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The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5-52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

Typical applications of LCA assume that the magnitude of life-cycle impact grows proportionally to the volume of demand, while in reality the additional impact due to marginal increase in demand may differ from the average impact. In the literature, the calculation of marginal life-cycle impacts often involves the use of optimization models, where typically the total economic costs are minimized. However, modeling spatially explicit marginal responses of a system involving multiple producers and consumers has not been discussed in LCA literature. In this paper, we demonstrate a spatial optimization technique for modeling marginal responses of a multi-producer, multi-consumer system. Our model determines the optimal production-by-location mix and associated environmental stressor at minimum systems cost. We demonstrate the model using a preliminary case study on blue water consumption by potato. We collected state-by-state data on potato yield, cost of potato production, and water use for irrigation, as well as interstate transportation fuel costs. We also estimated the marginal increase in demand for potato following USDA's recommended diet. The results show that the cradle-to-gate blue water consumption of potatoes based on 2016 demand was 96 m3/ton potato, which changes non-linearly along with the growth of potato demands. In order to meet the USDA's recommended diet, the additional demand on potato (530,000 ton per year) would result in a 29% lower blue water consumption per ton of potato (68 m3/ton) as compared to the average result of the current production system. In addition, we tested the model to analyze the marginal impacts under two scenarios: (1) high fuel tax and (2) high water price. The preliminary results indicate that water pricing is more effective than a fuel tax increase in reducing the marginal blue water consumption of potato based on our scenarios of the recommended diet demand. The results demonstrate that our model can be used to understand the non-linear behavior of marginal effect over demand crease, and for testing alternative policy scenarios involving a system with multiple producers and consumers across regions.

Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R2 values of resulting ANN models range from 0.54 to 0.75 (median R2 = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals.

Direct air capture (DAC) is critical for achieving stringent climate targets, yet the environmental implications of its large-scale deployment have not been evaluated in this context. Performing a prospective life cycle assessment for two promising technologies in a series of climate change mitigation scenarios, we find that electricity sector decarbonization and DAC technology improvements are both indispensable to avoid environmental problem-shifting. Decarbonizing the electricity sector improves the sequestration efficiency, but also increases the terrestrial ecotoxicity and metal depletion levels per tonne of CO2 sequestered via DAC. These increases can be reduced by improvements in DAC material and energy use efficiencies. DAC exhibits regional environmental impact variations, highlighting the importance of smart siting related to energy system planning and integration. DAC deployment aids the achievement of long-term climate targets, its environmental and climate performance however depend on sectoral mitigation actions, and thus should not suggest a relaxation of sectoral decarbonization targets.

Capacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180-$730 and 110-570 kgCO2 per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%-67%, while impact on emissions is mixed (-20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

The costs and benefits of climate change mitigation are known to be distributed unevenly across time and space, while their intergenerational distribution across nations has not been evaluated. Here, we analyze the lifetime costs and benefits of climate change mitigation by age cohorts across countries under the Paris Agreement. Our results show that the age cohorts born prior to 1960 generally experience a net reduction in lifetime gross domestic product per capita. Age cohorts born after 1990 will gain net benefits from climate change mitigation in most lower income countries. However, no age cohorts enjoy net benefits regardless of the birth year in many higher income countries. Furthermore, the cost-benefit disparity among old and young age cohorts is expected to widen over time. Particularly, lower income countries are expected to have much larger cost-benefit disparity between the young and the old. Our findings highlight the challenges in building consensus for equitable climate policy among nations and generations.

