Biodegradable plastics are increasingly valued for their superior biodegradability, which offers promising alternatives to conventional, non-degradable plastics. Although previous studies have applied life cycle assessment (LCA) to evaluate their environmental impacts from production to engineered end-of-life (EOL) (such as anaerobic digestion)[1], they rarely considered biodegradable plastics released into natural environments where microplastics can be generated and their environmental impacts remain unexplored. The prior reason is that assessing the potential environmental impacts of biodegradable microplastics is challenging due to the lack of life cycle environmental impact (LCIA) methods in traditional LCA.

Our study addresses the abovementioned research gaps by developing an LCIA method for biodegradable microplastics in freshwater ecosystems. Specifically, we advanced the LCIA framework of USEtox[2], considering microplastics as toxic particles with fate, exposure, and effect factors. In the fate modeling, we linked the size, density, and specific surface degradation rate (SSDR) of microplastics to their biodegradation and sedimentation rate constants. This addresses the oversimplification in previous research[3] regarding the dynamic interactions between biodegradation and sedimentation, which are important removal mechanisms of microplastics in water and sediment. Using the proposed LCIA method, we quantified the aquatic ecotoxicity of five common biodegradable plastics, including bio-based poly(lactic acid) (PLA), poly(3-hydroxybutyrate) (PHB), and thermoplastic starch (TPS), and fossil-based poly(ε-caprolactone) (PCL) and poly(butylene succinate) (PBS), with particle diameters ranging from micro- (1000, 100, and 10 µm) to nanometers (1 and 0.1 µm). Moreover, we applied both static and dynamic methods to assess the time-varying greenhouse gas (GHG) emissions of microplastics, combining their fate factors with biodegradation performance.

Our results show the large impacts of microplastic sizes on the environmental impacts of various plastics. For example, at the size classes of 1000, 100, and 10 µm, PLA (with the lowest SSDR) has the lowest GHG but the highest aquatic ecotoxicity. This implies a potential burden shifting from reduced aquatic ecotoxicity to increased GHG emissions of microplastics when substituting PLA by highly degradable plastics (such as PHB, PCL, TPS, and PBS). However, at diameters of 1 and 0.1 µm, PLA shows both the highest aquatic ecotoxicity and GHG emissions without burden shifting. Our results also indicate that the GHG emissions of microplastic in natural environments can be equivalent to a substantial fraction of the life cycle GHG (from production to engineered EoL) of biodegradable plastics. In the sensitivity analyses, we identified critical SSDR that result in maximum GHG emissions at each size class of microplastic. Our research not only presents the potential environmental impacts of biodegradable microplastics, but also provides a powerful tool for the eco-design of future biodegradable plastics.

Reference

[1]. Cazaudehore et al., Biotechnology Advances 2022, 56, 107916.

[2]. Rosenbaum et al., The International Journal of Life Cycle Assessment 2008, 13, 532.

[3]. Corella-Puertas et al., Journal of Cleaner Production 2023, 418, 138197

The US beverage industry has adopted the circular economy strategy of shifting from single use to reusable cups. The transition is justified by the rationale that a reusable cup will offset multiple single use cups and avoid the burdens from manufacturing and landfilling (of the post-use waste) of single use cups. However, the findings in the peer-reviewed LCAs and commercial literature are conflicting with a lack of conclusive evidence on the environmental benefit from reusable cups.

To identify the root causes of the conflicting findings, we systematically review and harmonize 233 peer-review publications and technical reports on the scientific literature on the LCA of single use and reusable cups. We use the pedigree matrix approach to objectively assess the data quality used in the LCAs.

The results reveal significant methodological and data shortcomings in existing LCAs which inhibit their ability to be representative of operations and assess the trade-offs between single use and re-usable cups for US market conditions. Only 3 of the studies present LCA results using primary industry-sourced lifecycle inventory (LCI) data representing the US operations. The 3 studies are published prior 2011 and do not account for three key changes in beverages industry: improvements in the manufacturing of the cups in the US, increase in the diversity of the materials used and design of cups, increase in the number and the geographical spread of beverage stores, cup distribution sites, washing locations and landfills in the US, and the variability in the GHG intensity of electricity across the USA. There is no discussion or quantitative uncertainty and sensitivity analysis on how the reliance on outdated and non-US LCI data limits the applicability of the LCA findings to the US beverage industry.

