Towards sustainable plastics: a sustainable chemistry assessment

Abstract

The expansion of plastic production and consumption has exacerbated the triple planetary crisis of climate change, biodiversity loss and pollution, posing severe threats to global ecosystems and human health. The ongoing global plastics treaty negotiations underscore the urgent need for systematic transformation to drive the plastics industry towards sustainable production and consumption. This study develops an integrated assessment framework from a sustainable chemistry perspective, quantifying the environmental impacts of hazardous additives and polymers using Chemical Footprint (ChF) and Greenhouse Gas Emission (GHG) and the economic effects of plastics circularity via Waste Management Input (WMI). A case study of China's plastics industry reveals a decline in the Sustainable Chemistry Index (SCI) from 0.679 to 0.519 between 2000 and 2011, primarily driven by the expansion of plastic production and consumption. However, between 2011 and 2020, reduced net imports of plastic waste and increased recycling rates slowed the decline, with the SCI reaching 0.492 in 2020. Scenario analysis (2021–2060) is conducted to explore the impact and interactions of four key plastic management policies. Among them, reducing plastic consumption (Scenario 2) has the strongest effect on sustainable chemistry, increasing the SCI to 0.636 by 2060. Conversely, promoting recycling alone (Scenario 5) has a limited impact, with the SCI stabilizing at 0.47–0.50, due to the unintentional recycling of hazardous additives. The increase of ChF offsets the benefits of reduced GHG and WMI. Notably, combining promoted recycling with strict additive control (Scenario 7) significantly improves the SCI, reaching 0.631 in 2060, which is comparable to Scenario 2, indicating a similar policy effect. This study presents a quantitative framework for assessing the performance of sustainable chemistry of the plastics industry and reveals key policy synergies, offering scientific insights to support effective policymaking and global plastics treaty negotiations.

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Green foundation

1. This study develops a quantitative framework for assessing sustainable chemistry in the plastics industry and reveals key policy synergies, offering scientific insights to support effective policymaking and global plastics treaty negotiations.

2. This study quantifies the performance of sustainable chemistry in the plastics industry across three pivotal dimensions with three indicators, i.e., substitution and safe management of hazardous additives, promotion of plastics circularity, and effective control of greenhouse gas emissions, and conducted a case study of China's plastics industry.

3. To provide a more accurate assessment of the performance of sustainable chemistry in the plastics industry, more research is needed on the recycling process, as well as on the use and environmental emissions of plastic additives in the future.

Introduction

The expansion of plastic production and consumption has intensified the triple planetary crisis of climate change, biodiversity loss and pollution and has introduced a significant threat to global human health and ecosystems.^1–5 In response to these unprecedented challenges in the global governance of plastic pollution, a historic resolution was adopted to develop an international legally binding instrument on plastic pollution (i.e., the forthcoming global plastics treaty) at the resumed fifth session of the United Nations Environment Assembly (UNEA-5) in 2022. It underscores the urgent need to address plastic pollution throughout its life cycle and to enhance industry-wide sustainability.⁵

Since plastics originate from the chemical industry, their sustainability fundamentally depends on the principles of green and sustainable chemistry.⁶ The concept of sustainable chemistry has evolved beyond the traditional green chemistry focus on minimizing the environmental and health impacts of hazardous chemicals and incorporates ecosystem, climate, and socio-economic dimensions.^6,7 Plastics encompass over 16 000 distinct chemicals, with more than one quarter classified as chemicals of concern.⁸ Plastic additives, which constitute roughly 4% of plastics, are typically not chemically bonded to the polymer matrix and could therefore be released into the environment over time, potentially causing adverse environmental and health effects.^8–11 Moreover, less than 8% of the global plastic waste historically generated has been recycled.¹² The current linear plastic economy of production, use and disposal results in non-negligible amounts of plastic leakage, causing microplastic pollution in the environment, evidenced by 22 million tons (Mt) of plastic waste entering the environment in 2019, including 6.1 Mt contaminating water bodies on a global scale.^13,14 Additionally, the plastics industry is a significant contributor to global greenhouse gas emission.^15,16 In 2020, the global plastics industry emitted a total of 2.2 gigatonnes (Gt) of CO₂, representing approximately 7% of global energy-related CO₂ emissions, with further increases expected in the future.¹⁵ Therefore, the sustainable chemistry in the plastics industry includes at least three pivotal dimensions, i.e., substitution and safe management of hazardous additives, promotion of plastics circularity, and effective control of greenhouse gas emission.

Despite its critical role in mitigating plastic pollution, the current sustainable chemistry framework for the plastics industry is hindered by significant methodological gaps. Aside from conceptual frameworks,^17,18 existing studies typically focus on the environmental impact assessment of individual dimensions.^19–24 Few studies that have conducted multi-dimensional environmental impact assessments are limited to a single component (e.g. polymers or additives) for the plastics industry.^25–27 These studies fail to conduct a quantitative and systematic assessment for polymers, additives, and their interactions throughout the life cycle of plastics due to the lack of a comprehensive set of quantitative indicators and a robust assessment framework for sustainable chemistry. This limits the overall understanding of the performance of sustainable chemistry in the plastics industry and constrains the ability to improve sustainable practices within the industry.

