Transition to bio-based plastic packaging reveals complex climate–biodiversity trade-offs

Domain: neuroscience
Published: 2026-01-31
Editorial outcome: major_revision

Abstract
Plastics are a major contributor to global greenhouse gas emissions and biodiversity loss, with packaging accounting for around 40% of European plastic production. Bio-based plastics are often promoted as a climate-friendly alternative, yet their broader environmental implications remain unclear. Here we conduct a harmonized life cycle assessment of fossil- and bio-based plastic packaging, integrating end-of-life fate and plastic leakage. We find that while bio-based plastics reduce greenhouse gas emissions, they increase ecosystem damage, primarily driven by land use. At the product level, outcomes are sensitive to feedstock origin and waste management. When mismanaged, environmentally persistent bio-based plastics contribute substantially to ecosystem damage. Scaling scenarios for Europe show that even complete substitution with bio-based plastics cannot offset the environmental burden of continued demand growth. Only strong demand-side measures, particularly demand reduction and improved circularity, can mitigate trade-offs across climate change and biodiversity, calling for a shift from material substitution to systemic interventions in production and consumption.

Introduction
Plastics have become integral to modern economies, driven by their durability, low weight, and versatility. Global production reached 460 million tons in 2019 , bringing the total to over 9.5 billion tons since 19501,2. Remarkably, 80% of all plastics ever produced were made in the past two decades, reflecting the exponential rise in global demand3. Initially developed for durable applications, plastics are now predominantly used in disposable or short-lived products. In EU-27+3 (European Union plus Norway, Switzerland and the United Kingdom), packaging alone accounts for 40% of plastic production, with 60% dedicated to food and beverage applications4–6.

While plastic packaging plays a critical role in preserving goods and preventing food waste7, its environmental cost is escalating. The sector emits around 2 Gt CO -equivalent (CO e) annually, 2 2 accounting for approximately 4.5% of greenhouse gas (GHG) emissions, mainly due to its reliance on fossil-based feedstocks and energy-intensive production processes8. Simultaneously, the accumulation of plastic waste has triggered a global pollution crisis9. An estimated 8 million tonnes of plastic enter the oceans each year10, where they persist, break down into microplastics (MPs), and accumulate in marine food webs11,12. Plastic debris harms wildlife through entanglement and ingestion, introduces toxic additives into ecosystems, and may pose risks to human health via contamination of air, water, and food13,14. Failures in end-of-life management, particularly in the packaging sector, remain a major source of uncontrolled emissions to terrestrial and aquatic environments5,15.

In response, bio-based plastics, deriving from biomass, have gained attention as substitutes for fossil-based materials16. Broadly, these can be divided into “drop-in” bio-based plastics (e.g., bio-based polyethylene), which are chemically identical to their fossil-based counterparts and compatible with existing recycling systems; and compostable bio-based plastics (e.g., polylactic acid) which are designed to biodegrade under industrial composting conditions and thus require distinct waste management infrastructure. By capturing biogenic CO e during plant growth, these 2 materials offer the prospect of lowering life-cycle carbon emissions, and are increasingly promoted as part of circular bioeconomy strategies17. Although bio-based plastics currently represent only 1–2% of global production, this share is expected to grow18. Substituting two thirds of conventional plastics with bio-based alternatives could avoid 241–316 MtCO e annually19. Such 2 transitions are encouraged by regulatory instruments like the Single Use Plastics Directive20.

However, the large-scale deployment of bio-based plastics faces significant barriers. High production costs make them less competitive in price-sensitive markets, and the lack of standardized recycling or composting infrastructure for the more innovative materials hinders their integration into existing waste systems21. More fundamentally, their sustainability remains debated. First-generation feedstocks, typically derived from readily fermentable sugars in edible polysaccharides such as corn and sugarcane, demand substantial land, water, and fertilizer inputs. This raises concerns about competition between food, fuels and plastics, as well as broader ecological degradation22. Second-generation biomass, derived from non-edible biological waste, has emerged in recent years as a more ethically viable and widely available feedstock, albeit a more complex one. Beyond the technical challenges limiting large-scale deployment, second-generation feedstocks may still induce indirect land-use change when their sourcing competes with existing land functions or displaces other uses. Furthermore, despite their renewable origin, mismanaged bio-based plastics can degrade and fragment in the environment much like their fossil-based counterparts, thereby contributing to plastic pollution.

Although life cycle assessment (LCA) has become a standard tool for evaluating the environmental performance of plastics, its application has often been limited in scope, with most studies emphasizing climate-related metrics23,24. Furthermore, few incorporate harmonized inventory data across life cycle stages or account for plastic leakage into the environment25. Large-scale assessments have advanced understanding of the GHG implications of plastic production and disposal, but they still overlook broader environmental dimensions, including impacts on ecosystem quality and human health26. Recently, analyses examining the substitution of conventional plastics with bio-based alternatives have provided a robust understanding of the implications of plastic pollution and carbon mitigation strategies for cropland expansion27. However, this study does not integrate other environmental dimensions within a comprehensive life cycle framework. As bio-based plastics gain market share, a more comprehensive understanding of their systemic impacts is urgently needed28.

Here, we conduct a harmonized life cycle assessment of fossil- and bio-based plastic packaging to quantify environmental trade-offs across climate change, ecosystem quality, and human health. We include case studies of five bio-based plastics: bio-based polyethylene (bio-PE), bio-based polyethylene terephthalate (bio-PET), polylactic acid (PLA), polyhydroxybutyrate (PHB), and thermoplastic starch (TPS); and seven fossil-based plastics: polyethylene terephthalate (PET), high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS), and polyurethane (PUR).

The analysis is structured at three levels. At the material level, we examine life cycle stage contributions and perform a detailed trade-off analysis across midpoint and endpoint indicators. At the product level, we compare packaging providing equivalent functionality, accounting for variability in feedstock origin and end-of-life treatment. At the macro level, we explore large-scale substitution scenarios for Europe under alternative demand and energy trajectories. The study adopts an attributional perspective, and thus no consequential effects such as indirect land use change are considered. The scope includes first- and second-generation bio-based plastics but excludes third-generation feedstocks due to the lack of reliable production data. All life cycle stages are included, with explicit integration of plastic leakage quantification and its associated impact assessment.

Results
Bio-based plastics reduce carbon footprint at the cost of increased ecosystem damage from land use

Figure 1 presents the impact results associated with the production of market-representative packaging materials from primary plastics, excluding the incorporation of recycled content. Although the uncertainty range is wider for bio-based polymers, reflecting the higher variability of agricultural and industrial inventories, their GHG emissions remain generally lower than those of fossil-based counterparts. The partial overlap of uncertainty ranges, observed mainly for bio-PET, bio-PE, and PHB, indicate that this advantage may narrow under less favorable production conditions but remains robust on average. (Fig. 1a). This benefit is mainly due to CO 2 uptake during plant growth, which serves as the feedstock for biopolymer production. Net emissions range from 3.5 to 5.5 kgCO 2 e kg⁻¹ for bio-based packaging, compared to 5 to 9.5 kgCO 2 e kg⁻¹ for fossil-based equivalents. In both material types, the polymer production stage dominates the carbon footprint. For bio-based systems, this is due to the energy-intensive conversion of biomass into fermentable carbohydrates and polymer precursors; for fossil-based systems, it is linked to steam cracking of hydrocarbon feedstocks. In contrast, the raw material extraction phase, whether fossil or agricultural, contributes relatively little to overall climate impacts (Fig. 1f). End-of-life scenarios also influence carbon outcomes. Fossil-based plastics stored in landfills tend to sequester carbon over a 100-year time horizon, whereas biodegradable plastics partially degrade, releasing biogenic CO and CH 29. Composting, when available, yields primarily CO e with limited 2 4 2 methane, while landfilling under anaerobic conditions results in a split of CO and CH 2 4 emissions30. The net benefit of bio-based packaging in terms of climate change therefore depends on both production inputs and end-of-life conditions.

However, this carbon advantage comes with a significant trade-off in ecosystem quality. Impacts expressed in PDF m² yr (Potentially Disappeared Fraction of species over a given area and time) are consistently higher for bio-based materials, ranging from 1 to 5 PDF m² yr kg⁻¹, compared to 0.3 to PDF m² yr kg⁻¹ for fossil-based options (Fig. 1c). These damages are mainly driven by land use, which includes occupation and transformation (Method section), followed by acidification (both terrestrial and marine), as shown in Fig 2. The magnitude of land use impact from bio-based plastics is linked to the nature of the feedstock and conversion efficiency. For instance, producing bio-polyethylene (bio-PE), which requires carbon-rich ethylene, demands approximately 6 kg of corn kg⁻¹ polymer, nearly three times more biomass than PLA production. As a result, land occupation and the associated pressures on biodiversity are amplified. First-generation PLA packaging relies on agricultural land, exerting considerable stress on ecosystems in the short term (Fig. 2b). Land occupation and transformation account for roughly 60% of the total impact for corn-based PLA and sugarcane-based PHB. However, these impacts are partially offset by reduced climate change effects due to biogenic carbon sequestration. Shifting to second-generation residual biomass for PLA production reduces the land-use contribution to ecosystem damage. This decrease is explained by the application of economic allocation to distribute the environmental burden among the outputs of corn cultivation. A lower economic value is attributed to corn stover. The environmental benefit of moving from first- to second-generation feedstocks hinges on the assumption that these residues are treated as biowastes. If co-products from first-generation crops acquire greater economic value, a larger share of land-use burden would be allocated to them. Moreover, some second-generation resources can also be derived from dedicated cellulose crops, such as wood, which may in turn require significant land areas. For fossil-based packaging, climate change emerges as the primary driver of ecosystem damage. The low contribution of land use reduces the overall impact (Fig. 2b).

