• Energy Research

Understanding uncertainty is essential in using life cycle assessment (LCA) to support decisions. Monte Carlo simulation (MCS) is widely used to characterize the variability in LCA results, be them life cycle inventory (LCI), category indicator results, normalized results, or weighted results. In this study, we present a new method to decompose MCS results into underlying contributors using the logarithmic mean Divisia index (LMDI) decomposition method with a case study on natural gas focusing on two impact categories: global warming and USETox human health impacts. First, after each run of MCS, the difference in simulated and deterministic results is decomposed using the LMDI decomposition method, which returns the contribution of each factor to the difference of the run. After repeating this for 1000 MCS runs, the statistical properties of the contributions by each factor are analyzed. The method quantifies the contribution of underlying variables, such as characterization factors and LCI items, to the overall variability of the result, such as characterized results. The method presented can decompose the variabilities in LCI, characterized, normalized, or weighted results into LCI items, characterization factors, normalization references, weighting factors, or any subset of them. As an illustrative example, a case study on natural gas LCA was conducted, and the variabilities in characterized results were decomposed into underlying LCI items and characterization factors. The results show that LCI and characterization phases contribute 65% and 35%, respectively, to the uncertainty of the characterized result for global warming. For the human health impact category, LCIs and characterization factors contribute 32% and 68%, respectively, to the overall uncertainty. In particular, methane emissions in LCI contributed the most to the overall uncertainties in global warming impact, while the characterization factor of chromium was identified as the main driver of the overall uncertainties in human health impact of natural gas. Using this approach, LCA practitioners can decompose the overall variability in the results to the underlying contributors under the MCS setting, which can help prioritize the parameters that need further refinement to reduce overall uncertainty in the results. The method reliably estimates the uncertainty contributions of the variables with large variabilities without the need for large computational resources, and it can be applied to any stage of an LCA calculation including normalization and weighting, or to other fields than LCA such as material flow analysis and risk assessment.

SummaryMany research efforts aim at an extension of life‐cycle assessment (LCA) in order to increase its spatial or temporal detail or to enlarge its scope. This is an important contribution to industrial ecology as a scientific discipline, but from the application viewpoint other options are available to obtain more detailed information, or to obtain information over a broader range of impacts in a life‐cycle perspective. This article discusses three different strategies to reach these aims: (1) extension of LCA—one consistent model; (2) use of a toolbox—separate models used in combination; and (3) hybrid analysis—combination of models with data flows between them.Extension of LCA offers the most consistent solution. Developments in LCA are moving toward greater spatial detail and temporal resolution and the inclusion of social issues. Creating a supertool with too many data and resource requirements is, however, a risk. Moreover, a number of social issues are not easily modeled in relation to a functional unit.The development of a toolbox offers the most flexibility regarding spatial and temporal information and regarding the inclusion of other types of impacts. The rigid structure of LCA no longer sets limits; every aspect can be dealt with according to the logic of the relevant tool. The results lack consistency, however, preventing further formal integration.The third strategy, hybrid analysis, takes up an intermediate position between the other two. This strategy is more flexible than extension of LCA and more consistent than a toolbox. Hybrid analysis thus has the potential to combine the strong points of the other two strategies. It offers an interesting path for further discovery, broader than the already well‐known combination of process‐LCA and input‐output‐LCA. We present a number of examples of hybrid analysis to illustrate the potentials of this strategy.Developments in the field of a toolbox or of hybrid analysis may become fully consistent with LCA, and then in fact become part of the first solution, extension of LCA.

Previous studies on the life-cycle environmental impacts of corn ethanol and gasoline focused almost exclusively on energy balance and greenhouse gas (GHG) emissions and largely overlooked the influence of regional differences in agricultural practices. This study compares the environmental impact of gasoline and E85 taking into consideration 12 different environmental impacts and regional differences among 19 corn-growing states. Results show that E85 does not outperform gasoline when a wide spectrum of impacts is considered. If the impacts are aggregated using weights developed by the National Institute of Standards and Technology (NIST), overall, E85 generates approximately 6% to 108% (23% on average) greater impact compared with gasoline, depending on where corn is produced, primarily because corn production induces significant eutrophication impacts and requires intensive irrigation. If GHG emissions from the indirect land use changes are considered, the differences increase to between 16% and 118% (33% on average). Our study indicates that replacing gasoline with corn ethanol may only result in shifting the net environmental impacts primarily toward increased eutrophication and greater water scarcity. These results suggest that the environmental criteria used in the Energy Independence and Security Act (EISA) be re-evaluated to include additional categories of environmental impact beyond GHG emissions.