This study seeks to bridge this critical gap by developing an assessment framework that explicitly addresses these key dimensions of sustainable chemistry. A pilot study, using China as a case study, is conducted. It is expected that the study could facilitate the identification of priority areas for plastic pollution control and sustainable development and also act as a preliminary exploration for the development of a sustainable chemistry assessment methodology.

Methodology

Scope and system boundaries

This study divides the life cycle of plastics into three stages: production (LC1), in-use (LC2), and waste management (LC3). The production stage includes the production, export and import of polymers, additives and plastic products. The waste management stage includes the import and export of plastic waste and the disposal of domestic waste. In the production stage, the plastics are classified into six polymers: polyethylene (PE, P1), polypropylene (PP, P2), polyvinyl chloride (PVC, P3), polystyrene (PS, P4), polyethylene glycol terephthalate (PET, P5) and others (P6). The additives are classified into nine functional categories: stabilizers (F1), heat stabilizers (F2), antioxidants (F3), pigments (F4), antistatic agents (F5), flame retardants (F6), plasticizers (F7), biocides (F8) and other processing aids (F9). In the in-use stage, the plastics are classified into seven sectors: packaging (U1), building and construction (B & C) (U2), agriculture (U3), electronics (U4), textile (U5), household (U6) and others (U7). In the waste management stage, five waste management methods are included for the disposal of domestic waste: recycling (WM1), landfill (WM2), incineration (WM3), open dumping (WM4) and littering (WM5). Since mechanical recycling is the most commonly used plastic recycling method, and the global chemical recycling rate in the plastics industry is less than 1% at present, only the mechanical recycling method is considered in this study.^28–31 The recycling rate in this study actually means the collection rate for mechanical recycling, and a sorting yield is used to calculate the actual yield of recycled materials, as well as the unintentional recovery of additives. Landfill (WM2) and incineration (WM3) in this study mean controlled municipal landfill and municipal incineration with energy recovery, while open dumping (WM4) and littering (WM5) are both considered as mismanaged methods for waste disposal.^20,32–34 Two environmental receptors are included: air (E1) and water (E2).³⁵

The case study of China's plastics industry covers the period from 2000 to 2020 to evaluate the historical performance in sustainable chemistry and extends from 2021 to 2060 for scenario analysis. The spatial scope is limited to mainland China, excluding Hong Kong, Macau, and Taiwan, due to the lack of relevant data.

Model construction

In this study, sustainable chemistry encompasses three dimensions of the plastics industry, including impacts of hazardous chemicals, circularity of products and impacts on climate change. The three negative indicators of chemical footprint (ChF), waste management input (WMI) and greenhouse gas emission (GHG) are employed in the construction of a set of sustainable chemistry assessment framework, as illustrated in Fig. 1.

Fig. 1 Sustainable chemistry assessment framework.

Reliable and comprehensive environmental assessments are best supported by the use of life cycle assessment (LCA).³⁶ Simplified metrics, such as the E factor and the Process Mass Intensity (PMI),^37–40 focus primarily on the resource consumption and environmental impact of individual production processes. Technoeconomic analysis (TEA), on the other hand, emphasizes the technical feasibility and economic benefits of processes but overlooks environmental and social impacts.^41,42 In contrast, LCA provides a holistic approach by tracking and quantitatively analyzing every stage of a product's life cycle, enabling the identification of key stages and processes. Therefore, this study conducted a sustainable chemistry assessment centered on the LCA and combined dynamic material flow analysis (DMFA), the environmental multimedia fate model, the species sensitivity distribution (SSD), and the entropy weighting method (EWM).

Calculation of chemical footprint (ChF). The environmental impacts of hazardous chemicals represent one of the main concerns in sustainable chemistry. As a member of the environmental footprint family, the chemical footprint is an increasingly recognized and applied method for the quantitative assessment of the environmental impact of additive use.^1–3 It represents the amount of environmental space required to dilute a pollutant to an acceptable concentration in the ecosystem, generally expressed in terms of water volume.^24,43

To estimate the ChF of the plastics industry, it is first necessary to identify the categories of hazardous additives in plastics. Existing lists of plastic additives have incomplete information on their hazards and use,^8,10,44,45 and this study uses a list-of-lists approach for developing a detailed list of hazardous additives.^46,47 We summarized existing literature studies, reports, and databases to construct a database of plastic additives based on detailed application information (Table S11). A total of 16 hazard endpoints of hazardous additives were identified in terms of human health hazards, environmental hazards and physical hazards (Table S12). Then, 23 international lists of hazardous chemicals (Table S13) were screened to obtain a list of hazardous additives for plastics using keywords such as CAS number or substance name (Table S14). The final database contains 80 hazardous additives and primarily encompasses substances of very high concern (SVHCs) as listed in the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) Regulation by the European Union (EU).⁴⁸ These additives are classified according to toxic modes of action (TMoAs) as oxidative stress, DNA damage, tissue injury, target organ toxicity, polar narcosis, nonpolar narcosis and diester toxicity (Table S16).