Typical applications of LCA assume that the magnitude of life-cycle impact grows proportionally to the volume of demand, while in reality the additional impact due to marginal increase in demand may differ from the average impact. In the literature, the calculation of marginal life-cycle impacts often involves the use of optimization models, where typically the total economic costs are minimized. However, modeling spatially explicit marginal responses of a system involving multiple producers and consumers has not been discussed in LCA literature. In this paper, we demonstrate a spatial optimization technique for modeling marginal responses of a multi-producer, multi-consumer system. Our model determines the optimal production-by-location mix and associated environmental stressor at minimum systems cost. We demonstrate the model using a preliminary case study on blue water consumption by potato. We collected state-by-state data on potato yield, cost of potato production, and water use for irrigation, as well as interstate transportation fuel costs. We also estimated the marginal increase in demand for potato following USDA's recommended diet. The results show that the cradle-to-gate blue water consumption of potatoes based on 2016 demand was 96 m3/ton potato, which changes non-linearly along with the growth of potato demands. In order to meet the USDA's recommended diet, the additional demand on potato (530,000 ton per year) would result in a 29% lower blue water consumption per ton of potato (68 m3/ton) as compared to the average result of the current production system. In addition, we tested the model to analyze the marginal impacts under two scenarios: (1) high fuel tax and (2) high water price. The preliminary results indicate that water pricing is more effective than a fuel tax increase in reducing the marginal blue water consumption of potato based on our scenarios of the recommended diet demand. The results demonstrate that our model can be used to understand the non-linear behavior of marginal effect over demand crease, and for testing alternative policy scenarios involving a system with multiple producers and consumers across regions.

Capturing and utilizing CO2 as carbon feedstock for chemicals, fuels, or polymers is frequently discussed to replace fossil carbon and thereby help mitigate climate change. Emission reductions by Carbon Capture and Utilization (CCU) depend strongly on the choice of the CO2 source because CO2 sources differ in CO2 concentration and the resulting energy demand for capture. From a climate-change perspective, CO2 should be captured at the CO2 source with the lowest CO2 emissions from capture. However, reported carbon footprints differ widely for CO2 captured, from strongly negative to strongly positive for the same source. The differences are due to methodological ambiguity in the treatment of multifunctionality in current assessment practice. This paper reviews methodological approaches for determining the carbon footprint of captured CO2 as carbon feedstock, and shows why some approaches lead to suboptimal choices of CO2 sources and that increased consistency in life cycle assessment (LCA) studies on CCU is needed. Based on strict application of Life Cycle Assessment (LCA) standards and guidelines, it is shown that substitution should be applied to avoid suboptimal choices of CO2 sources. The resulting methodological recommendations are applied to estimate the carbon footprint of feedstock CO2 for current CO2 sources in Europe and for future CO2 sources in a scenario for a low carbon economy. For all CO2 sources, the cradle-to-gate footprint of captured CO2 is negative ranging from -0.95 to -0.59 kg CO2 eq. per kg of feedstock CO2 today and from -0.99 to -0.98 kg CO2 eq. in a low carbon economy. The carbon footprints of different CO2 sources differ mainly due to their energy demands. The presented assessment method and the carbon footprints of the CO2 feedstocks CO2 provide the basis for future assessments of carbon capture and utilization processes. This journal is

Abstract We estimated the global living wage gap (LWG) as $US674 billion per year, which is comparable to the gross domestic product (GDP) of Switzerland. India (IND), the largest LWG country, contributed 32% of the global LWG. For a pair of jeans, closing the gap increases low-skilled cotton and textile wages in IND by 137% and 52%, respectively, while the retail price would increase only by 8% if consumed in Western countries. However, we found that most of the outputs with large LWGs from low-income countries are consumed domestically, suggesting that (a) closing the gap significantly increases the domestic price of products such as agriculture and textiles in low-income countries; and that (b) living wage premiums in high-income countries alone have a limited impact on closing the global LWG. The results highlight the need for both ethical trade and domestic living wage initiatives to close global LWGs.

Synthetic microfibers are found virtually everywhere in the environment, but emission pathways and quantities are poorly understood. By connecting regionalized global datasets on apparel production, use, and washing with emission and retention rates during washing, wastewater treatment, and sludge management, we estimate that 5.6 Mt of synthetic microfibers were emitted from apparel washing between 1950 and 2016. Half of this amount was emitted during the last decade, with a compound annual growth rate of 12.9%. Waterbodies received 2.9 Mt, while combined emissions to terrestrial environments (1.9 Mt) and landfill (0.6 Mt) were almost as large and are growing. Annual emissions to terrestrial environments (141.9 kt yr-1) and landfill (34.6 kt yr-1) combined are now exceeding those to waterbodies (167.2 kt yr-1). Improving access to wastewater treatment is expected to further shift synthetic microfiber emissions from waterbodies to terrestrial environments. Preventing emissions at the source would therefore be a more effective mitigation measure.