Another emerging concern is the impact of plastic leakage into the environment, which remains underrepresented in traditional LCA models31. Based on average loss rates in Europe, the production of 1 kg of packaging results in emissions of approximately 0.1 g of MPs during polymer production and an additional 10 g of macroplastic debris at end-of-life32,33. Using recent characterization methods, we estimated the physical impact of these losses on marine ecosystems on the short-term (≤ 100 years) and long-term (> 100 years)34–36. An estimation of the uncertainties associated with the characterization factors (CFs) used is provided in Supplementary Fig. 2. We considered similar effect factors to characterize the impact of different microplastic emissions on marine biota, making damage comparisons between polymers only dependent on the residence time of debris34. Due to the higher risk of end-of-life packaging loss, secondary microplastics (MP ), arising from the fragmentation of macroplastics reaching the environment, exert far greater 2 impacts than primary microplastics (MP ), which are directly emitted from the technosphere 1 during pellet production (Fig. 1d). Polymers with low degradation rates in the marine environment exhibit longer residence times, increasing exposure risks for marine organisms. For infinite-term impacts (sum of the short and long term), all non-biodegradable polymers in the marine environment cause damage ranging from 0.005 to 0.035 PDF m² yr kg⁻¹ of packaging produced. Packaging debris composed of polymers with high degradation rates, such as PHB, exhibits minimal persistence in the marine environment, resulting in significantly attenuated effects, with impacts approaching 0 PDF m² yr kg⁻¹. Unde r a scenario of average fragmentation of macroplastics released post-consumption25, the results reveal a minor contribution to the overall impacts across the different life cycle stage in high-income countries with effective waste collection systems (Fig. 2b). However, this contribution becomes significant in low-income countries, where end-of-life plastic management is challenging. In such contexts, the impact of PLA packaging leakages can account for up to 30% of the total ecosystem burden.

Human health damages from plastic packaging result from multiple pathways, including particulate matter, water availability, and human toxicity (Fig. 2a). While these are covered in life cycle models, uncertainties persist, especially for toxicity-related impacts, due to data gaps and simplified modeling37. Direct human exposure to microplastics or chemical additives during the use or leakage phases is not included in conventional LCAs, as no validated human-health exposure and effect characterization models currently exist for plastics. The results indicate a disadvantage for bio-based packaging mainly due to the fertilizers and the substantial water requirements associated with crop cultivation (Fig. 1b and Fig. 2b). Bio-based packaging production leads to an impact ranging from 3 × 10⁻⁶ to 5 × 10⁻⁶ Disability-Adjusted Life Years (DALY) kg⁻¹, corresponding to a loss of approximately two to three minutes of life expectancy (Fig. 1b). Excluding polyurethane (PUR), the production of fossil-based packaging results in an impact ranging from 0.2 × 10⁻5 and 0.3 × 10⁻5 DALY kg⁻¹ (about one to two minutes of life expectancy).

Bio-based product-level impacts are highly sensitive to feedstock and end-of-life

To assess how upstream and downstream choices affect environmental performance at the product level, we analyzed the life cycle of a PLA food tray (13.9 g). The functional unit was defined as the ability to contain 1 liter of food product, ensuring comparability across materials with different densities and compositions38. We examined the effects of feedstock type (first- or second-generation), recycled content incorporation, and end-of-life options for bio-based packaging, in comparison with fossil-based trays, including polyethylene terephthalate (PET) (18.5 g), polypropylene (PP) (11.9 g), and extruded polystyrene (XPS) (9.9 g).

Feedstock origin has a major influence on ecosystem impacts, and a more limited but non-negligible influence on climate. Switching between first-generation feedstocks (corn and sugarcane) has only minor effects on GHG emissions (18 vs. 14 gCO e tray⁻¹), due to similar 2 agricultural practices and allocation rules (Fig. 3a). In contrast, the use of residues such as corn stover reduces agricultural emissions, but leads to higher impacts during conversion, which is more energy-intensive and less efficient. As a result, the total climate impact of stover-based PLA was less favorable than expected (20 gCO e tray⁻¹), highlighting the trade-off between benefits during 2 agricultural phase, and burdens during manufacture. When excluding the benefit of biogenic CO 2 uptake, PLA production from recycled pellets results in lower climate change impacts (26 gCO e 2 tray⁻1) compared to production from virgin feedstock. From an ecosystem quality perspective, however, the differences were substantial: stover-based PLA tray has a footprint of ~0.015 PDF m² yr, far lower than the ~0.026 and ~0.031 associated with corn- and sugarcane-based trays. These results confirm that land use is the dominant variable in ecosystem damage and that residue-based systems offer real advantages. Geographic context also influences impact levels. For example, sugarcane cultivation in Brazil for PHB production benefits from natural rainfall, resulting in a lower water footprint than artificially irrigated crops in other regions (Fig. 3d). By reducing input requirements and eliminating the need for agricultural cultivation, the production of PLA from recycled pellets significantly lowers ecosystem damage (~0.003 PDF m² yr tray⁻¹).

Over a 100-year time horizon, end-of-life pathways can produce contrasting effects on climate change and ecosystem outcomes (Fig. 3b). Landfilling and incineration with energy recovery result in comparable GHG emissions (15 gCO e tray⁻¹), as carbon is either partly sequestered in landfills 2 or oxidized to CO during combustion, with the latter offset by energy recovery. Composting leads 2 to higher emissions (26 gCO e tray⁻¹), since PLA undergoes extensive aerobic degradation, 2 converting most of its biogenic carbon into CO . Although the compost generated can substitute 2 for soil amendments, the associated benefit remains minor and does not offset emissions to the same extent as energy recovery. Recycling results in the lowest GHG emissions, because the polymer carbon remains stored in the recycled material, delaying its release. When PLA is littered, a small amount of CO is emitted due to its slow biodegradation under marine conditions, but the 2 main burden shifts to ecosystem quality, as persistent microplastics affect marine biota (0.012 PDF m² yr tray⁻¹). This level of impact far exceeds that of any controlled treatment, underscoring the critical role of preventing plastic leakage for ecosystem.

Comparisons with fossil-based trays reveal contrasting trends across impact indicators. Figure 3c presents the results for climate change and ecosystem quality impacts, combining different scenarios for raw-material sourcing and end-of-life management. The baseline PLA production setup, representing average market conditions, is complemented by lower- and upper-bound estimates (PLA-low and PLA-high) that capture geographical variability in feedstock yield and energy intensity. Assumptions underlying these regionalized cases are provided in Supplementary Table 14.

In terms of greenhouse-gas emissions, PLA trays consistently outperform those made from PET (110 gCO e tray⁻¹) and PP (75 gCO e tray⁻¹) across all configurations. Under a mixed end-of-life 2 2 scenario, PLA shows climate performances comparable to XPS (50–60 gCO e tray⁻¹) in the 2 baseline case and substantially lower in the lower-bound case. In contrast, under the upper-bound conditions, only complete recycling at end-of-life would keep emissions below those of XPS.

For ecosystem quality, feedstock type exerts a stronger influence than geographic variability. Differences between PLA-low and PLA-high are smaller than those between first-generation, residue-based, and recycled systems. Only trays made from recycled pellets or corn stover, and managed properly at end-of-life, achieve results within the fossil-based range (~0.005–0.02 PDF m² yr tray⁻¹).

Scaling bio-based plastics reveals that only strong demand reduction avoids ecological trade-offs

To assess the system-wide consequences of bioplastic transitions, we modeled the environmental impacts of plastic packaging demand in Europe from 2020 to 2050 under various substitution scenarios (Fig. 4). We simulated a progressive replacement of fossil-based packaging with first-generation bio-based alternatives, under two energy contexts (current mix vs. full decarbonization), and three annual growth trajectories for packaging demand: -3%, 0% (no growth) and 3%. In all cases, substitution was modeled as a gradual transition toward 100% bio-based packaging by 2050, allowing us to isolate the effect of substitution from other structural changes. In addition, gradual increases in recycling rates were included in the analysis to account for the potential incorporation of recycled materials for packaging production.

Assuming that all sectors align with GHG emission reduction trajectory recommended by the Intergovernmental Panel on Climate Change ( IPCC)39, the target would be to reach approximately 17 MtCO e by 2050 (Fig 4.a). If packaging demand increases by 3% annually, approximately the 2 current European growth rate33, total GHG will more than double by 2050 under a business-as-usual scenario, reaching almost 250 MtCO e. Full substitution with bio-based plastics could 2 prevent this increase, containing emissions at around 100 MtCO e under a decarbonized system 2 incorporating recycled materials (Bio-low). Nevertheless, even under the most favourable trajectory, emission levels would remain fivefold higher than the target recommended by the IPCC.

In a no-growth scenario, replacing fossil plastics with bio-based alternatives results in a significant decrease in carbon emissions, potentially reaching 50 MtCO e by 2050. Yet this level would still 2 remain clearly above the threshold recommended by the IPCC. Only a reduction in packaging demand of at least 3% year⁻¹ would substantially lower CO e emissions to levels aligned with 2 IPCC recommendations, potentially positioning Europe as a key player in global climate change mitigation.