Then, the environmental emissions of hazardous additives are calculated using emission factors with a one-year time step. The environmental emission factors for each additive in the production and waste management stages are substituted with the environmental emission factors of related functional additives from the Organisation for Economic Co-operation and Development (OECD) due to a lack of data, and the emission factors in the in-use stage are calculated with a dispersion model.^35,49 The formulae are as follows:

El_e,LC1 = EF_e,LC1 × Q_chemical,LC1 (1)

where El_e,LC1, El_e,LC2 and El_e,LC3 are the environmental emissions to environmental receptor e (e = E1, E2) of hazardous additives during production, in-use and waste management stages, respectively (t); EF_e,LC1 and EF_e,LC3,WM are the environmental emission factors for hazardous additives entering the environmental receptor e (e = E1,E2) in the production stage and waste management stage with a waste disposal method wm (wm = WM1, WM2, WM3, WM4, and WM5), respectively; Q_chemical,LC1 and Q_chemical,LC2 are the amounts of additives in the production and in-use stages, respectively (t); Q_{chemical,LC3,wm} is the amount of additives in plastic wastes disposed through a waste disposal method wm (wm = WM1, WM2, WM3, WM4, and WM5) (t); Q_plastic,LC2 is the amount of plastic products in the in-use stage (t); T is the scale parameter of the lifetime of plastics (year); A_plastic, ρ_plastic and thickness are the surface area (m²), density (kg m⁻³) and thickness (m) of plastics; and D_add and MW are the diffusion coefficient (m² s⁻¹) and molecular weight (g mol⁻¹) of additives. Environmental emissions in the in-use stage were assumed to entirely enter air.^35,49

After summarizing the environmental emissions of each additive, the environmental multimedia fate model Simplebox 4.0 (steady state) developed by the Dutch National Institute for Public Health and the Environment (RIVM) is used to estimate the environmental concentrations of hazardous additives in the environmental media.^50,51

The SSD model is then used to describe the sensitivity of a given species to the TMoAs of a given hazardous additive, i.e. the potentially affected fraction (PAF) on species. It is calculated using a log-logistic distribution:^52,53

where PAF_TMoA,s is the PAF on species with hazardous additives of the same TMoA in the environmental compartment s; β_TMoA is the species variance in toxicity (i.e., the slope of the distribution curve) of the TMoA (Table S16);^53,54 HU_TMoA is the effective hazard unit of the TMoA; HU_TMoA,s is the effective hazard unit of the TMoA in the environmental compartment s (g L⁻¹); C_x,s is the concentration of hazardous additive x in the environmental compartment s; μ_x is the mean value of log-transformed toxicity values (EC₅₀) of hazardous additive x (g L⁻¹); and n is the number of species for which EC₅₀ data are available.

This study uses the response addition method to calculate the joint toxicity of hazardous additives,^55,56 and uses the volume-weighted method to calculate PAFs on species of different hazardous additives in different environmental compartments.^24,43 The chemical footprint is calculated by converting the PAFs on species to environmental space occupancy:

where ChF is the chemical footprint of hazardous additives (m³), msPAF_s is multi-substance PAFs in the environmental compartment s; volume_s is the size of the environmental compartment s (m³); msPAF_MAX is the maximum acceptable msPAF_s in the study area based on the established environmental carrying boundary; and ES_total is the total size of environmental compartments in the study area (m³) (Table S18). In this study, 3% is used for msPAF_MAX.⁴³

Calculation of waste management input (WMI). Promoting the circular economy is widely recognized as a critical strategy to combat global plastic pollution.^57–60 Two principal methods are usually employed for quantifying the circularity of plastics. The first method quantifies the proportion of recycled materials used during production, but it fails to capture the variations among different waste disposal methods within a circular economy.^61–64 The second method assesses the method in which waste is disposed of and is adopted in this study.^61,65,66 This study further incorporates two quantifiable indicators, i.e., cumulative energy demand (CED) and waste management cost (WMC), as proposed by Volk et al. (2021)⁶⁶ to analyze the circularity of plastic across various disposal methods. CED is converted into a monetary value, and the summarized monetary value of CED and WMC is used as a measure of plastics circularity, called waste management input (WMI). The obtained WMIs could reflect the prioritization of waste disposal options in the European Waste Hierarchy.⁶⁷ Specifically, CED is defined as the total amount of primary energy required during the life cycle of a product,^68,69 while WMC includes the total economic costs associated with waste management, excluding energy use.⁶⁶ Notably, both the CED and WMC for recycling are negative, due to the substitution of primary materials with recycled ones. Similarly, incineration exhibits a negative CED due to the recovery of energy. In contrast, open dumping and littering, recognized as major contributors to plastic leakage,^20,33,34,70 are evaluated by incorporating the quantifiable ecosystem service costs of plastic pollution on marine ecosystems to calculate their WMC.⁷¹ As a result, the calculated WMI can be either positive or negative, representing a net input or a net benefit in the waste management stage, respectively. The calculation of WMI, CED and WMC is based on the following equations:where WMI, CED and WMC are waste management input (yuan), cumulative energy demand (giga Joule, GJ) and waste management costs (yuan), respectively; the CED and WMC coefficients P_CED,wm (GJ t⁻¹) and P_WMC,wm (yuan per t) are applicable to the waste disposal method wm (wm = WM1, WM2, WM3, WM4, and WM5) (Tables S19 and S21); Q_{plastic,LC3,wm} is the amount of plastic waste disposed of through a waste disposal method wm (wm = WM1, WM2, WM3, WM4, and WM5) (t); and P_cost is the energy price (yuan per kwh) based on the average feed-in tariffs of Chinese power producers from official data sources (Table S20). The constants 10⁹ (J GJ⁻¹) and 3.6 × 10⁶ (J kwh⁻¹) are conversion factors used to transform the unit of CED from GJ to J, and the unit of P_cost from yuan per kwh to yuan per J, respectively.