Abstract Normalisation provides a measure of the relative contribution from a product system to one or more environmental problems. Total yearly emissions for a reference year in a reference region are normally used to calculate normalisation figures. This paper provides up-to-date normalisation figures for the Netherlands in 1997/1998, Western Europe in 1995 and the world in 1990 and 1995. Impact categories considered were depletion of abiotic resources, land competition, global warming, stratospheric ozone depletion, acidification, eutrophication, photochemical ozone formation, radiation and toxicity. In all cases, a limited set of emissions or extractions are dominant contributors to the normalisation scores. Although much effort was spent on collecting emissions data and characterisation factors, particularly normalisation scores for radiation and toxicity remain considerably uncertain.

Prior studies have estimated that a liter of bioethanol requires 263-784 L of water from corn farm to fuel pump, but these estimates have failed to account for the widely varied regional irrigation practices. By using regional time-series agricultural and ethanol production data in the U.S., this paper estimates the state-level field-to-pump water requirement of bioethanol across the nation. The results indicate that bioethanol's water requirements can range from 5 to 2138 L per liter of ethanol depending on regional irrigation practices. The results also show that as the ethanol industry expands to areas that apply more irrigated water than others, consumptive water appropriation by bioethanol in the U.S. has increased 246% from 1.9 to 6.1 trillion liters between 2005 and 2008, whereas U.S. bioethanol production has increased only 133% from 15 to 34 billion liters during the same period. The results highlight the need to take regional specifics into account when implementing biofuel mandates.

As the Chinese economy has become an integral part of the global supply chain, quantifying the environmental impacts by Chinese industry is indispensible to understanding the environmental performance of products in general. Comprehensive and consistent environmental data infrastructure, however, is lacking in China, hindering such an understanding. In this paper, we demonstrate a simplified method for assembling and harmonizing various data sources to develop a sectoral environmental database for input-output life cycle assessment (IO-LCA). We first identified key substances by analyzing previous normalization studies and other countries' sectoral environmental databases. Data for priority substances were compiled and then adjusted and validated. The database created from this process was then used to analyze the direct and indirect environmental impacts generated by Chinese rural and urban consumptions. Expenditures on food and other basic household needs like heating and cooking were found to play a dominant role in generating environmental impacts in China, while previous studies of industrialized countries also highlighted the importance of transportation. This database provides background information for LCA through, for example, the hybrid approach, and is also conducive to ongoing efforts to develop generic life cycle inventory databases in China.

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.

Embodied greenhouse gas (GHG) emissions and their structure of inducement by the supply-chain networks of 480 goods and services in the United States are analyzed for 44 GHGs. Producing a dollar of a product or service generates an average of 0.36 kg of CO2 equivalent GHGs onsite, increasing to 0.83 kg when supply-chain-induced emissions are taken into account. Services produce less than 5% of total U.S. GHG emissions directly, and their direct GHG emission intensities per dollar output are much less (0.04 kg C02 equiv/dollars) than those of physical products, even when supply-chain-induced emissions are included (0.47 kg C02 equiv/dollars). When both supply-chain effects and the volume of household expenditures are taken into account, however, household consumption of services excluding electric utilities and transportation services proves to be responsible for 37.6% of total industrial GHG emissions in the United States, almost twice the amount due to household consumption of electric utility and transportation services. Given the current structure of GHG emissions, a shift to a service-oriented economy is shown to entail a decrease in GHG emission intensity per unit GDP but an increase, by necessity, in overall GHG emissions in absolute terms. The results are discussed in the context of U.S. climate change policy.