We use results from tailored case-studies on how the following improvements will address the shortcomings listed above and increase the robustness of LCAs to assess circular economy strategies for the beverages industry in the US:

1. Develop standardized LCI datasets for single use and reusable cups which is anonymized (to overcome data confidentiality concerns), best represents industry-wide operations and can be used by LCA practitioners

2. Incorporate operational standards (e.g., ANSI standards for utensil washing) in LCAs for the beverages industry

3. Develop an updatable and publicly available dataset on the locations of the stores, distribution and washing centers for the beverages industry

4. Account for geo-spatial sensitivity in electricity mixes and the locations of store, cup distribution and washing, and landfill sites

5. Incorporate uncertainty analysis through global sensitivity analysis to identify key uncertainties and environmental hotspots in the LCA results which the industry can address

6. Quantify the impact of data quality on results through the pedigree matrix approach and acknowledge how data gaps can limit the applicability and generalization of LCA results

Background

Cotton is a globally significant commodity, contributing extensively to economies for countries such as the United States, which accounts for approximately 3.75% of its agricultural export value (1,2). However, cotton production poses notable environmental challenges, including intensive water usage, pesticide and fertilizer application, and vulnerability to climate change. Recent droughts, particularly in Texas (3), have exacerbated these issues, highlighting the pressing need for sustainable practices in cotton farming.

Method

Life Cycle Assessment (LCA) has emerged as a critical tool for quantifying environmental impacts and resource usage across a product's lifecycle. This study aims to develop a prospective LCA framework integrated with machine learning to predict the environmental impacts of cotton production under various climate scenarios. The proposed framework leverages supervised machine learning models to forecast irrigation water requirements, incorporating these predictions into the life cycle inventory (LCI) of the agricultural phase. Additionally, the framework evaluates the benefits of adopting sustainable technologies, such as electrified agricultural machinery, which can enhance energy efficiency and reduce environmental emissions. Our projections show that the total water usage of cotton production is expected to increase, although the overall pattern of less irrigation water usage in the East and more irrigation water usage in the West has not changed. From year 2000, the decreasing trend in electricity and diesel usage highlights the benefits of technological advancements in agricultural machinery and irrigation systems. The pro-electricity scenario proposed in this study, which suggests replacing diesel with electricity to power farm equipment, not only meets cost constraints but also offers environmental benefits with lower total energy usage and lower greenhouse gas (GHG) emissions.

Significance

By addressing the dual objectives of assessing climate-driven future impacts and evaluating sustainable strategies, this study provides critical insights into the future sustainability of U.S. cotton production. It enables stakeholders to make informed decisions on regional practices and technological innovations, ensuring resilient and sustainable cotton production in the face of climate change.

Reference:

1.Cotton Sector at a Glance | USDA Economic Research Service. USDA Economic Research Service https://www.ers.usda.gov/topics/crops/cotton-and-wool/cotton-sector-at-a-glance/ (2022).

2. U.S. Cotton Exports in 2023 | USDA Foreign Agricultural Service. USDA Foreign Agricultural Service. https://fas.usda.gov/data/commodities/cotton#:~:text=Cotton%20%7C%20USDA%20Foreign%20Agricultural%20Service,$660.59%20Million (2023).

3. Cotton: World Markets and Trade | USDA Foreign Agricultural Service. https://apps.fas.usda.gov/psdonline/circulars/cotton.pdf (2024).

Packaging plays a crucial role in society by helping ensure product containment, protection, and preservation. However, the growing consumption of packaging, often linked with economic growth, has sparked interest in the potential environmental impacts of packaging materials. This life cycle assessment (LCA) study covers cradle to end-of-life, excluding use phase impacts (e.g., breakage and shelf life), of polyethylene (PE)-based packaging and alternative materials (paper, metals, and glass). The study compares the potential environmental impacts of PE-based packaging and alternatives for nineteen example end-uses in the US. The packaging formats were selected within five prevalent PE packaging applications: collation shrink films, pallet wraps, heavy-duty sacks, rigid non-food, and flexible food. The potential environmental impacts were assessed using ten impact categories: Global Warming Potential (GWP, with and without biogenic CO2 uptake), fossil resource use, water scarcity, mineral resource use, land occupation, land transformation, acidification (freshwater and terrestrial) and freshwater eutrophication.

The comparative assessment showed that PE-based packaging had lower potential environmental impacts in >70% of comparative cases across the assessed indicators. PE-based packaging showed lower potential environmental impacts in 14 of 19 (74%) comparisons for fossil resource use, 15 of 19 (79%) comparisons each for GWP (with and without biogenic CO2 uptake), mineral resource use, acidification (freshwater and terrestrial) and land transformation, 18 of 19 (95%) comparisons for land occupation, and 19 of 19 comparisons for freshwater eutrophication. Overall, PE packaging had lower potential environmental impacts in 157 of 190 (83%) environmental indicators across all applications.