In contrast, ecosystem damage increases significantly under all substitution scenarios (Fig. 4b). Replacing fossil-based plastics with first-generation bio-based alternatives could lead to a three- to fivefold increase in cumulative ecosystem damage between 2020 and 2050. This is primarily due to the expansion of agricultural land required to supply biomass feedstocks. In the 3% growth-case scenario with full substitution without incorporation of recycled material (Bio-Max), the total impact reaches 1.10 × 10¹¹ PDF m² yr. This metric represents the overall potential loss of species, integrating all stressors affecting ecosystem quality at the endpoint level, with land occupation and transformation being the dominant contributors. To illustrate the order of magnitude, this value would correspond to a level of biodiversity damage comparable to that expected over roughly 10% of Europe’s current arable land, acknowledging that most of the loss would result from agricultural expansion. While this comparison remains illustrative, given the very low likelihood of achieving full substitution to bio-based plastics and the fact that a substantial share of biomass production would occur outside Europe, it highlights the potential ecological burden associated with large-scale substitution. Even in the most favorable energy scenario, ecosystem pressures remain significantly higher under bio-based packaging than under continued fossil use, revealing a strong climate–biodiversity trade-off at scale.

For both analyzed indicator, the gap between fossil-based and bio-based trajectories narrows significantly with decreasing packaging demand, underscoring the critical importance of consumption reduction. Demand growth emerges as the dominant driver of environmental burden. While material substitution and energy decarbonization help reduce per-unit impacts, they are insufficient to achieve absolute reductions in a context of continued consumption growth. Our results show that, even under the most ambitious mitigation pathways, increased throughput of materials outweighs the gains from cleaner technologies. Substitution policies thus emerge as adjustment variables that can help alleviate environmental pressure. Without curbing the structural growth in plastic demand, technological improvements and feedstock substitutions are likely to be offset. This highlights the need to couple substitution strategies with broader demand-side policies, material sufficiency measures, and shifts in consumption patterns.

Methods
Life cycle assessment goal and scope

The objective of this study is to assess the burden shifting associated with carbon footprint reduction strategies for the plastic packaging industry. The selection of bio-based and fossil-based polymers reflects the composition of the current packaging market, following the approach of Zheng et al. (2019)46. Bio-based packaging materials facing substantial technical and economic barriers to large-scale adoption were excluded from the study scope due to uncertainties surrounding their feasibility as substitutes for conventional packaging. Similarly, cardboard and glass packaging were not included in the study due to the significant differences in their properties and usage compared to fossil-based plastic packaging.

A bottom-up approach was adopted to compare the environmental impacts associated with the production of fossil-based and bio-based packaging at different levels. At the material level, the functional unit (FU) is 1 kg of packaging. The assessment allows for the identification of burden shifting between fossil and biomass-based production while analyzing the contributions of different life cycle stages and the contribution of midpoint impacts on damage (Fig. 1 and 2). At the product level, the FU is a tray with a volume of 1 L. The assessment enables the study of technical and management strategies aimed at optimizing biomass use as an alternative (Fig. 3). At the macro level, the FU is the European packaging demand. The assessment provides insights into the ecosystem effects of substituting conventional packaging with bio-based alternatives (Fig. 4).

Harmonization procedure for the life cycle inventory

A harmonized inventory review of exchanged flows across the life cycle covered raw-material extraction, polymer production, packaging manufacturing, and end-of-life, ensuring result comparability and consistency (Supplementary Fig. 1). The conversion and end-of-life rates are provided in Supporting Table 1.

Two harmonization layers were applied: system harmonization (align FU, system boundaries, and methodological choices across reviewed LCAs) and technical harmonization (standardize inventories by adjusting data and calculations). The workflow was: (1) disaggregate life cycle inventory (LCI) data to unit-process level; (2) classify by life-cycle stage and polymer; (3) sort by data quality; (4) complete gaps to match the FU. When multiple sources described the same process, mass/energy balance was verified before integration. For sources with consistent input shares, averages were used. Where ecoinvent v3.10 generic data existed, it was incorporated either as an average with literature values or as a reference for gap-filling (Supplementary Table 3). Because references flows varied (resin, bottles, film, cups, etc.), 1 kg of packaging was used as the standard FU.

Fossil-based packaging inventories rely on aggregated ecoinvent v3.10 data across life-cycle stages. Foreground flows (chemicals, energy, infrastructure, waste) were compiled and the background (energy/material supplies) was taken from ecoinvent v3.10 global/market mixes. Given limited traceability, activity data for infrastructure, energy, and chemicals prioritize global market suppliers.

Feedstock and carbon sequestration. The environmental impacts of feedstock were modeled based on first-generation biomass production due to its market readiness. Corn cultivated in the United States was selected as the feedstock for PLA, bio-PE, bio-PET, and TPS production, while sugarcane grown in Brazil was used for PHB production. For systems based on second-generation feedstocks, the agricultural phase was allocated economically between grain and stover (95% and 5%, respectively) following the World Food LCA Database.

The CO uptake during plant growth was calculated directly from the carbon content of each 2 polymer and the biogenic fraction of feedstock used, ensuring a consistent carbon mass balance across life-cycle stages. This method enables tracking of biogenic carbon through end-of-life processes and does not represent the total photosynthetic fixation by the plant, nor does it follow economic allocation rules. The resulting sequestration factors are 3.14 kgCO 2 kg⁻¹ for bio-PE, 0.69 for bio-PET, 1.83 for PLA, 2.04 for PHB, and 0.56 for TPS.

Bio-based polymer production. Bio-polyethylene: The production of bio-PE from corn begins with the enzymatic hydrolysis of starch, breaking it down into glucose monomers30. The glucose undergoes fermentation by yeast to yield bioethanol, which is then purified. In the final stage, bioethanol is subjected to catalytic dehydration, converting it into ethylene (CH), the key ₂ ₄ monomer for polyethylene synthesis (Supplementary Table 4).

Bio-polyethylene terephthalate: The production of bio-PET consists of the condensation polymerization of two chemicals: monoethylene glycol (MEG) and terephthalic acid (TPA), respectively accounting for 30% and 70% by weight47–49. If replacing fossil-based MEG with bio-(MEG) from biomass is relatively widespread, PTA remains largely fossil-based. Bio-PET retains the same properties as traditional PET, allowing it to integrate seamlessly into existing recycling systems. Bioethanol obtained from sugar fermentation is dehydrated to ethylene, which is subsequently oxidized to ethylene oxide and hydrolyzed to produce bio-based monoethylene glycol (bio-MEG). In parallel, TPA is synthesized from the catalytic oxidation of para-xylene (p-xylene) with acetic acid (Supplementary Table 5).

Polylactic acid: PLA production from corn involves several key processes, which were integrated into a tailored life cycle inventory50. First, the dried corn grains undergo wet milling to separate their components. The extracted starch is then hydrolyzed enzymatically into glucose monomers, which serve as the substrate for bacterial fermentation. To regulate pH during fermentation, calcium carbonate is introduced as a neutralizing agent, leading to the formation of calcium lactate upon reaction with calcium hydroxide. The solution is subsequently acidified with sulfuric acid to yield lactic acid, which undergoes purification before being converted into PLA through a ring-opening polymerization process (Supplementary Table 6). Details on PLA production from corn stover and sugarcane are provided in Supplementary Tables 7 and 8.

Polyhydroxybutyrate: PHB is a biodegradable polyester produced by bacteria as a carbon and energy storage compound51. Sucrose, the primary fermentable sugar, is extracted and refined from the juice before being supplied to PHB-producing bacteria. Under nutrient-limited conditions with excess carbon availability, these bacteria accumulate PHB in the form of intracellular granules. Following fermentation, the bacterial biomass is recovered through centrifugation or filtration. PHB is then extracted using solvent-based methods, followed by purification, drying, and processing into powder or granules for subsequent use (Supplementary Table 9).

Thermoplastic starch: TPS is a biodegradable polymer derived from corn starch. The production process begins with the milling of corn to separate starch from proteins, fibers, and oil. The extracted starch is then purified and dried into a fine powder. During processing, the starch is blended with plasticizers and subjected to heat and mechanical shear, leading to gelatinization, where the starch granules swell, absorb the plasticizers, and transition into a thermoplastic phase suitable for further processing. The inventory is based on aggregated data from the environmental product declaration of Mater-Bi (Ecoinvent 3.10).

Packaging Manufacturing. Due to the variability in processing technologies, the manufacturing of bio-based and fossil-based polymer resins into packaging was modeled using the generic extrusion and thermoforming processes provided by ecoinvent, based on global market data. Differences in manufacturing processes between conventional and bio-based packaging alternatives were not accounted for, as inventory data specific to these variations remain challenging to obtain.