Calculation of greenhouse gas emission (GHG). The assessment of impacts on climate change at a national level or industry level typically employs the measurement of GHG.^72–74 The GHG is estimated using the coefficient method, expressed in the form of CO₂ equivalent emissions, using the following equations:

GHG_LC1 = EF_CO2,LC1 × Q_plastic,LC1 (15)

GHG_LC2 = EF_CO2,LC2 × Q_plastic,LC2 (16)

where GHG_LC1, GHG_LC2 and GHG_LC3 are greenhouse gas emissions in the production, in-use and waste management stages (t), respectively; Q_plastic,LC1 is the amount of plastic products in the production stage (t); the GHG coefficients EF_CO2,LC1, EF_CO2,LC2 and EF_CO2,LC3,wm are applicable to the production and in-use stages and the waste disposal method wm (wm = WM1, WM2, WM3, WM4, and WM5) (t CO₂-eq t⁻¹), respectively (Tables S22 and S23). GHG in the in-use stage is negligible compared to GHG from the production and waste management stages,⁷⁵ and is therefore not calculated in this study.

Calculation of the sustainable chemistry index (SCI). In this study, the EWM is used to normalize and assign weights to the three indicators, ChF, WMI and GHG, to obtain the final Sustainable Chemistry Index (SCI). It utilizes information entropy to measure the degree of data dispersion of each indicator and assigns different weights.⁷⁶ It avoids the potential impact of human bias on weight allocation and dynamically reflects data characteristics, compared with the analytic hierarchy process and the social preference method,⁷⁷ and has been widely used in environmental decision-making research.^78,79 The entropy value and the weight of each indicator are calculated by first standardizing each indicator and calculating the ratio of each indicator:

d_p = 1 − e_p (21)

where x_p,y, N_p,y and r_p,y represent the original value, standardized value and percentage for indicator p (p = 1, 2, 3) in year y, respectively; e_p, d_p and w_p represent entropy, deviation and weights for indicator p (p = 1, 2, 3); m and n represent the number of years and indicators (n = 3), respectively; and min x_p,y and max x_p,y represent the minimum value and maximum value of x_p,y, respectively.

Finally, the sustainable chemistry index (SCI) is calculated:

where SCI_y represents the sustainable chemistry index (SCI) in year y. We use 1 minus the resulting value as the SCI because ChF, WMI and GHG in this study are all negative indicators for sustainable chemistry. The obtained SCI ranges from 0 to 1, with a higher SCI meaning a better state of sustainable chemistry of the industry.

The amounts of products and additives are calculated using DMFA. A Weibull distribution is applied to describe the probability of plastics entering the waste management stage (SI-1–SI-6).

Scenario analysis

To analyze the principal factors impacting the future sustainable chemistry performance and the interactions of different policies, a scenario analysis of China's plastics industry in 2021–2060 is conducted.

Eight scenarios were developed, as shown in Table 1. Scenario 1 follows a business-as-usual trend. Scenarios 2 to 5 each represent a single policy, focusing on reducing oil-based plastic production, substituting oil-based plastics with bio-based plastics, reducing the use of hazardous additives, and enhancing recycling, respectively. Scenarios 6 to 8 represent combined policies. Scenario 6 substitutes oil-based plastics with bio-based plastics and enhances recycling. Scenario 7 reduces hazardous additives and enhances recycling. Scenario 8 implements all four policies simultaneously.

Table 1 Scenarios and descriptions

Scenario	Description
Scenario 1 (BAU)	Business as usual, the production and consumption of plastic products and additives will increase at the current rate, with recycling and incineration rates increasing to a stable state similar to developed countries
Scenario 2 (oil-reduce)	Reduces the production and consumption of plastic products and additives
Scenario 3 (oil-sub)	Substitutes oil-based plastics with bio-based plastics
Scenario 4 (add-control)	Bans the use of hazardous chemicals in plastics
Scenario 5 (recycle)	More ambitious recycling, landfill, open dumping and littering targets
Scenario 6 (oil-sub & recycle)	Substitutes oil-based plastics with bio-based plastics, and more ambitious recycling, landfill, open dumping and littering targets
Scenario 7 (add-control & recycle)	Bans the use of hazardous chemicals in plastics, and more ambitious recycling, landfill, open dumping and littering targets
Scenario 8 (comprehensive)	Reduces the production and consumption of plastic products and additives, substitutes oil-based plastics with bio-based plastics, bans the use of hazardous chemicals in plastics and achieves more ambitious waste management targets