Chemical production is set to become the single largest driver of global oil consumption by 2030. To reduce oil consumption and resulting greenhouse gas (GHG) emissions, carbon dioxide can be captured from stacks or air and utilized as alternative carbon source for chemicals. Here, we show that carbon capture and utilization (CCU) has the technical potential to decouple chemical production from fossil resources, reducing annual GHG emissions by up to 3.5 Gt CO2-eq in 2030. Exploiting this potential, however, requires more than 18.1 PWh of low-carbon electricity, corresponding to 55% of the projected global electricity production in 2030. Most large-scale CCU technologies are found to be less efficient in reducing GHG emissions per unit low-carbon electricity when benchmarked to power-to-X efficiencies reported for other large-scale applications including electro-mobility (e-mobility) and heat pumps. Once and where these other demands are satisfied, CCU in the chemical industry could efficiently contribute to climate change mitigation.

The hybrid approach in Life Cycle Assessment (LCA) that uses both input-output and process data has been discussed in the context of mitigating truncation error and burdens of data collection. However, the implication of introducing input-output data on the overall uncertainty of an LCA result has been debated. In this study, we selected an existing process LCA, performed a Monte Carlo simulation after hybridizing each truncated flow at a time, and analyzed the dispersion and position of the distribution in the results. The results showed that hybridization effectively moved the mean of the life cycle greenhouse gas (GHG) emissions 38% higher while maintaining the standard deviation within the 0.62-0.78 range (relative standard deviation, 3-4%). We identified key activities contributing to the overall uncertainty and simulated the potential effect of collecting higher quality supplier-specific data for those activities on the overall uncertainty. The results showed that replacing as few as 10 of the largest uncertainty contributors with high precision supplier-specific data substantially narrowed the distribution. Our results suggest that hybridizing truncated inputs improves accuracy of LCA results without compromising their precision, and prioritizing supplier-specific data collection can further enhance precision in a cost-effective manner.

Increasingly diverse chemicals are used in consumer products, while our understanding of their exposure pathways and associated human health risks still lags behind. This paper aims to identify the dominant patterns of exposure pathways and associated health risks of chemicals used in consumer products reported in the peer-reviewed literature. We analyzed 342 articles covering 202 unique chemicals, and distilled the information on the functional uses, product applications, exposure routes, exposure pathways, toxicity endpoints and their combinations. We found that the volume of the literature addressing human health risks of chemicals in consumer products is increasing. Among others, phthalates, bisphenol-A, and polybrominated diphenyl ethers were the most frequently discussed chemical groups in the literature reviewed. Emerged from our review were a number of frequently reported functional use/product application combinations, including plasticizers, polymers/monomers, and flame retardants used in food contact products, personal care products, cosmetics, furniture, flooring, and electronics. We also observed a strong tendency that the number of publications on a chemical surges following major regulatory changes or exposure incidents associated with the chemical. We highlight the need to develop the capacity and the mechanism through which human health risks of chemicals in consumer products can be identified prior to their releases.

In the West, limited government capacity to solve environmental problems has triggered the rise of a variety of "nonstate actors" to supplement government efforts or provide alternative mechanisms for addressing environmental issues. How does this development - along with our efforts to understand it - map onto environmental governance processes in China? China's efforts to address environmental issues reflect institutionalized governance processes that differ from parallel western processes in ways that have major consequences for domestic environmental governance practices and the governance of China "going abroad." China's governance processes blur the distinction between the state and other actors; the "shadow of the state" is a major factor in all efforts to address environmental issues. The space occupied by nonstate actors in western systems is occupied by shiye danwei ("public service units"), she hui tuanti ("social associations") and e-platforms, all of which have close links to the state. Meanwhile, international NGOs and multinational corporations are also significant players in China. As a result, the mechanisms of influence that produce effects in China differ in important ways from mechanisms familiar from the western experience. This conclusion has far-reaching implications for those seeking to address global environmental concerns, given the importance of China's growing economy and burgeoning network of trade relationships.

