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Life Cycle Analysis (life + cycle_analysis)

Distribution by Scientific Domains
Life Sciences60%

Selected Abstracts

Using Trading Zones and Life Cycle Analysis to Understand Nanotechnology Regulation

THE JOURNAL OF LAW, MEDICINE & ETHICS, Issue 4 2006
Ahson Wardak
This article reviews the public health and environmental regulations applicable to nanotechnology using a life cycle model from basic research through end-of-life for products. Given nanotechnology's immense promise and public investment, regulations are important, balancing risk with the public good. Trading zones and earth systems engineering management assist in explaining potential solutions to gaps in an otherwise complex, overlapping regulatory system. [source]

Greenhouse gas emissions from four bioenergy crops in England and Wales: Integrating spatial estimates of yield and soil carbon balance in life cycle analyses

GCB BIOENERGY, Issue 4 2009
JONATHAN HILLIER
Abstract Accurate estimation of the greenhouse gas (GHG) mitigation potential of bioenergy crops requires the integration of a significant component of spatially varying information. In particular, crop yield and soil carbon (C) stocks are variables which are generally soil type and climate dependent. Since gaseous emissions from soil C depend on current C stocks, which in turn are related to previous land management it is important to consider both previous and proposed future land use in any C accounting assessment. We have conducted a spatially explicit study for England and Wales, coupling empirical yield maps with the RothC soil C turnover model to simulate soil C dynamics. We estimate soil C changes under proposed planting of four bioenergy crops, Miscanthus (Miscanthus×giganteus), short rotation coppice (SRC) poplar (Populus trichocarpa Torr. & Gray ×P. trichocarpa, var. Trichobel), winter wheat, and oilseed rape. This is then related to the former land use , arable, pasture, or forest/seminatural, and the outputs are then assessed in the context of a life cycle analysis (LCA) for each crop. By offsetting emissions from management under the previous land use, and considering fossil fuel C displaced, the GHG balance is estimated for each of the 12 land use change transitions associated with replacing arable, grassland, or forest/seminatural land, with each of the four bioenergy crops. Miscanthus and SRC are likely to have a mostly beneficial impact in reducing GHG emissions, while oilseed rape and winter wheat have either a net GHG cost, or only a marginal benefit. Previous land use is important and can make the difference between the bioenergy crop being beneficial or worse than the existing land use in terms of GHG balance. [source]

Limited LCAs of pharmaceutical products: merits and limitations of an environmental management tool

CORPORATE SOCIAL RESPONSIBILITY AND ENVIRONMENTAL MANAGEMENT, Issue 2 2003
Anne Marie de Jonge
This article explores both the merits and the limitations of life cycle analysis (LCA) as an environmental management tool in the framework of the pharmaceutical industry. In this study, limited LCAs in the form of product lifecycle-oriented energy balances were established for two rather different pharmaceutical products. Primary energy requirements served as the single indicator for the products' direct and indirect environmental impacts. The functional units of the products were defined as the one year treatments of average patients. The results of the case studies indicate that the portion of the active substance in the pharmaceutical end product is an important predictor for the breakdown of energy requirements and thus environmental impacts over the life cycle. Despite its limitations, the energy balances provide first-hand indications of where eco-efficiency measures should be taken. In this sense, the limited LCAs served as a useful environmental management tool. Copyright © 2003 John Wiley & Sons, Ltd. and ERP Environment [source]

Greenhouse gas emissions from four bioenergy crops in England and Wales: Integrating spatial estimates of yield and soil carbon balance in life cycle analyses

GCB BIOENERGY, Issue 4 2009
JONATHAN HILLIER
Abstract Accurate estimation of the greenhouse gas (GHG) mitigation potential of bioenergy crops requires the integration of a significant component of spatially varying information. In particular, crop yield and soil carbon (C) stocks are variables which are generally soil type and climate dependent. Since gaseous emissions from soil C depend on current C stocks, which in turn are related to previous land management it is important to consider both previous and proposed future land use in any C accounting assessment. We have conducted a spatially explicit study for England and Wales, coupling empirical yield maps with the RothC soil C turnover model to simulate soil C dynamics. We estimate soil C changes under proposed planting of four bioenergy crops, Miscanthus (Miscanthus×giganteus), short rotation coppice (SRC) poplar (Populus trichocarpa Torr. & Gray ×P. trichocarpa, var. Trichobel), winter wheat, and oilseed rape. This is then related to the former land use , arable, pasture, or forest/seminatural, and the outputs are then assessed in the context of a life cycle analysis (LCA) for each crop. By offsetting emissions from management under the previous land use, and considering fossil fuel C displaced, the GHG balance is estimated for each of the 12 land use change transitions associated with replacing arable, grassland, or forest/seminatural land, with each of the four bioenergy crops. Miscanthus and SRC are likely to have a mostly beneficial impact in reducing GHG emissions, while oilseed rape and winter wheat have either a net GHG cost, or only a marginal benefit. Previous land use is important and can make the difference between the bioenergy crop being beneficial or worse than the existing land use in terms of GHG balance. [source]

Partial life cycle analysis: a model for pre-breeding census data

OIKOS, Issue 3 2001
Madan K. Oli
Matrix population models have become popular tools in research areas as diverse as population dynamics, life history theory, wildlife management, and conservation biology. Two classes of matrix models are commonly used for demographic analysis of age-structured populations: age-structured (Leslie) matrix models, which require age-specific demographic data, and partial life cycle models, which can be parameterized with partial demographic data. Partial life cycle models are easier to parameterize because data needed to estimate parameters for these models are collected much more easily than those needed to estimate age-specific demographic parameters. Partial life cycle models also allow evaluation of the sensitivity of population growth rate to changes in ages at first and last reproduction, which cannot be done with age-structured models. Timing of censuses relative to the birth-pulse is an important consideration in discrete-time population models but most existing partial life cycle models do not address this issue, nor do they allow fractional values of variables such as ages at first and last reproduction. Here, we fully develop a partial life cycle model appropriate for situations in which demographic data are collected immediately before the birth-pulse (pre-breeding census). Our pre-breeding census partial life cycle model can be fully parameterized with five variables (age at maturity, age at last reproduction, juvenile survival rate, adult survival rate, and fertility), and it has some important applications even when age-specific demographic data are available (e.g., perturbation analysis involving ages at first and last reproduction). We have extended the model to allow non-integer values of ages at first and last reproduction, derived formulae for sensitivity analyses, and presented methods for estimating parameters for our pre-breeding census partial life cycle model. We applied the age-structured Leslie matrix model and our pre-breeding census partial life cycle model to demographic data for several species of mammals. Our results suggest that dynamical properties of the age-structured model are generally retained in our partial life cycle model, and that our pre-breeding census partial life cycle model is an excellent proxy for the age-structured Leslie matrix model. [source]