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In alignment with the pertinent strategies and plans for the plastics industry domestically and internationally and considers the zero draft of the forthcoming global plastics treaty,^{14,44,60,80–84} four policy control variables shown below are set as follows:

(1) Polymer production: polymer production is a significant contributor to greenhouse gas emission in the plastics industry, and one proposed solution to reduce plastic pollution in this dimension is the substitution of bio-based polymers for conventional oil-based polymers.^85–87 In the baseline scenario, the polymer production processes will still use fossil fuels to produce oil-based polymers. In the control scenario, the polymer production processes will partly use biomass to produce bio-based polymers. PE and PVC will be replaced with bio-PE, PET will be replaced with bio-PET, and PP, PS and others will be replaced with other bio-based plastics, i.e., polylactic acid (PLA), polyhydroxyalkanoates (PHA) and thermoplastic starch (TPS).⁸⁶

(2) Plastic consumption: in the baseline scenario, if plastic consumption is not regulated, plastic consumption will be two times and three times the 2016 consumption in 2040 and 2050, respectively.⁸⁸ In the control scenario, the Chinese government will reduce demand for durable plastic products by 10% by 2040,⁸¹ and demand for plastic tableware by 30% by 2025, compared to 2020.⁸⁹

(3) Additive use: the zero draft of the forthcoming global plastics treaty proposes to reduce the use of hazardous additives in plastics but does not give specific targets.⁶⁰ Based on the Stockholm Convention on Persistent Organic Pollutants and the progress of implementation such as the REACH Regulation in the EU,^48,90 the control of hazardous additives in plastics will become more stringent, and their production and use will be gradually phased out or severely restricted. Accordingly, this study assumes that, in the baseline scenario, the use of additives will increase with the plastic production and consumption. In the control scenario, additives identified by scientific assessments as posing a clear risk to health and the environment, i.e., those listed in the Candidate List of SVHCs for Authorisation of the REACH Regulation or listed as persistent organic pollutants (POPs) in the Stockholm Convention, will be banned after several years of being listed.^91,92

(4) Waste management: promoting plastic recycling and incineration (energy recovery) is a possible way to reduce plastic pollution. The available data suggests that both recycling and incineration rates will increase, while open dumping and littering rates will decrease.^93,94 However, these rates will reach different targets in the two scenarios in this study. In the baseline scenario, the recycling and incineration rates are set to increase to the current stable recycling and incineration rates similar to those of EU and United States countries.^95–97 In the control scenario, the recycling rate will increase to reach a more ambitious target, while the landfill rate will decrease to a more ambitious target, based on the EU targets.^98–100 Besides, the sorting yields are set to be different in the recycling process in these two scenarios. The energy prices in 2021–2060 are assumed to be the same as those in 2020, since this price remained relatively stable over the past decade, with a coefficient of variation of only 1.3% during 2010–2020 (Table S20).

Sensitivity and uncertainty analysis

To evaluate the robustness of the three indicators used in this study, we conducted sensitivity analysis and Monte Carlo simulation with 15 000 runs using the Crystal Ball add-in for Microsoft Excel 2021 (Microsoft Corp., Redmond, WA, U.S.A.), taking 2020 as an example. The input variables include polymer production, product production, product export, additive use, scale parameter of lifetime, shape parameter of lifetime, environmental emission factor, share of hazardous additives, CED coefficient, WMC coefficient and GHG coefficient. The output variables include environmental emissions to air and water, WMI and GHG. In the Monte Carlo analysis, all input variables are assumed to follow a normal distribution. Data quality is assessed using five data quality indicators: reliability, completeness, temporal correlation, geographic correlation, and other correlations.^29,101–103 In the sensitivity analysis, the equation is as follows:where S is the sensitivity of the output result to the input parameter; I₀, I_+10%, and I_−10% are the original value of the input parameter and the values 10% above and below its present value, respectively; O₀, O_+10%, and O_−10% are the output results from applying I₀, I_+10%, and I_−10% as the input parameters, respectively.

To assess the robustness of the entropy weighting method, this study set a 30% perturbation to the weights of ChF, WMI and GHG by ±30%, respectively, and analysed the consistency of the results before and after the perturbation. Spearman's correlation analysis is used to evaluate the similarity of the ordering of the two sets of data.