Greenhouse gas emissions from incineration, landfill, composting and littering. GHG emissions from different end-of-life scenarios were estimated using different modeling approaches, including ecoinvent 3.10 datasets, and complementary literature data. Estimates were made over a 100-year time horizon to maintain consistency with the fact that impacts are assessed on the short term. For incineration, emissions were calculated from the polymer carbon content, distinguishing biogenic and fossil carbon fractions, and crediting the recovered energy according to the substitution-based allocation used in the consequential ecoinvent 3.10 database. For landfilling and composting, degradation rates were taken from Afshar et al. (2024)2, and methane and CO emissions were then derived from the assumptions of Benavides et al. (2020)30, which 2 specify the share of polymer carbon degraded, the fraction of methane oxidized, and the resulting atmospheric releases (Supplementary Table 10-11). Degradation rates under marine leakage conditions were based on kinetic data from Corella-Puertas et al. (2023)34, allowing estimation of GHG generation in seawater (Supplementary Table 12). These calculations simplify underlying biochemical processes and thus carry some uncertainty that warrants future refinement.

Collecting and recycling. The impacts related to end-of-life plastic collection and recycling were considered burden-free in the assessment of primary plastics (Fig. 1 and Fig. 2). For the assessment of production with re-incorporation of recycled materials (Fig. 3 and Fig. 4), impacts were modelled using data from the ecoinvent 3.11 database. Collection and recycling rates have been determined based on polymer type and recyclability potential.

Scenario modeling of large-scale substitution in European packaging

Carbon-reduction trajectories were modeled at the European level, reflecting Europe’s central role in the plastics transition52,53 and the availability of polymer-specific packaging data5. Trajectories were based on annual projections of packaging production and demand growth33 (Supplementary Table 15). Substitution trajectories replacing fossil-based with bio-based packaging followed the configurations of Zheng et al. (2019)46, which consider technical feasibility. Bio-low and Bio-high scenarios were modeled, corresponding to systems with and without energy decarbonization, respectively. The Bio-high scenario uses current electricity mixes from ecoinvent v3.10, while the Bio-low scenario applies the decarbonized global mix from SSP2-RCP 1.9, assuming a full renewable transition54.

For each scenario, polymer-specific material quantities across life-cycle stages were calculated from projected European packaging demand using a mass-flow model (Supplementary Fig. 3). The model was parameterized with polymer-specific conversion factors, collection rates, recycling efficiencies, end-of-life pathways, and leakage rates for 2020 and 2050 (Supplementary Tables 1 and 2). Material flows were computed using Supplementary Eqs. (4)–(14). Climate change and ecosystem quality damages were then quantified by combining life-cycle stage-specific material quantities with the corresponding impact factors per kilogram of polymer.

The dashed red line in Fig. 4a represents an indicative 1.5 °C-aligned greenhouse gas emissions pathway for the European plastics packaging system, derived from IPCC AR6 mitigation scenarios39. We operationalized this benchmark by (i) taking the IPCC global emissions trajectory consistent with 1.5 °C, (ii) allocating it to the plastic sector using its share of global GHG emissions reported in the literature, and (iii) scaling to the European packaging scope using the baseline 2020 packaging emissions in our model.

Impact assessment methodologies

Methodology. We used the IMPACT World+ v2.0.1 footprint version in Fig. 1, Fig. 3, and Fig. 4 to report decision-relevant indicators—climate change, ecosystem quality excluding climate change, human health excluding climate change, water scarcity footprint, and fossil and nuclear energy use—while maintaining methodological consistency with the expert version55,56. For climate change, we applied the short-term category using GWP100, which aligns with stakeholder priorities. We also report the “human health (residual)” and “ecosystem quality (residual)” indicators, which exclude contributions from climate change and water availability, as well as all long-term impact categories considered of lower relevance to stakeholders.

Figure 2 presents the contribution of midpoint indicators to the endpoint metrics human health and ecosystem quality, using IMPACT World+ v2.0.1 expert to evaluate environmental damage at the end of the cause–effect chain (including climate-change contributions). Only short-term (≤ 100 years) contributions were retained to ensure consistency with the time horizon considered in the footprint method.

Land use. Two types of land use are modeled in the life cycle inventories and assessed following the IMPACT World+ life cycle impact assessment method: land transformation and land occupation. Land transformation (m²) represents the conversion of natural land to a managed use (e.g., deforestation to create cropland). It is a short-term, non-time-dependent flow. Land occupation (m² yr) represents the maintenance of land in a productive state over time (e.g., continued cultivation of cropland). Both flows are recorded at the inventory level and translated into midpoint indicators: occupied area × duration for land occupation, and transformed area for land transformation. These are subsequently converted to endpoint ecosystem damage using the biodiversity-based CFs developed by de Baan et al. (2013)57, which express the potential loss of species over area and time (PDF m² yr) for each land-use type and ecoregion. These factors express the difference in ecosystem quality between a reference (undisturbed) state and the land-use type considered, based on observed changes in species richness and abundance across multiple taxa.

This framework therefore quantifies direct land-use impacts (occupation and transformation) but does not include indirect land-use change (ILUC), which represents market-mediated effects such as agricultural displacement. ILUC modeling requires economic or equilibrium approaches and is typically addressed within consequential LCA rather than attributional LCA58.

Integration of plastics impact assessment. The environmental damages caused by plastic leakage across the life cycle of bio-based and fossil-based packaging were assessed by coupling inventory-based estimates of plastic losses with the mechanistic CFs from the MarILCA initiative36 which account for fate, exposure, and effect processes and are conceptually aligned with USEtox59 (Supplementary Table 13).

The term leakage refers to the quantity of plastic that escapes the technosphere and enters the natural environment. Leakage occurs through both losses and releases along various transfer and redistribution pathways. The quantities of plastic losses were estimated using regionally parameterized statistical models, including plastics directly disposed into waterways and oceans, uncollected waste, and collected waste that is poorly managed60.

At the inventory level, MP emissions originate from the leakage of pellets during polymer 1 production, whereas MP result from the fragmentation of macroplastics mismanaged at end-of- 2 life25,32,33. Plastic losses from other sources, such as transportation or the use phase, were excluded from the study due to their relatively minor contribution and the lack of available data. The generation of MP was estimated based on an average fragmentation rate of macroplastics released 2 at end-of-life. A value of 50% was adopted, following the sensitivity analyses conducted by Corella-Puertas et al. (2023)34. Future research should ideally incorporate polymer-specific fragmentation rates to dynamically estimate MP₂ formation. MP were modelled as spherical 1 particles with a diameter of 1000 μm, while MP were represented as spherical particles of 100 2 μm34. Ideally, a statistical estimation of particle sizes and shapes could be performed in future work to assign more realistic physical properties to these fluxes.

At the impact-assessment level, the MarILCA CFs translate these emissions into potential damage to marine ecosystems. The fate component models the distribution and persistence of plastic particles across environmental compartments, including both the water column and marine sediments, and depends on polymer-specific surface loss rates, shape, and size of debris, as degradation primarily occurs at the surface of plastic fragments.35,61 The exposure–effect component links modeled environmental concentrations in the water column and sediments with experimentally derived effect data, 34,62 using a single generic factor that quantifies the physical harm of MPs to marine organisms, as polymer-specific differentiation is not yet available in the literature36,63. In the absence of robust toxicity data on the effects of macroplastics on marine biota (e.g., through entanglement), the impacts of macro debris were not included in this study. Similarly, limited data are available regarding the toxicity of polymer degradation products once released into the marine environment. Consequently, the potential effects associated with the leaching of additives could not be incorporated into this assessment.

In IMPACT World+, endpoint indicators such as (eco-)toxicity distinguish short-term (100 years) from long-term (>100 years) impacts, whereas MarILCA CFs integrate emissions over an infinite time horizon. To ensure consistency, we reconstructed finite-horizon fate factors by adapting the MarILCA fate matrix to a 100-year temporal boundary, using degradation and transfer data from Nadim et al. (2025)36. The fate model represents all first-order degradation and inter-compartment transfer processes through a multicompartment rate matrix (K), which governs the time-dependent mass of particles in each environmental compartment. Integrating this coupled system over a finite time horizon allows quantification of the fraction of total mass residence captured within 100 years. This provides short-term fate factors consistent with the IW+ threshold, while the remaining fraction defines the long-term contribution. Rapidly degrading or mobile plastics are almost entirely accounted for within the short-term horizon, whereas highly persistent polymers retain a significant residual burden beyond it. The analytical derivation of these finite-horizon matrices and their implementation are detailed in Supplementary Fig. 2 and Supplementary Eqs. (1)–(3).

Two contrasting end-of-life scenarios were also modeled to reflect differences in waste-management efficiency (Fig.2): a high-income context assuming 4 % plastic losses to the environment and a low-income context assuming 95 % losses.

Uncertainty analysis

Inventory Data. A Monte Carlo uncertainty analysis was performed to evaluate the robustness of the results and to account for variability in both foreground and background datasets. The analysis focused on inventory data, since uncertainties in impact assessment models were not included: IMPACT World+ does not provide standard deviations for its characterization factors, except for the plastic-impact category described below.

Uncertainties in inventory data were quantified assuming lognormal distributions. For all background datasets, the geometric standard deviations provided in ecoinvent 3.10 were used directly, based on its built-in pedigree matrix approach64. For the foreground systems developed in this study (production and end-of-life treatment of plastics) pedigree scores were established qualitatively according to data source, data generation method (industrial report, experimental data, or modelled estimate), publication date, and geographic relevance. These scores were converted into uncertainty factors following the ecoinvent convention and applied to compute geometric standard deviations for each exchange. The resulting uncertainty ranges served as input for the Monte Carlo simulation (1,000 iterations). Due to the presence of both positive and negative balancing flows for freshwater in ecoinvent, the Monte Carlo propagation for water-related categories occasionally yielded non-physical results (e.g., negative net water use). Consequently, uncertainty could not be reliably computed for ecosystem quality and water scarcity indicators, which are therefore reported as deterministic midpoint and endpoint values.