Chemicals in consumer products have become the focus of recent regulatory developments including California's Safer Consumer Products Act. However, quantifying the amount of chemicals released during the use and post-use phases of consumer products is challenging, limiting the ability to understand their impacts. Here we present a comprehensive framework, OrganoRelease, for estimating the release of organic chemicals from the use and post-use of consumer products given limited information. First, a novel Chemical Functional Use Classifier estimates functional uses based on chemical structure. Second, the quantity of chemicals entering different product streams is estimated based on market share data of the chemical functional uses. Third, chemical releases are estimated based on either chemical product categories or functional uses by using the Specific Environmental Release Categories and EU Technological Guidance Documents. OrganoRelease connects 19 unique functional uses and 14 product categories across 4 data sources and provides multiple pathways for chemical release estimation. Available user information can be incorporated in the framework at various stages. The Chemical Functional Use Classifier achieved an average accuracy above 84% for nine functional uses, which enables the OrganoRelease to provide release estimates for the chemical, mostly using only the molecular structure. The results can be can be used as input for methods estimating environmental fate and exposure.

In life cycle assessment (LCA), performing Monte Carlo simulation (MCS) using fully dependent sampling typically involves repeated inversion of a technology matrix for a sufficiently large number of times. As the dimension of technology matrices for life cycle inventory (LCI) databases grows, MCS using fully dependent sampling is becoming a computational challenge. In our previous work, we pre-calculated the distribution functions of the entire LCI flows in the ecoinvent ver. 3.1 database to help reduce the computation time of running fully dependent sampling by individual LCA practitioners. However, it remains as a question whether the additional errors due to the use of pre-calculated uncertainty values are large enough to alter the conclusion of a comparative study, and, if so, what is the odds of such cases. In this study, we empirically tested the probability of altering the conclusion of a comparative LCA due to the use of pre-calculated uncertainty values. We sampled 10,000 random pairs of elementary flows of ecoinvent LCIs (ai and bi) and ran MCSs (1) using pre-calculated uncertainty values and (2) using fully dependent sampling. We analyzed the distribution of the differences between ai and bi (i.e., ai-bi) of each run, and quantified the probability of reversing (e.g., ai > bi became ai < bi) or moderating the conclusion (e.g., ai > bi became ai ≈ bi). In order to better replicate the situation under a comparative LCA setting, we also sampled 10,000 random pairs of elementary flows from the processes that produce electricity, and repeated the same procedure. The results show that no LCIs derived using pre-calculated uncertainty values constitute large enough differences from those using fully dependent sampling to reverse the conclusion. However, in 5.3% of the cases, the conclusion from one approach is moderated under the other approach or vice versa. When elementary flow pairs are sampled only from the electricity-producing processes, the probability of moderating the conclusions increases to 10.5%, while that of reversing the conclusions remains nil. As the number of unit processes in LCI databases increases, running full MCSs in a PC-environment will continue to be a challenge, which may lead some LCA practitioners to avoid uncertainty analysis altogether. Our results indicate that pre-calculated distributions for LCIs can be used as a proxy for comparative LCA studies in the absence of adequate computational resources for full MCS. Depending on the goal and scope of the study, LCA practitioners should consider using pre-calculated distributions if the benefits of doing so outweighs the associated risks of altering the conclusion.