Results and discussion

Trends in sustainable chemistry performance from 2000 to 2020

As shown in Fig. 2a, the SCI of China's plastics industry declines significantly from 0.679 in 2000 to 0.519 in 2011. This highlights the continuous deterioration in the industry's performance in sustainable chemistry over 2000–2011 and is driven by the growing environmental impacts associated with the rapid expansion of production and consumption of polymers, additives and products. From 2000 to 2011, the production of polymers, use of additives and consumption of products have increased 3.4, 4.5 and 3.8 times (Tables S1, S2 and S9). This leads to an increase in waste generation. The increase in waste net imports has further exacerbated the increase in domestic waste disposal, as well as the increase of emissions of hazardous additives and greenhouse gas in the waste management stage. From 2000 to 2011, China's net import of plastic waste increased from 0.28 Mt to 2.45 Mt (Table S5), and domestically disposed waste increased from 10.05 Mt to 48.66 Mt according to our material flow analysis. As a result, the environmental emissions of hazardous additives (Fig. S4), the waste management input (Fig. S5), and greenhouse gas emissions (Fig. S6) have all increased between 2000 and 2011. As shown in Fig. 2b, three negative indicators of sustainable chemistry performance, ChF, WMI and GHG, increase by factors of 2.5, 3.3 and 3.5, respectively.

Fig. 2 (a) Trends in the sustainable chemistry index (SCI) and the contributions of chemical footprint (ChF), waste management input (WMI) and greenhouse gas emission (GHG). (b) Trends in ChF, WMI and GHG as well as the contributions of different stages for China's plastics industry in 2000–2020.

The decline of the SCI slowed significantly during 2011–2020, with an SCI value of 0.492 in 2020. This is mainly related to improvements in waste management, including the decline of net import of plastic wastes (Table S5), increase of recycling rates, and decline of open dumping and littering rates (Table S6). The recycled plastic waste increased from 1.52 Mt to 6.10 Mt, while the mismanaged plastic waste declined from 28.64 Mt to 10.29 Mt during 2011–2020, according to our material flow analysis. The environmental emissions of hazardous additives (Fig. S4) and waste management input (Fig. S5) of mismanaged plastic waste therefore declined. This slowed the increase rate of ChF and WMI, as shown in Fig. 2b. However, this positive effect on sustainable chemistry is offset by the increased polymer production (Table S1) and waste incineration rates (Table S6). The polymer production increased from 75.97 Mt to 139.62 Mt, and waste incineration increased from 2.03 Mt to 15.81 Mt from 2011 to 2020, according to our analysis. This led to the continuous increase of GHG and its increased contribution to the SCI in 2011–2020 in Fig. 2a and b.

Taking 2020 as an example, a further material flow analysis shows that PET textile, PE packaging and PP packaging products are the main products impacting the sustainable chemistry performance in the plastics industry. The flame retardants (F6) are the main contributors of the ChF among different additives, with their contribution of 94.6% to ChF in 2020 (Fig. S7). The material flow analysis in Fig. 3 shows that PET textile, PE packaging and PP packaging products are the main application sectors for flame retardants, accounting for a total of 50.7%, 32.3% and 56.9% of flame retardants in the production, in-use and waste management stages, respectively. Although plasticizers (F7) account for a larger share in the production, in-use and waste management stages than flame retardants, their contribution to ChF is much lower due to their lower toxic potential (Table S16). Moreover, PET textile, PE packaging and PP packaging are also the main contributors of WMI and GHG, accounting for 67.9% of WMI and 52.4% of GHG in 2020 (Fig. S9 and S10), due to the large production and waste of plastics. In 2020, these products accounted for 34.5% and 31.8% of plastic production and waste, respectively. As shown in Fig. 3, although PVC building and construction products accounted for 22.7% of the in-use stock of plastics, their contributions to WMI and GHG are merely 1.0% and 6.4%, respectively, in 2020. This is due to their longest lifetime scale parameter and thus the longest stay in the in-use stock (Table S7). Therefore, PVC building and construction products account for a much smaller share of both plastic production and waste, at 7.1% and 3.8%, respectively, which results in their smaller contribution of GHG and WMI compared to PET textile, PE packaging and PP packaging products. The GHG of the plastics industry in China is 557 Mt (95% CI: 402.66–713.95 Mt) in 2020 in this study, which is quite close to one relevant study.¹⁰⁴

Fig. 3 (a) Stocks and flows of plastic products, polymers and additives, and (b) distributions of polymers and additives in the production, in-use and waste management stages for China's plastics industry as an example of 2020. Plastic products consist of polymers and additives. Production here refers only to production in 2020, and thus the quality of the in-use stock is greater than that of the production stage. International trade of plastic waste is included but is 0 in 2020.

Projected sustainable chemistry performance in future scenarios

In the BAU scenario (Scenario 1), the sustainable chemistry development of the plastics industry continues to degrade, with a gradual decline in the SCI from 0.491 in 2020 to 0.063 in 2060, as shown in Fig. 4a. Despite the fact that the import of plastic waste was banned in 2020,¹⁰⁵ the production and use of plastic polymers, additives and products continue to grow between 2020 and 2060. This leads to a continued increase in emissions of hazardous additives and greenhouse gas, with corresponding ChF and GHG reaching 2.2 and 3.4 times the 2020 levels in 2060 (Fig. S12a). Lower recycling targets have a limited effect on scaling up plastics circularity, with WMI remaining at 30 to −40 billion yuan (Fig. S11c), making the contribution of WMI to SCI remain stable, as shown in Fig. 4a. In contrast, in the comprehensive scenario (Scenario 8), the SCI shows a continuous upward trend and reaches 0.746 in 2060, which is consistently higher than the other scenarios, as shown in Fig. S4h. This implies that the simultaneous adoption of the four policies is the most effective in enhancing the sustainable chemistry development of the plastics industry.