Plastic impacts. The uncertainties associated with the CFs (Supplementary Fig. 2) were estimated as 95% confidence intervals using a Monte Carlo simulation with 5,000 iterations, based on data provided by Saadi et al. (2025)36.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study presents several noteworthy results. It establishes that bio-based plastics lead to a clear reduction in greenhouse\ngas (GHG) emissions compared to fossil-based plastics. However, this climate benefit comes at the cost of significantly\nhigher ecosystem damage, primarily due to land use changes and the demands of agricultural input. This trade-off between\nclimate mitigation and biodiversity preservation is a central theme throughout the study. The research also highlights\nmaterial-specific impacts; such as how corn stover-based PLA offers reduced land-use intensity but incurs higher energy\nconversion costs. Another important and innovative aspect is the inclusion of marine plastic leakage within the life cycle\nassessment (LCA), quantifying both micro- and macro plastic impacts—an area often overlooked in traditional LCA studies.\nFurthermore, through macro-scale modelling of European plastic demand from 2020 to 2050, the authors show that even a\nfull substitution of fossil-based plastics with bio-based alternatives cannot align emissions with IPCC targets unless annual\ndemand is reduced by at least 3%. This demonstrates the critical importance of demand-side interventions alongside\nmaterial substitution.\nThe work is significant to the field of sustainability science, industrial ecology, and environmental policy. It offers a\ncomprehensive and systemic multi-scale LCA that incorporates climate, biodiversity, and human health dimensions, as well\nas plastic leakage—a considerable expansion beyond traditional climate-centric LCAs. Compared to existing literature such\nas that by Zheng & Suh (2019), Spierling et al. (2018), and Van Roijen & Miller, (2022), this study makes a novel contribution\nby integrating harmonized inventory datasets across multiple plastic types, modeling scale-up effects at a macro-regional\nlevel (Europe), applying advanced methods like Impact World+ and MarILCA, and connecting material-level performance\nwith systemic sustainability narratives. These advances make it a valuable reference for both academic researchers and\npolicymakers navigating the complex trade-offs in circular bioeconomy transitions.\nThe study’s conclusions are well supported by the evidence presented. Quantitative LCA results and scenario modelling (as\nshown in Figures 1–4) substantiate the claims made. The work demonstrates strength in its detailed modeling of polymer-\nspecific and feedstock-specific impacts, its use of validated databases such as ecoinvent 3.10, and its clear separation of\nimpact categories to avoid double counting. However, the study could be strengthened by including a deeper discussion of\nthird-generation feedstocks (e.g., algae or microbial biomass), which would help balance the current focus on first- and\nsecond-generation biomass. Additionally, sensitivity analyses on critical variables like land-use change or plastic leakage\nrates would enhance the robustness and policy relevance of the conclusions.\nThere are no critical flaws in the data analysis or interpretation that would prevent publication. However, a few areas warrant\nclarification or minor revision. For instance, the general conclusion that bio-based plastics increase ecosystem damage\ncould benefit from more nuanced framing, emphasizing that this outcome is particularly tied to first-generation feedstocks\nand current agricultural practices. Uncertainty quantification is another gap: the figures do not present confidence intervals or\nvariability ranges, and their inclusion would improve transparency. Furthermore, while the marine plastic leakage modeling\nis cutting-edge, it relies on relatively new frameworks (e.g., MarILCA) that are not yet standardized in LCA practice; this\nshould be more explicitly acknowledged as a limitation.\nMethodologically, the study is sound and meets the high standards of industrial ecology and environmental systems\nresearch. The use of ImpactWorld+ v2.0.1 provides international comparability, and the harmonization of life cycle inventory\ndata from various sources is carefully executed. The treatment of end-of-life pathways—including landfill, composting,\nincineration, and recycling—is transparent and well integrated. A key strength is the study’s high level of methodological\ntransparency and reproducibility. However, one limitation is that certain biodegradation pathways (e.g., composting or\nmarine environments) are modeled in simplified ways that do not yet include degradation kinetics or additive behavior—\nfuture research should aim to address this.\nThe Methods section provides enough detail for the work to be reproducible. It clearly describes functional units and system\nboundaries for material-, product-, and macro-level assessments. Procedures for inventory harmonization and classification\nare well explained, and assumptions regarding carbon sequestration credits for different bio-based polymers are explicitly\noutlined. Modeling of end-of-life impacts, including plastic leakage and biogenic emissions, is also thoroughly detailed. To\nfurther improve clarity, the authors could add a summary table of key parameters and assumptions in the main text and more\nexplicitly state the geographic and temporal boundaries for feedstock production scenarios. Overall, the methodological\ntransparency and attention to reproducibility make this study a reliable and impactful contribution to the field.\nSupplementary information\nThe supplementary information is scientifically robust, methodologically sound. It contains comprehensive dataset\nsupporting the main manuscript, including detailed life cycle inventory data for both conventional and bio-based plastic\npackaging materials, conversion and end-of-life parameters, and greenhouse gas (GHG) emission models under various\nwaste management scenarios such as landfilling, composting, and marine littering. Furthermore, the inclusion of\nmicroplastics-related impacts in the life cycle assessment (LCA) adds significant value, reflecting the authors' engagement\nwith emerging environmental concerns.\nHowever, several issues require attention to ensure clarity and consistency. The document currently contains numerous\ntypographical and formatting inconsistencies, particularly in the tables. Decimal separators are inconsistently applied (e.g.,\n“0,.46” instead of “0.46”), and some rows contain misaligned entries or ambiguous abbreviations. Tables such as\nSupplementary Table 1 and those detailing LCI data (Tables 3–6) are densely packed, which hampers readability.\nAdditionally, Supplementary Figures 1 and 2 are referenced but not clearly presented; high-resolution versions with detailed\ncaptions and proper axis labeling should be included. Moreover, key methodological assumptions—such as degradation\nrates over 100 years or the basis for methane generation and capture—are listed but not sufficiently explained. Providing\nshort narrative justifications or references for these assumptions would enhance transparency and reproducibility.\nAnother area that would benefit from clarification is the characterization of microplastics impacts. The parameters and impact\ncategories (e.g., PDF.m².yr/kg) used in Supplementary Table 10, while highly relevant, are technical and would be better\nunderstood if accompanied by brief explanatory notes or a glossary. Overall, while the scientific content is solid and\nvaluable, the presentation and formatting should be carefully revised to meet the standards.", "reviewer_2": "Overall\nThe authors conduct a harmonized LCA of fossil- and bio-based plastics. They find that while bioplastics reduce GHG\nemissions, they also increase ecosystem damage compared to fossil-based plastics, primarily due to land use and\nunmanaged emissions. The net outcome is found to be sensitive to feedstock origin and waste treatment. When evaluating\nthe results at the scale of Europe, the authors argue that, in part due to these tradeoffs, complete substitution with bio-based\nplastics cannot offset the impacts of continued plastic demand growth. In closing, the authors advocate for more demand-\nside measures to mitigate trade-offs.\nOverall, this is an interesting paper with useful findings. This field is currently receiving a lot of attention, and more studies\nthat assess tradeoffs, as opposed to simply the impacts of different kinds of materials are needed. The authors incorporate\nboth an assessment of the land impacts of bioplastics, as well as end of life treatments, to reach their conclusions. Given the\nneed for more work to address these critical questions, as well as the authors’ thoughtful approach, I recommend this paper\nfor publication.\nMy main concern with the paper is related to uncertainty, and how uncertainty affects the claims made in the paper. Due to\nthe complexity of the scenarios and uncertainties in what we know, the authors rely on significant assumptions to reach their\nfinal conclusion. To some extent, this is unavoidable. Given that this paper is submitted in a multidisciplinary journal and\nmakes claims about plastics policy, however, I would encourage the authors to think beyond their initial findings and provide\ngreater insight into what assumptions underlie their claims. In doing so, showing the reader how their conclusions are\nderived from the data, the assumptions within that data, and what uncertainties might be overlooked in making these larger\nclaims. I will discuss this in more detail at the end of the review.\nLine-by-Line\nOne minor clarification comment: On line 1 the authors cite a statistic about packaging accounting for 40% of total\nproduction, but in line 25, they cite this as both “in EU-28” and “global plastic production”. Clarity on what this 40%\nrepresents would be appreciated.\nLine 32 - The authors mention entanglement and ingestion as a plastic impact, but this paper does not evaluate macroplastic\nimpacts (line 422). Throughout the paper, the authors could be clearer on what is measured and what is not. This needn’t go\nin the introduction, and could go in the methods or supplementary, but given the policy-relevance, more detail on what is not\nevaluated is warranted.\nLine 36 (and throughout the introduction) – Consider specifying what kind of bio-based plastics are being discussed. Drop-in\nbioplastics have different uses, impacts, and end-of-life than compostable bio-based plastics. Describing these factors and\ndifferences between bioplastic types may be useful to the reader.\nLine 43 - Similar to the above comment, the authors could also mention composting infrastructure, as not all bio-based\nplastics would likely enter recycling infrastructure.\nLine 46 - Second-generation feedstocks could also have land-use impacts, depending on the system of production. If the\nfeedstock displaces a different land-use, land-use change could still occur no matter the feedstock being utilized. (e.g.