly improved the clarity and positioning of the paper. The\nreviewer has rightly distilled the spatial-temporal perspective that CEA innovators and\npolicymakers should approach with, and that which we are communicating here. Without\nsuch a nuanced perspective to CEA, one might be quick to dismiss or overstate the\npotential in which it brings to transform the agrifood supply chain.\nFor ease of perusal, specific responses to reviewer 1 comments can be found\nfrom HERE, and specific responses to reviewer 2 comments can be found from HERE.\nWe thank both reviewers again for their insightful comments and welcome any\nfurther clarifications.\nPoint-by-point response to the reviewers’ comments\nResponse to comments by Reviewer 1\n Comment:\nPage 1, Line 14 – Amend “the feasibility to meet these goals” to “the feasibility of\nmeeting these goals”"}, "author_response": ""}]

Decision letter