Fig. 4 (a–h) Trends in the sustainable chemistry index (SCI) and the contributions of chemical footprint (ChF), waste management input (WMI) and greenhouse gas emission (GHG) for China's plastics industry in 2020–2060 in different scenarios.

Comparing the SCI in 2060 with that in 2020 for each scenario (Fig. S11a), it can be seen that of the four policies, reducing plastic consumption (Scenario 2) has the most positive effect on the sustainable chemical development of the plastics industry, with the SCI increasing to 0.635 in 2060, second only to Scenario 8. Reducing plastic consumption reduces the amounts of polymers and additives produced and used and thus reduces the emissions of hazardous additives and greenhouse gases during the life cycle. Compared to 2020, ChF and GHG decrease by 53.7% and 44.3%, respectively, in Scenario 2 in 2060 (Fig. S12b). It is worth noting that the increase in SCI does not stem from a decrease in WMI. Similar to Scenario 1, the WMI for Scenario 2 remains between 30 and −5 billion yuan due to the low recycling targets (Fig. S11c).

As shown in Fig. 4b and c, the substitution of bio-based plastics (Scenario 3) or controlling hazardous additives (Scenario 4) alone slows the rate of degradation of sustainable chemistry performance, but their SCIs are still on a downward trend. This is because these two policies only focus on a single issue of sustainable chemistry, i.e., reducing greenhouse gas emission or reducing environmental emissions of hazardous additives, and fail to address other key issues, thus limiting the sustainable chemistry development of the plastics industry. Specifically, Scenario 3 uses bio-based polymers instead of oil-based polymers, thus reducing greenhouse gas emission from polymer production processes compared to Scenario 1. By 2060, GHG in Scenario 3 decreases by 14.9% compared to Scenario 1. However, ChF in Scenario 3 remains at the same level as in Scenario 1 between 2020 and 2060 due to the unregulated use of hazardous additives (Fig. S12c). Scenario 5 significantly reduces environmental emissions of hazardous additives by phasing out hazardous chemicals in plastics. By 2060, the ChF in Scenario 5 is 98.5% lower than in 2020. However, since this scenario does not impose any controls on polymer production, GHG in Scenario 5 remains the same as Scenario 1 between 2020 and 2060 (Fig. S12d).

Further analysis of the recycling scenario (Scenario 5) and its joint policy scenarios (Scenario 6 and Scenario 7) shows that waste management is a key link in connecting the safe management of hazardous additives, promoting plastics circularity, and effectively controlling greenhouse gas emission. Meanwhile, the use of additives remains a key constraint on the effectiveness of waste management policies. As shown in Fig. 4e, from 2020 to 2060, the SCI in Scenario 5 remains in the range of 0.47–0.50, indicating that merely promoting recycling is still insufficient to significantly improve sustainable chemistry, though it is capable of curbing the deterioration. In Scenario 5, the raised targets for recycling rates promote the scaling up of plastics recycling and reduction of mismanaged wastes, with WMI declining from 30.2 billion yuan in 2020 to −627.8 billion yuan in 2060. Besides, the use of recycled plastics reduces the production of virgin polymers and could reduce greenhouse gas emission during polymer production. By 2060, GHG in Scenario 5 decreases by 9.5% compared to Scenario 1. In Scenario 5, ChF shows an increasing and then decreasing trend between 2020 and 2060, with the turning point in 2050 (Fig. S12e). This reflects the dual impact of the promotion of recycling on ChF in the absence of controls on the use of hazardous additives. On the one hand, promoting recycling leads to the unintentional recycling of hazardous additives, which will prolong their presence in the life cycle and thus increase environmental emissions. On the other hand, declined mismanaged waste can help reduce the emissions of hazardous additives in this stage. The SCI in Scenario 6 also remained in the range of 0.48–0.54, suggesting a similar effect on sustainable chemistry to Scenario 5.

The effect of the promotion of recycling on sustainable chemistry development is more significant when hazardous additives are strictly regulated. As shown in Fig. 4g, after phasing out hazardous chemicals in plastics in 2030, the SCI in Scenario 7 shows a continuous upward trend and reaches 0.631 in 2060, which is close to the level of the SCI in Scenario 2. This suggests that similar policy effects as reducing plastic consumption can be achieved by controlling hazardous additives and promoting recycling. Compared to Scenario 5 and Scenario 6, Scenario 7 significantly reduces ChF by reducing the use of hazardous additives and their unintentional recycling, with a 99.1% reduction in ChF by 2060 compared to 2020. Such interpretations from the scenario analysis just echo the latest observations on the limitations of relying solely on recycling.^13,106 Researchers and international organizations have raised concern about the unintended recycling of hazardous chemicals, which undermines the effectiveness of plastic recycling strategies.^45,84,107