Most existing life-cycle release models for engineered nanomaterials (ENM) are static, ignoring the dynamics of stock and flows of ENMs. Our model, nanoRelease, estimates the annual releases of ENMs from manufacturing, use, and disposal of a product explicitly taking stock and flow dynamics into account. Given the variabilities in key parameters (e.g., service life of products and annual release rate during use) nanoRelease is designed as a stochastic model. We apply nanoRelease to three ENMs (TiO2, SiO2 and FeOx) used in paints and coatings through seven product applications, including construction and building, household and furniture, and automotive for the period from 2000 to 2020 using production volume and market projection information. We also consider model uncertainties using Monte Carlo simulation. Compared with 2016, the total annual releases of ENMs in 2020 will increase by 34-40%, and the stock will increase by 28-34%. The fraction of the end-of-life release among total release flows will increase from 11% in 2002 to 43% in 2020. As compared to static models, our dynamic model predicts about an order of magnitude lower values for the amount of ENM released from this sector in the near-term while stock continues to build up in the system.

The number of chemicals in the market is rapidly increasing, while our understanding of the life-cycle impacts of these chemicals lags considerably. To address this, we developed deep artificial neural network (ANN) models to estimate life-cycle impacts of chemicals. Using molecular structure information, we trained multilayer ANNs for life-cycle impacts of chemicals using six impact categories, including cumulative energy demand, global warming (IPCC 2007), acidification (TRACI), human health (Impact2000+), ecosystem quality (Impact2000+), and eco-indicator 99 (I,I, total). The application domain (AD) of the model was estimated for each impact category within which the model exhibits higher reliability. We also tested three approaches for selecting molecular descriptors and identified the principal component analysis (PCA) as the best approach. The predictions for acidification, human health, and the eco-indicator 99 model showed relatively higher performance with R2 values of 0.73, 0.71, and 0.87, respectively, while the global warming model had a lower R2 of 0.48. This study indicates that ANN models can serve as an initial screening tool for estimating life-cycle impacts of chemicals for certain impact categories in the absence of more reliable information. Our analysis also highlights the importance of understanding ADs for interpreting the ANN results.

The sustainable development goals (SDGs) launched by the United Nations (UN) set a new direction for development covering the environmental, economic, and social pillars. Given the complex and interdependent nature of the socioeconomic and environmental systems, however, understanding the cause-effect relationships between policy actions and their outcomes on SDGs remains as a challenge. We provide a systematic review of cause-effect analysis literature in the context of quantitative sustainability assessment. The cause-effect analysis literature in both social and natural sciences has significantly gained its breadth and depth, and some of the pioneering applications have begun to address sustainability challenges. We focus on randomized experiment studies, natural experiments, observational studies, and time-series methods, and the applicability of these approaches to quantitative sustainability assessment with respect to the plausibility of the assumptions, limitations and the data requirements. Despite the promising developments, however, we find that quantifying the sustainability consequences of a policy action, and providing unequivocal policy recommendations based on it is still a challenge. We recognize some of the key data requirements and assumptions necessary to design formal experiments as the bottleneck for conducting scientifically defensible cause-effect analysis in the context of quantitative sustainability assessment. Our study calls for the need of multi-disciplinary effort to develop an operational framework for quantifying the sustainability consequences of policy actions. In the meantime, continued efforts need to be made to advance other modeling platforms such as mechanistic models and simulation tools. We highlighted the importance of understanding and properly communicating the uncertainties associated with such models, regular monitoring and feedback on the consequences of policy actions to the modelers and decision-makers, and the use of what-if scenarios in the absence of well-formulated cause-effect analysis.

A dynamic life cycle impact assessment model demonstrates a non-constant intake fraction of inhaled nano-TiO2 as total emissions changes.

This study seeks to understand the role of primary processing, i.e. the first post-mining stage, in supply risk, by means of a case study on three critical metals (neodymium, cobalt, and platinum) in the context of Japan. Applying the ‘footprint’ concept with a multiregional input–output model, we have quantified the direct and indirect vulnerability of the Japanese economy to such risks. Considering the supply risks associated with primary processors, we find that Japanese final consumers are exposed to relatively higher supply risks for neodymium as compared with cobalt and platinum. Our study shows that the primary processing stage of a metal’s supply chain may contribute significantly to the overall supply risks, suggesting that this stage should be taken into due account in understanding and mitigating supply-chain vulnerability through, e.g. supplier diversification and alternative material development.