These results indicate that broad-based plastic substitution proposals and regulations can potentially inadvertently lead to unintended consequences of increasing environmental impacts, highlighting the need for nuanced, application-specific decision-making approaches that consider life cycle impacts of packaging materials.

When finished with an electronic device, consumers choose between storing, recycling, giving away, trading-in, reselling, or throwing it away. This choice has environmental and data privacy implications, e.g. reuse of devices is generally environmentally preferable to recycling, which is preferable to throwing away in the trash. This work develops empirical models to explain consumer End-of-First Use (EoFU) disposition as a multicriteria choice among competing options. The base data is established through a survey of 4,000 U.S. consumers over 10 device categories, with queries of stated knowledge and attitudes on EoFU options and past and planned behaviors. Different modeling approaches are used to explain behavior: cluster analysis, multivariable regression and neural networks. Results include that the decision to store or not store a device is strongly influenced by data security concerns when recycling or reselling, perceived convenience of recycling, and wanting to keep a backup of data. Once the choice is made to disposition outside the home, knowledge of and trust in recycling were important in consumers choosing to recycle. Reselling of devices was strongly influenced by knowledge of how to sell. Cluster and related analytical techniques indicate distinct groups of consumers. For example, k-means clustering over responses to questions stated knowledge and attitudes reveals 3 distinct groups: Cluster 1 has higher data security concerns when recycling, reselling or donating, and less knowledge and trust in End-of-First-Use options overall. The intended behavior of cluster 1 shows higher than average uncertainty in what to do at End-of-First-Use and more intent to store (lower values for other options - recycling, reselling and donating). Cluster 2 shows higher knowledge and trust in recycling, reselling, and donation, and slightly higher than average concern about data security of these options. The intended behavior of cluster 2 shows higher intent to resell, trade-in or donate, and lower levels of being uncertain of what to do and of storing. Cluster 3 expresses much less concern about data security, and lower utility of a stored device. Their intended behavior shows less storage and higher levels of other End-of-First-Use options. The longer-term goal of this research streams is developing interventions to shift behavior, e.g. distributing information on where to recycle and how to resell. The cluster analysis suggests that matching intervention to consumer segment might be more effective than a blanket intervention. Sensitivity analysis is done to clarify which shifts in knowledge and/or attitudes leads to larger increases in reselling and recycling.

The primary nutrients in fertilizers—nitrogen (N), phosphorus (P), and potassium (K) (NPK)— are traditionally derived through carbon-intensive methods and rely on non-renewable feedstock whose supply is constrained and from geopolitically sensitive countries. A transition to a circular economy that recovers N, P and K from human urine (HU) to fertilizer helps reduce reliance on constrained and environmentally intensive fertilizer feedstock. Existing research primarily focuses on increasing the technical efficiency and recovery rates of extracting NPK from HU. However, there has been a lack of analysis on identifying potential sources of HU in the US, locating the infrastructure to convert HU to fertilizers and transport the HU-derived fertilizers to farms.

To address this knowledge gap, we present the first study which geospatially optimizes and maximizes the environmental benefits from implementing a circular economy for NPK from HU to crops in the US. We quantify the HU supply from 2600 geospatially dispersed public schools across the state of Arizona (AZ) housing 1,095,000 students and 134,000 teachers and school staff. The NPK from the HU is modeled to meet the NPK demand for 78% crops grown across 916,000 acres of cropland, which accounts for all the cultivated land in AZ. The analysis applies the anticipatory-LCA framework to account for extraction of NPK from HU, water savings from avoided flushing in HU diversion toilets, the transport of HU to the recovery facility, the conversion of NPK to fertilizers, the transport of fertilizers to the farm and processing of solid and liquid waste which accumulate across the various steps. We explore two options for locating NPK recovery facilities from HU: centralized and decentralized. For centralized facilities HU is transported from multiple schools and nutrient extraction occurs at a larger scale than decentralized facilities. In decentralized facilities nutrient extraction occurs at the school to avoid transportation to a centralized facility and nutrient extraction occurs at a smaller scale than centralized facilities.

The results showed that HU generated in just the public schools in AZ can annually meet 2% of the N, 1.5% of the P and 1% of the K used in fertilizers in AZ. The transition to a circular NPK economy for HU-based fertilizers reduces the climate and water footprint of fertilizers in AZ by 35% and 55% respectively. In addition, the analysis will present a geospatial map containing the optimal locations of the various centralized and decentralized facilities for NKP recovery from HU in AZ.