\nhttps://doi.org/10.1111/gcbb.70024)\nLine 83-88 - What specifically is being measured to quantify land-use impacts? The authors use “land footprint”, “land\noccupation”, and “land-use intensity”. Is the measurement simply the total land-use required to produce the target quantity of\nbio-based plastic? As such, does this exclude potential land-use displacement effects? Given that one of the main trade-offs\ndiscussed in this paper is derived from the land-use impacts, greater detail should be provided that discusses 1. How\nspecifically land-use is calculated, and 2. How this value is converted to the final “impact” value.\nLine 91 - Similar to the above point, is the decrease in land-use impact from the shift to second-generation feedstocks simply\nbecause these are assumed to be residues and not have a land-use impact? What is the assumption about how these\nsecond-generation feedstocks will be produced?\nLine 96-111 - In evaluating the impact of plastic leakage, if my understanding is correct, the authors rely on several major\nassumptions about the impacts of plastic waste in the environment, including a degradation model and a microplastic\nexposure model. These are both areas of study with significant uncertainty, and much of the research is theoretical modeling\nand not experimental. While this doesn’t invalidate this research, greater attention should be given to describing some of\nthese assumptions within the manuscript. Given the potential uncertainties, the authors are potentially propagating\nsignificant uncertainty throughout the calculator to arrive at their final estimate. Additionally, because of data limitations, the\nauthors do not include measurement of macroplastics or human toxicity, both of which could be impactful. Again, while this\ndoes not detract from the study, a discussion of what is included/not included somewhere in the paper is warranted.\nLine 112 - In this paragraph, it seems that direct human toxicity-related impacts, in both the use phase and the end-of-\nlife/leakage phase are not included in this study. Greater clarification on what is and is not included would be appreciated.\nLine 218 – This sentence is confusing, as it seemingly relates a PDF*m2*yr value to a total amount of arable land. Is this\nsaying the impact would be that significant? Also, while this may seem high, reaching 100% bio-based plastic by 2050 is\nextremely unlikely. This is a useful heuristic, but the authors should qualify the likelihood of reaching this value, as well as\nwith the fact that a lot of the land production would not occur in the EU.\nLine 222 - This paragraph, demonstrating overall demand growth in plastic has a larger impact than the different types of\ninterventions, is useful. The takeaway, that the demand impact is greater than the difference in impact between types of\nplastic, could be clarified and emphasized more in this section.\nLine 243 – The authors claim that ‘bio-based materials offer a promising route to decarbonize the plastics sector” but is this\nactually established in the paper? It seems the central argument of the paper is that there are potentially serious tradeoffs for\nutilizing bio-based plastics as a solution.\nLine 256 - Is this the recycling rate for packaging? It seems high for the global level.\nLine 328 – What land-use impacts are accounted for? Are emissions from land-use change evaluated?\nFinally, two papers that may be useful:\nhttps://doi.org/10.1038/s41893-024-01492-7\nhttps://doi.org/10.1038/s41586-022-05422-5\nPlastic sustainability and plastics policy\nThe plastic crisis, and how to address it, is a high-profile topic. Because this is a topic with policy-relevance, and the authors\nmake claims about policy goals, the authors should be more forthcoming about the basis on which they are making those\nclaims. This is especially important since this is a multidisciplinary journal, thus not specific to the LCA field and will be read\nby people with an interest and expertise in the topic, but not necessarily the methodology.\nBecause LCA studies build on other studies, one potential risk is propagating errors and uncertainties, and thereby affirming\nassumptions that are already codified in the study.\nFor example, the authors argue that second-generation feedstocks “reduce the land-use contribution to ecosystem damage”\n(Line 91), as the example second-generation biomass is a residue, corn stover. But this may not necessarily be the case. A\ndedicated energy crop that requires agricultural land may require dedicated land for production, especially if it is less land-\nuse efficient than first generation crops. Now, in many circumstances the assumption in the study probably holds. To be\nmore valuable to decisionmakers, however, the authors should be more forthright in describing these assumptions so that it\nis clearer to the reader under what circumstances the study applies, and does not apply. For example, the authors could\nspecify that corn stover is the example second-generation feedstock, and discuss ranges of potential impact for other\nfeedstocks where data permits. This could go in a supplementary table.\nSimilarly, the authors could provide some estimates of potential variation in the study. The authors mention different impacts\nbetween sugarcane production in Brazil and corn production in the United States. Demonstrating the magnitude of this\nvariation is useful for establishing the magnitude of the impact compared to the uncertainty and range of variation within the\nscenario.\nRelatedly, the authors include microplastic impacts, but exclude macroplastic impacts when discussing impacts of plastic\nleakage in the environment. It is entirely possible that the impact of what is measured (microplastic impacts) is less than\nwhat is not measured (macroplastic impacts). However, because of the uncertainty in this assessment and limited data, this\nis simply unknown. The authors should be more forthcoming in what is known and not known to again, help the reader in\nevaluating how this study applies in other contexts. Additionally, when there are estimates with significant uncertainties,\nsuch as in the impacts of microplastics on biota and microplastic degradation rates, the authors should acknowledge the\nuncertainty.\nWhen it is possible to provide measures of uncertainty, or ranges of potential values, I would encourage the authors to do so\nto demonstrate the size of uncertainty relative to the magnitude of differences between the scenarios.\nWhile true uncertainty estimates may not be possible, I would at the very least encourage the authors to describe more of the\nassumptions within the study – and key uncertainties therein – so that it is easier to distinguish under what circumstances\nthe assumptions do, and don’t, hold. The authors could provide more of this detail in text in the supplementary. Another\noption would be to create a table of key findings (i.e. for the various categories of impact, what are the largest contributors to\nthe impact) and the key assumptions that underlie those findings. The authors could also consider making Figure 3a and 3b\nmore useful in the life-cycle diagram by illustrating how and where in the plastics lifecycle these impacts arise, thereby\nshowing which impacts are captured and which are not."}, "author_response": "Point-by-point response to the reviewers’ comments\nManuscript ID.: NCOMMS-25-48982A\nReviewer #2:\nChanges in\nN° Reviewer’s comments Authors’ responses the\nmanuscript\n2.1 Line 1: The authors cite a statistic about The statement has been clarified to specify that the 40% L.9\npackaging accounting for 40% of total share refers to European plastic production. A supporting L.24-25\nproduction, but in line 25, they cite this as both reference has been added to substantiate this figure,\n“in EU-28” and “global plastic production”. particularly for the share of packaging used for food\nClarity on what this 40% represents would be applications.\nappreciated.\n2.2 Line 32: The authors mention entanglement We have specified in the Results section what is actually L.128-133\nand ingestion as a plastic impact, but this paper being evaluated. We have clarified in the Methods L.509-516\ndoes not evaluate macroplastic impacts (line section that our assessment focuses exclusively on the\n422). Throughout the paper, the authors could physical effects of microplastics on marine biota.\nbe clearer on what is measured and what is Macroplastic-related effects (e.g., entanglement) and\nnot. This needn’t go in the introduction, and additive toxicity are excluded due to the current lack of\ncould go in the methods or supplementary, but robust characterization factors.\ngiven the policy-relevance, more detail on what\nis not evaluated is warranted.\n2.3 Line 36: Consider specifying what kind of bio- We have expanded the introduction to distinguish L.36-40\nbased plastics are being discussed. Drop-in between the two main categories of bio-based plastics:\nbioplastics have different uses, impacts, and “drop-in” bio-plastics (e.g., bio-PE, bio-PET) and\nend-of-life than compostable bio-based compostable bio-plastics (e.g., PLA, PHA). \nplastics. Describing these factors and\ndifferences between bioplastic types may be\nuseful to the reader\n2.4 Line 43: Similar to the above comment, the The revised paragraph on bio-based plastic types now L.46-48\nauthors could also mention composting explicitly introduces industrial composting as the\ninfrastructure, as not all bio-based plastics corresponding end-of-life pathway for compostable\nwould likely enter recycling infrastructure.\nplastics, in parallel with recycling for drop-in plastics.\n1\n2.5 Line 46: Second-generation feedstocks could We have clarified that the sustainability of second- L. 52-56\nalso have land-use impacts, depending on the generation biomass depends strongly on the local\nsystem of production. If the feedstock displaces production system and residue management practices.\na different land-use, land-use change could still The revised text now specifies that indirect land-use\noccur no matter the feedstock being utilized. change can still occur when the sourcing of non-edible\nbiomass displaces existing land uses or alters soil\nfunctions.\n2.6 Line 83-88: What specifically is being We now clarify that two LCI land-use flows are modeled: L.106-109\nmeasured to quantify land-use impacts? The land occupation (m²·yr) and land transformation (m²). For L.192-194\nauthors use “land footprint”, “land occupation”, each product system we (i) sum these flows to obtain the L. 471-485\nand “land-use intensity”. Is the measurement two midpoint indicators (occupation over time;\nsimply the total land-use required to produce transformed area), and then (ii) convert them to endpoint\nthe target quantity of bio-based plastic? As ecosystem damage using the Impact World+\nsuch, does this exclude potential land-use characterization factors of de Baan et al. (2013), which\ndisplacement effects? Given that one of the link each land-use type and ecoregion to PDF·m²·yr\nmain trade-offs discussed in this paper is (potentially disappeared fraction of species × area ×\nderived from the land-use impacts, greater time). This harmonized approach replaces the earlier\ndetail should be provided that discusses 1. How wording (“land footprint”, “land-use intensity”). Indirect\nspecifically land-use is calculated, and 2. How land-use change (ILUC) is not included, as it is a\nthis value is converted to the final “impact” consequential effect requiring market-mediated\nvalue. modeling; this limitation is noted in the Methods section.