Sensitivity and uncertainty analysis

The Monte Carlo analysis results demonstrate that the output variables follow a normal distribution (Fig. S13), suggesting statistical stability. The order-of-magnitude differences between the 2.5^th and 97.5^th percentiles remain within an acceptable range (Table 2). Furthermore, sensitivity analysis results (Fig. S14) show that all of the absolute values of sensitivity are below 1.8, with 75.0% of the values having absolute values below 0.5, indicating that the results are not overly sensitive to variations in input variables. Only 6.3% of the sensitivities have absolute values greater than 1, primarily involving product production (impacting WMI), WMC coefficients (impacting WMI) and energy price (impacting WMI). The product production data (Table S2) and energy prices (Table S20) are derived from official statistics, and WMC coefficients are mainly obtained from peer-reviewed literature studies (Table S21), thus ensuring high reliability. These findings collectively support the robustness of the proposed indicators under uncertainty. Spearman correlation analysis of weight perturbations indicates that the average correlation coefficients exceed 0.85 across all perturbations. Notably, after 2035, the coefficients remain significantly above 0.9 (Fig. S15). This demonstrates that the SCI ranking results for the eight scenarios remain highly stable despite weight perturbation, particularly in long-term projections. Thus, the entropy weighting method exhibits strong robustness to weight variations, supporting its applicability in this study.

Table 2 Difference between the 2.5^th percentile and the 97.5^th percentile of the Monte Carlo analysis

Output variables	Estimated value in this study	2.5^th percentile	97.5^th percentile
Environmental emissions to air	1.98 kt	1.43 kt	2.62 kt
Environmental emissions to water	1.78 Mt	1.28 Mt	2.31 Mt
Waste management input (WMI)	30.24 billion yuan	22.68 billion yuan	37.92 billion yuan
Greenhouse gas emission (GHG)	557.48 Mt	402.66 Mt	713.95 Mt

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Information on the use and environmental emission of hazardous additives in China is still scarce at present. For example, new alternatives of unknown toxicity are appearing in China's plastics market.^108,109 The “regrettable substitution” may appear during the phase-out of hazardous additives but is not considered in this study due to a lack of data. The impact of plastic leakage in the in-use stage on environmental emissions of hazardous additives is not included due to limited data, which may underestimate ChF in this stage. In addition, due to the lack of information on the recycling process, this study does not consider the elimination of hazardous additives during processing, nor the impact of losses during processing, i.e. waste collected for recycling but not sorted and regranulated. They may be incinerated or landfilled and will introduce uncertainty to the calculation of GHG and WMI of recycling. The presence of hazardous additives in recycled materials is not considered a limitation to the industry in which the product is used. Since the value of the recycled material depends on its purity,^14,107,110 this will introduce some uncertainty. What's more, this study does not include the contribution to greenhouse gas emission from the use of additives in the manufacture of plastic products, which may be important for certain plastics with high additive use.²⁶ To provide a more accurate assessment of sustainable chemistry in the plastics industry, more research is needed on the recycling process, as well as on the use and environmental emissions of plastic additives in the future.

Conclusions

This study provides a comprehensive assessment of sustainable chemistry in the plastics industry, highlighting that given the current economic and technological conditions, reducing plastic consumption remains the most effective policy option for promoting sustainable chemistry in the industry. The intensive use of hazardous additives limits the benefits of recycling, as it leads to unintentional recycling and circulation of hazardous chemicals in recycled materials. In this regard, chemical recycling may probably serve as an option, as it could separate out hazardous additives. However, this approach presents trade-offs, including higher energy consumption and costs, necessitating further technological advancements to improve its feasibility.

Our findings also reveal that a combination of controlling hazardous additives and promoting recycling can achieve effects comparable to reducing plastic consumption in the long term. Therefore, maximizing plastic recycling with a circular economy framework is essential for significantly reducing chemical footprint, and climate chage impact and promoting plastics circularity. A key strategy to enable this transition is the elimination or substantial reduction of hazardous plastic additives. Moreover, further innovation in low-carbon, cost-effective waste management processes will be crucial. By uncovering the synergies between different policy pathways, this study provides scientific insights to inform global plastics treaty negotiations, supporting evidence-based policymaking for a more sustainable and circular plastics economy.

Author contributions

Jiazhe Chen: writing – original draft, methodology, data curation, formal analysis, and visualization. Zhichun Zhang: writing – review & editing, formal analysis, and investigation. Qiaonan Jing: writing – review & editing and validation. Shaoxuan Zhang: writing – review & editing and resources. Rongjing Lu: writing – review & editing. Jianguo Liu: writing – review & editing, conceptualization, methodology, resources, funding acquisition, project administration, and supervision.

Conflicts of interest

There are no conflicts to declare.

Data availability

Data are available within the article and its supplementary information (SI). Supplementary information: additional detailed methodology, data and results. The values used for scenario analysis are given in SI-2. See DOI: https://doi.org/10.1039/d5gc03504j.

Acknowledgements

This work was funded by the National Key Research and Development Program of China (2024YFC3908805) and the National Natural Science Foundation of China (22476003).

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