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Biofuels are widely seen as substitutes for fossil fuels to offset the imminent decline of oil production and to mitigate the emergent increase in GHG emissions. This view is, however, based on too simple an analysis, focusing on only one piece in the whole mosaic of the complex biofuel techno-system, and such partial approaches may easily lead to ideological bias based on political preference. This study defines the whole biofuel techno-system at three scales, i.e., the foreground production (A), the background industrial network (B, including A), and the supporting Earth biosphere (C, including B). The thermodynamic concepts of energy, exergy and emergy measure various flows at these three scales, viz. primary resources, energy and materials products, and labor and services. Our approach resolves the confusion about scale and metric: direct energy demand and direct exergy demand apply at scale A; cumulative energy demand and cumulative exergy demand apply at scale B; and energy is applied at scale C, where it is named emergy, while exergy also can be applied at scale C. This last option was not examined in the present study. The environmental performance of the system was assessed using a number of sustainability indicators, including resource consumption, input renewability, physical benefit, and system efficiency, using ethanol from corn stover in the US as a technology case. Results were compared with available literature values for typical biofuel alternatives. We also investigated the influence of methodological choices on the outcomes, based on contribution analysis, as well as the sensitivity of the outcomes to emergy intensity. The results indicate that the techno-system is not only supported by commercial energy and materials products, but also substantially by solar radiation and the labor and services invested. The bioethanol techno-system contributes to the overall supply of energy/exergy resources, although in a less efficient way than the process by which the Earth system produces fossil fuels. Our results show that bioethanol cannot be simply regarded as a renewable energy resource. Furthermore, the method chosen for the thermodynamic analysis results in different outcomes in terms of ranking the contributions by various flows. Consequently, energy analysis, exergy analysis, and emergy analysis jointly provide comprehensive indications of the energy-related sustainability of the biofuel techno-system. This thermodynamic analysis can provide theoretical support for decision making on sustainability issues.

The present paper outlines an update of the fate and exposure part of the fate, exposure and effects model USES-LCA. The new fate and exposure module of USES-LCA was applied to calculate human population intake fractions and fate factors of the freshwater, marine and terrestrial environment for 3393 substances, including neutral organics, dissociating organics and inorganics, emitted to 7 different emission compartments. The human population intake fraction is on average 10(-5)-10(-8) for organics and 10(-3)-10(-4) for inorganics, depending on the emission compartment considered. Chemical-specific human population intake fractions can be 1-2.7 orders of magnitude higher or lower compared to the typical estimates. For inorganics, the human population intake fractions highly depend on the assumption that exposure via food products can be modelled with constant bioconcentration factors. The environmental fate factor is on average 10(-11)-10(-18) days m(-3) for organics and 10(-10)-10(-12) days m(-3) for inorganics, depending on the receiving environment and the emission compartment considered. Chemical-specific environmental fate factors can be 1-8 orders of magnitude higher or lower compared to the typical estimates. The largest differences between the new and old version of USES-LCA are found for emissions to air and soil. This is caused by a significant change in the structure of the air and soil compartments in the new version of USES-LCA, i.e. the distinction between rural and urban air, including rain-no rain conditions and including soil depth dependent intermedia transport.