\n2.7 Line 91: Similar to the above point, is the The lower land-use impacts associated with second- L.115-123\ndecrease in land-use impact from the shift to generation feedstocks arise from the economic allocation\nsecond-generation feedstocks simply because applied between maize grain and corn stover in the life-\nthese are assumed to be residues and not have cycle inventories. As corn stover has a relatively low\na land-use impact? What is the assumption market value, a smaller share of the land occupation and\nabout how these second-generation feedstocks transformation burdens is attributed to it, resulting in\nwill be produced? reduced ecosystem damage per kilogram of polymer.\nThis assumption reflects an attributional perspective, in\nwhich residues are treated as co-products of existing\nagricultural systems. Indirect land-use change (ILUC)\neffects, which would require consequential modeling of\nmarket-mediated land expansion, are not included in this\nstudy and are acknowledged as a limitation in the\nMethods section.\n2\n2.8 Line 96-111: In evaluating the impact of plastic The reviewer is fully correct that our assessment of L.486-531\nleakage, if my understanding is correct, the plastic leakage relies on several assumptions regarding L.550-552\nauthors rely on several major assumptions degradation, fate, and effect models. We have therefore L.128-146\nabout the impacts of plastic waste in the revised and expanded the methodology accordingly. The Supplementary\nenvironment, including a degradation model Methods section now incorporates the latest fig. 2\nand a microplastic exposure model. These are developments from the MariLCA framework, including Supplementary\nboth are areas of study with significant uncertainty, updated characterization factors that account for table 10\nand much of the research is theoretical microplastic effects in both the water column and marine\nmodeling and not experimental. While this sediments, as well as a clearer description of the loss–\ndoesn’t invalidate this research, greater fate–effect modeling approach. These revisions further\nattention should be given to describing some of distinguish between primary microplastic emissions (from\nthese assumptions within the manuscript. pellet losses) and secondary ones (arising from\nGiven the potential uncertainties, the authors macroplastic fragmentation), as well as between short-\nare potentially propagating significant and long-term impacts based on degradation dynamics.\nuncertainty throughout the calculator to arrive Two contrasting high- and low-income country scenarios\nat their final estimate. Additionally, because of were further introduced to represent differences in end-\ndata limitations, the authors do not include of-life mismanagement rates.\nmeasurement of macroplastics or human\ntoxicity, both of which could be impactful. A dedicated uncertainty analysis was performed using\nAgain, while this does not detract from the Monte Carlo simulations (5 000 iterations) on the\nstudy, a discussion of what is included/not microplastic characterization factors.\nincluded somewhere in the paper is warranted.\nFinally, we note that human-health effects related to\nchemical additives or microplastic exposure are not yet\nincluded, owing to the current lack of robust\ncharacterization factors. This limitation is now explicitly\nacknowledged in the Methods and Discussion sections.\n2.9 Line 112: In this paragraph, it seems that direct The reviewer is right. We clarified that direct human L.149-152\nhuman toxicity-related impacts, in both the use exposure to microplastics or chemical additives during\nphase and the end-of-life/leakage phase are the use and leakage phases is not included in\nnot included in this study. Greater clarification conventional attributional LCAs, as no validated human-\non what is and is not included would be health fate–exposure–effect characterization models are\nappreciate currently available for plastics.\n2.10 Line 218: This sentence is confusing, as it The PDF·m²·yr metric was clarified as an endpoint L. 263-272\nseemingly relates a PDF*m2*yr value to a total indicator representing the total potential loss of species\namount of arable land. Is this saying the impact from all contributing environmental stressors. We also\nwould be that significant? Also, while this may specified that land-use pressures (occupation and\n3\nseem high, reaching 100% bio-based plastic by transformation) are the main drivers of this aggregated\n2050 is extremely unlikely. This is a useful biodiversity damage. The comparison to European\nheuristic, but the authors should qualify the arable land was retained as an illustrative benchmark to\nlikelihood of reaching this value, as well as convey magnitude, while acknowledging the scenario’s\nwith the fact that a lot of the land production would limited feasibility and global production scope.\nnot occur in the EU.\n2.11 Line 222: This paragraph, demonstrating A paragraph was added to clarify that demand growth L. 280-282\noverall demand growth in plastic has a larger remains the dominant driver of environmental impacts\nimpact than the different types of interventions, and to emphasize that substitution strategies must be\nis useful. The takeaway, that the demand coordinated with broader demand-side policies and\nimpact is greater than the difference in impact material sufficiency measures.\nbetween types of plastic, could be clarified and\nemphasized more in this section.\n2.12 Line 243: The authors claim that ‘bio-based The paragraph has been reformulated to better reflect L. 295-297\nmaterials offer a promising route to the findings. It now explicitly acknowledges that while\ndecarbonize the plastics sector” but is this bio-based materials can contribute to decarbonization,\nactually established in the paper? It seems the this comes with significant trade-offs in terms of\ncentral argument of the paper is that there are ecosystem quality, thereby clarifying that substitution\npotentially serious tradeoffs for utilizing bio- alone cannot ensure sustainable transitions.\nbased plastics as a solution.\n2.13 Line 256: Is this the recycling rate for The recycling rate values (23% and 94%) are taken from L.314\npackaging? It seems high for the global level Bachmann et al. (2023). The lower value represents the\ncurrent global average recycling rate for plastic\npackaging, while the upper value corresponds to a\ntheoretical technical maximum derived from material flow\noptimization under ideal collection and processing\nconditions. These values are used to illustrate the\npotential mitigation range rather than to represent\nobserved global recycling rates.\n2.14 Line 328: What land-use impacts are As clarified earlier (response to comment 2.1), indirect L.471-485\naccounted for? Are emissions from land-use land-use change (ILUC) is not included in this study.\nchange evaluated? ILUC represents a consequential, market-mediated\neffect that falls outside the scope of attributional LCA.\nOnly direct land occupation and transformation are\nmodeled, as detailed in the Methods section. This\nlimitation is explicitly acknowledged in the Methods.\n4\n2.15 Finally, two papers that may be useful: We thank the reviewer for recommending these two L.59-80\nhttps://doi.org/10.1038/s41893-024-01492-7 references. Although we were aware of their existence\nhttps://doi.org/10.1038/s41586-022-05422-5 and had built part of our rationale upon their findings,\nthey had been omitted from our citation list. We have\ntherefore slightly restructured the introduction to include\nthem and to clarify our positions.\nWe have also improved the presentation of the study’s\nobjectives, scope, and limitations.\n2.16 The authors argue that second-generation We understand the reviewer’s concern, which relates to L.106-109\nfeedstocks “reduce the land-use contribution to potential land-use competition arising when second- L.471-485\necosystem damage” (Line 91), as the example generation feedstocks are sourced from dedicated\nsecond-generation biomass is a residue, corn energy crops rather than residues. In this study, second-\nstover. But this may not necessarily be the generation biomass is represented by corn stover, which\ncase. A dedicated energy crop that requires is modeled as an agricultural residue and therefore does\nagricultural land may require dedicated land for not induce additional land occupation. Indirect land-use\nproduction, especially if it is less land-use change (ILUC) effects from dedicated crops are not\nefficient than first generation crops. Now, in included, as they fall under consequential modeling\nmany circumstances the assumption in the beyond the scope of this attributional LCA. This limitation\nstudy probably holds. To be more valuable to and its implications are now explicitly mentioned in the\ndecisionmakers, however, the authors should Methods.\nbe more forthright in describing these\nassumptions so that it is clearer to the reader\nunder what circumstances the study applies,\nand does not apply. For example, the authors\ncould specify that corn stover is the example\nsecond-generation feedstock, and discuss\nranges of potential impact for other\nfeedstocks where data permits. This could go in a\nsupplementary table.\n2.17 Similarly, the authors could provide some We thank the reviewer for this valuable suggestion. To L.211-234\nestimates of potential variation in the study. better capture variability in production conditions, Figure\nThe authors mention different impacts between 3 has been revised.\nsugarcane production in Brazil and corn Panel c now focuses on climate change and ecosystem\nproduction in the United States. Demonstrating quality impacts, allowing the introduction of two new\nthe magnitude of this variation is useful for scenarios (PLA-low and PLA-high) that explicitly\nestablishing the magnitude of the impact represent the range of possible outcomes for agricultural\nlocation and energy decarbonization.\n5\ncompared to the uncertainty and range of These scenarios were designed to reflect the differences\nvariation within the scenario. in impact observed between sugarcane production in\nBrazil and corn production in the United States, as\nmentioned in the text, thereby illustrating the potential\nvariation within realistic global production conditions.\nThe results show that while production geography and\nenergy intensity affect total impacts, the feedstock type\nremains the dominant driver, with larger differences\nbetween first- and second-generation feedstocks than\nbetween regional settings.