Purpose: The matrix method for the solution of the so-called inventory problem in LCA generally determines the inventory vector related to a specific system of processes by solving a system of linear equations. The paper proposes a new approach to deal with systems characterized by a rectangular (and thus non-invertible) coefficients matrix. The approach, based on the application of regression techniques, allows solving the system without using computational expedients such as the allocation procedure. Methods: The regression techniques used in the paper are (besides the ordinary least squares, OLS) total least squares (TLS) and data least squares (DLS). In this paper, the authors present the application of TLS and DLS to a case study related to the production of bricks, showing the differences between the results accomplished by the traditional matrix approach and those obtained with these techniques. The system boundaries were chosen such that the resulting technology matrix was not too big and thus easy to display, but at the same time complex enough to provide a valid demonstrative example for analyzing the results of the application of the above-described techniques. Results and discussion: The results obtained for the case study taken into consideration showed an obvious but not overwhelming difference between the inventory vectors obtained by using the least-squares techniques and those obtained with the solutions based upon allocation. The inventory vectors obtained with the DLS and TLS techniques are closer to those obtained with the physical rather than with the economic allocation. However, this finding most probably cannot be generalized to every inventory problem. Conclusions: Since the solution of the inventory problem in life cycle inventory (LCI) is not a standard forecasting problem because the real solution (the real inventory vector related to the investigated functional unit) is unknown, we are not able to compute a proper performance indicator for the implemented algorithms. However, considering that the obtained least squares solutions are unique and their differences from the traditional solutions are not overwhelming, this methodology is worthy of further investigation. Recommendations: In order to make TLS and DLS techniques a valuable alternative to the traditional allocation procedures, there is a need to optimize them for the very particular systems that commonly occur in LCI, i.e., systems with sparse coefficients matrices and a vector of constants whose entries are almost always all null but one. This optimization is crucial for their applicability in the LCI context.

Life-cycle assessment (LCA) is a form of chain analysis in which structural pathways in the economic system are delineated and connected to environmental problems. As such, it can be seen as an extension of, or a complement to, energy analysis. The main developments over the past 30 years are sketched in a perspective that puts an emphasis on standardization and scientific consensus creation. We end with a logical next development: a closer cooperation and harmonization with the domain of energy analysis, and an expected growing interest from the energy-application side.

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.

The increasing trend in the consumption of various materials has also led to a huge increase in the final waste streams especially in the form of municipal solid waste (MSW) and the consequent environmental pollutions in particular greenhouse gas (GHG) emissions. These have made MSW management a significant environmental issue for governments and policy-makers. To address these challenges, developed countries have implemented sustainable material management (SMM) strategies which have been comprehensively reviewed herein. Moreover, waste generation statistics reported for most of the developed and developing countries as well as the existing gaps in MSW management among these countries have been fully discussed. The present paper was also aimed at comprehensively assessing electricity generation potentials from MSW using an integrated solid waste management system (including three different technologies of anaerobic digestion (AD), incineration, and pyrolysis-gasification) while the consequent GHG emission reduction potentials as a result of their implementation were also explored. To facilitate the understanding of the potential impacts of these treatment strategies, Iran's data were used as a case study. More specifically, the theoretical and technical potentials of electricity generation were calculated and the GHG emission reduction potentials were estimated using a life cycle assessment (LCA) approach. Overall, it was found that 5005.4–5545.8 GW h of electricity could be generated from MSW in Iran annually which could lead to approximately 3561–4844 thousand tons of avoided CO2eq. Such GHG reductions would be translated into approximately 0.5% of Iran's annual GHG emissions and would be considered a promising achievement given Iran's international GHGs reduction commitment, i.e., 4% reduction of anthropogenic GHGs emissions by 2030 below the business as usual scenario. Such findings could also be modeled for the other developing countries around the world where efficient MSW management is yet to be implemented.

Hybrid life cycle assessment (LCA), through combining input-output (IO) models and process-based LCA for a complete system boundary, is widely recognized as a more accurate approach than process-based LCA with an incomplete system boundary. Without a complete process model for verification, however, the performance of hybrid LCA remains unclear. Here, using a counterexample we show that hybrid LCA does not necessarily provide more accurate results than process-based LCA, simply because the aggregation of heterogeneous processes in IO models may introduce more errors. In so doing, we prove that only when IO-based LCA and process-based LCA have the same level of detail would they yield the same results. Whether hybrid LCA provides more accurate estimates depends on whether the IO model introduced serves as an adequate proxy for the missing products as opposed to if they were estimated by a complete process model. The use of a highly-aggregated IO model runs the risk of overestimation, and could result in a larger relative error than the truncation error resulting from an incomplete process model. Our study seeks to provide a balanced view of hybrid LCA, and our findings offer important insights for future hybrid LCA studies to improve the accuracy and realm of applicability of the approach.