\nThis addition provides a transparent quantification of\nvariability and situates it relative to the underlying\nuncertainty of the results.\n2.18 Relatedly, the authors include microplastic Please refer to comment 2.8 L.486-531\nimpacts, but exclude macroplastic impacts L.550-552\nwhen discussing impacts of plastic leakage in L.128-146\nthe environment. It is entirely possible that the Supplementary\nimpact of what is measured (microplastic fig. 2\nimpacts) is less than what is not measured Supplementary\n(macroplastic impacts). However, because of table 10\nthe uncertainty in this assessment and limited\ndata, this is simply unknown. The authors\nshould be more forthcoming in what is known\nand not known to again, help the reader in\nevaluating how this study applies in other\ncontexts. Additionally, when there are\nestimates with significant uncertainties,\nsuch as in the impacts of microplastics on biota and\nmicroplastic degradation rates, the authors\nshould acknowledge the uncertainty.\n2.19 When it is possible to provide measures of We agree with the reviewer. To quantify the magnitude L.86-90\nuncertainty, or ranges of potential values, I of uncertainty relative to inter-scenario differences, we L.533-549\nwould encourage the authors to do so to performed Monte Carlo simulations (1,000 iterations) to\ndemonstrate the size of uncertainty relative to propagate uncertainty in inventory data for polymer\nthe magnitude of differences between the production. The 5th–95th percentile ranges are now\nscenarios. displayed as error bars in Figure 1.\nUncertainties were derived from the ecoinvent 3.10\n6\npedigree matrix, complemented by quality assessments\nof the foreground agricultural and industrial datasets\ncompiled in this study. This analysis captures the\nvariability associated with life cycle inventory data only,\nas standard deviations are not provided for current LCIA\ncharacterization factors.\nBecause of the numerical instability introduced by\nfreshwater flows in ecoinvent (which can alternate\nbetween positive and negative values during Monte\nCarlo sampling), uncertainty ranges could not be robustly\ncomputed for Ecosystem Quality and Water Scarcity.\nThese categories are therefore reported as deterministic\naverages to ensure consistency. This limitation is noted\nin the Methods section.\n2.20 While true uncertainty estimates may not be We thank the reviewer for this constructive suggestion.\npossible, I would at the very least encourage We have strengthened the discussion of uncertainty and\nthe authors to describe more of the assumptions throughout the revised manuscript. Figure 1\nassumptions within the study – and key now explicitly represents uncertainty ranges derived from\nuncertainties therein – so that it is easier to the Monte Carlo analysis of inventory data, while Figure\ndistinguish under what circumstances the 3 explores scenario variability across different feedstock,\nassumptions do, and don’t, hold. The authors and end-of-life conditions. Together, these elements aim\ncould provide more of this detail in text in the to clarify how model assumptions influence the results\nsupplementary. Another option would be to and their robustness. If the reviewer and editor consider\ncreate a table of key findings (i.e. for the it useful, we would be glad to include in a subsequent\nvarious categories of impact, what are the revision a supplementary table summarizing the key\nlargest contributors to the impact) and the key assumptions and dominant contributors for each impact\nassumptions that underlie those findings. The category.\nauthors could also consider making Figure 3a\nand 3b more useful in the life-cycle diagram by\nillustrating how and where in the plastics\nlifecycle these impacts arise, thereby showing\nwhich impacts are captured and which are not.\nChanges in the\nN° Reviewer’s comment Authors’ response\nmanuscript\n1.1 The study could be strengthened by including We agree that third-generation feedstocks, such as L.77-80\na deeper discussion of third-generation algal or microbial biomass, represent an emerging L.300-307\nfeedstocks (e.g., algae or microbial biomass), and promising pathway for bio- based plastic\nwhich would help balance the current focus on production. But given this limited data availability and\nfirst- and second-generation biomass. the current uncertainty surrounding their large-scale\ndeployment, we chose to focus our assessment on\nfirst- and second-generation feedstocks, for which\nmore robust and comparable datasets exist. We\nhave clarified the scope in the revised manuscript.\n1.2 Additionally, sensitivity analyses on critical We agree with the reviewer on the relevance of L.210-234\nvariables like land-use change or plastic conducting deeper sensitivity analyses. Figure 3 has L.141-146\nleakage rates would enhance the robustness been revised to include a sensitivity analysis on the\nand policy relevance of the conclusions. geographical location of agricultural production\nphases. Please refer to comment 2.17 for further\ndetails.\nRegarding plastic leakage, additional loss scenarios\nhave also been incorporated into Figure 2. Two\ncontrasting cases representing high- and low-income\ncountries were introduced to reflect differences in\nend-of-life mismanagement rates.\n1.3 However, a few areas warrant clarification or We have slightly revised the conclusion paragraph to L.300-307\nminor revision. For instance, the general better emphasize the differences between feedstock\nconclusion that bio-based plastics increase generations and their respective environmental\necosystem damage could benefit from more implications.\nnuanced framing, emphasizing that this\noutcome is particularly tied to first-generation\nfeedstocks and current agricultural practices.\n1.4 Uncertainty quantification is another gap: the We added 5th–95th percentile error bars in Fig. 1 L.86-90\nfigures do not present confidence intervals or from a 1,000-draw Monte Carlo on inventory data, L.533-549\nvariability ranges, and their inclusion would improving transparency on variability\nincrease transparency (Please refer to comment 2.19)\n8\n1.5 Furthermore, while the marine plastic leakage We now explicitly acknowledge that MarILCA is an L.486-531\nmodeling is cutting-edge, it relies on relatively emerging framework in LCA practice and we flag this L.550-552\nnew frameworks (e.g., MarILCA) that are not as a limitation where plastic-leakage impacts are L.129-147\nyet standardized in LCA practice; this should modeled Supplementary fig.\nbe more explicitly acknowledged as a (Please refer to comment 2.8) 2\nlimitation. Supplementary\ntable 10\n1.6 However, one limitation is that certain We fully agree with the reviewer on the importance of L.420-432\nbiodegradation pathways (e.g., composting or better integrating the degradation dynamics of Supplementary\nmarine environments) are modeled in biodegradable plastics at end of life. Although kinetic table 7-9\nsimplified ways that do not yet include data for landfill and composting conditions have not\ndegradation kinetics or additive behavior— yet been implemented, we refined the biodegradation\nfuture research should aim to address this. percentages based on a new source that\ndifferentiates rates by polymer type (Afshar et al.,\n2024). The degradation of debris reaching the\nmarine environment was also updated using kinetic\ndata reported by Corella-Puertas et al. 2023.\nCorresponding GHG emission from these\nbiodegradation processes were revised accordingly,\nleading to adjustments in the results presented in\nFigure 3.\nWe have provided a more detailed description of the\nunderlying assumptions in the Methods section.\n1.7 To further improve clarity, the authors could We clarified geographic boundaries for the feedstock Supplementary\nadd a summary table of key parameters and production (for PLA scenarios) in Supplementary table 11\nassumptions in the main text and more Table 11 and explained the assumptions in the Fig. 3\nexplicitly state the geographic and temporal caption.\nboundaries for feedstock production scenarios. If helpful, we can add a concise assumptions\nsummary table to the main text in a subsequent\nround.\n(please refer to comment 2.20)\n1.8 However, several issues require attention to We thank the reviewer for noting these formatting Supplementary\nensure clarity and consistency. The document inconsistencies, which have now been corrected. information\ncurrently contains numerous typographical and The tables have been reorganized for improved\nformatting inconsistencies, particularly in the readability, and typographical and formatting errors\ntables. Decimal separators are inconsistently have been addressed.\n9\napplied (e.g., “0,.46” instead of “0.46”), and\nsome rows contain misaligned entries or\nambiguous abbreviations. Tables such as\nSupplementary Table 1 and those detailing LCI\ndata (Tables 3–6) are densely packed, which\nhampers readability.\n1.9 Additionally, Supplementary Figures 1 and 2 The resolution of the graphs in the main text has All figures\nare referenced but not clearly presented; high- been generally improved wherever possible. In the\nresolution versions with detailed captions and Supplementary Information, figures and tables have\nproper axis labeling should be included. been reorganized and accompanied by more\ndetailed captions where necessary.\n1.10 Moreover, key methodological assumptions— We agree that key modeling assumptions such as L.420-432\nsuch as degradation rates over 100 years or degradation rates and methane generation needed Supplementary\nthe basis for methane generation and clearer justification. The revised Methods (GHG table 7-9\ncapture—are listed but not sufficiently emissions from incineration…) section now briefly\nexplained. Providing short narrative explains the basis and references for these\njustifications or references for these parameters, and Supplementary Tables 7–9 indicate\nassumptions would enhance transparency and the data sources used for landfill, composting, and\nreproducibility. marine degradation models.\n1.11 Another area that would benefit from To improve the interpretation of the parameters Supplementary\nclarification is the characterization of presented in Supplementary Table 10, a brief table 10\nmicroplastics impacts. The parameters and description has been added below the table,\nimpact categories (e.g., PDF.m².yr/kg) used in specifying in particular the meaning of the impact\nSupplementary Table 10, while highly relevant, are technical and would be better units used. More broadly, the general methodology\nunderstood if accompanied by brief explanatory for assessing plastic pollution has been further\nnotes or a glossary. detailed in the Methods section.\n10"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am satisfied with the revision. The MS can be accepted for the publication.", "reviewer_2": "The authors have sufficiently addressed my comments on the manuscript. In particular, I appreciate the time and effort that\nwent into the uncertainty analysis and inclusion of greater methodological details."}, "author_response": ""}]

Decision letter