Principles for life cycle inventories of land use on a global scale

Purpose

To assess the diverse environmental impacts of land use, a standardization of quantifying land use elementary flows is needed in life cycle assessment (LCA). The purpose of this paper is to propose how to standardize the land use classification and how to regionalize land use elementary flows.

Materials and methods

In life cycle inventories, land occupation and transformation are elementary flows providing relevant information on the type and location of land use for land use impact assessment. To find a suitable land use classification system for LCA, existing global land cover classification systems and global approaches to define biogeographical regions are reviewed.

Results and discussion

A new multi-level classification of land use is presented. It consists of four levels of detail ranging from very general global land cover classes to more refined categories and very specific categories indicating land use intensities. Regionalization is built on five levels, first distinguishing between terrestrial, freshwater, and marine biomes and further specifying climatic regions, specific biomes, ecoregions and finally indicating the exact geo-referenced information of land use. Current land use inventories and impact assessment methods do not always match and hinder a comprehensive assessment of land use impact. A standardized definition of land use types and geographic location helps to overcome this gap and provides the opportunity to test the optimal resolution of land cover types and regionalization for each impact pathway.

Conclusions and recommendation

The presented approach provides the necessary flexibility to providers of inventories and developers of impact assessment methods. To simplify inventories and impact assessment methods of land use, we need to find archetypical situations across impact pathways, land use types and regions, and aggregate inventory entries and methods accordingly.

     

LCA of the timber sector in Ghana: preliminary life cycle impact assessment (LCIA)

Purpose  

Most life cycle impact assessment (LCIA) approaches in life cycle assessment (LCA) are developed for western countries. Their LCIA approaches and characterization methodologies for different impact categories may not be necessarily relevant to African environmental conditions and particularly not for the timber sector in Ghana. This study reviews the relevance of existing impact categories and LCIA approaches, and uses the most relevant for the timber sector of Ghana.     

Categorizing water for LCA inventory

Purpose  

As impact assessment methods for water use in LCA evolve, so must inventory methods. Water categories that consider water quality must be defined within life cycle inventory. The method presented here aims to establish water categories by source, quality parameter and user.     

Eutrophication of aquatic ecosystems a new method for calculating the potential contributions of nitrogen and phosphorus

Aim, Scope and Background  

Aquatic eutrophication is a widespread problem in inland and coastal waters around the world and it should therefore be one of the impact categories to be considered in LCA studies of products and services. In LCAs there are several impact assessment methods to determine characterisation factors for eutrophying nutrients, but few methods have been developed to model fate and spatial aspects. One such method was developed as part of an LCA application of the Finnish forest industry. The aim of this study was to present this characterisation method in which the potential contributions of nitrogen and phosphorus to eutrophication of aquatic ecosystems are calculated. The use of the method was demonstrated by producing site/sector-specific characterisation factors and by constructing a reference value of aquatic eutrophication for Finland. A discussion of sensitivity and uncertainty aspects related to input data is also presented.     

Linear array of conserved sequence motifs to discriminate protein subfamilies: study on pyridine nucleotide-disulfide reductases

Background  

The pyridine nucleotide disulfide reductase (PNDR) is a large and heterogeneous protein family divided into two classes (I and II), which reflect the divergent evolution of its characteristic disulfide redox active site. However, not all the PNDR members fit into these categories and this suggests the need of further studies to achieve a more comprehensive classification of this complex family.     

A comparative life cycle assessment of the sugarcane value chain in the province of Tucumán (Argentina) considering different technology levels

Purpose

The purpose of this work is to quantify the environmental impact of the sugarcane industry in Tucumán (Argentina) through the life cycle analysis (LCA). The distinctive feature is the consideration of different technology levels (TLs) in the agricultural stage: high (HTL), medium (MTL), and low (LTL).

Methods

The scope of the study covers the agricultural and industrial stages through a “from cradle to gate” approach (from sugarcane cultivation until production of finished products: sugar and alcohol). The system is divided into Agriculture, Sugar Factory, and Distillery. Data used for the inventory are mainly provided by local experts, sugarcane growers, and processing companies. The characteristics of each TL are taken from a regional classification. For the impact assessment, the CML 2001 model (nine impact categories) is used.

Results and discussion

Regardless of the TL, in most of the impact categories, an important contribution attributable to the use of synthetic agrochemicals is evident. As for the comparison among TLs, the ethanol produced with HTL has less impact values than the ones produced with MTL and LTL in seven categories. These results can be mainly explained by the better cultural yields obtained with HTL, and to the fact that sugarcane is not burnt before harvesting in HTL as it is in MTL and LTL.

Conclusions

This study explores the implications of using different TLs for the agricultural tasks on the sugarcane supply chain in Tucumán, which is characterized by a vertically nonintegrated productive scheme. If practices associated to HTL are implemented, a reduction of the environmental impact is observed in most categories. It is necessary to compare these results with economic and social implications to ensure sustainability of the sugarcane value chain.

     

Multi-component based cross correlation beat detection in electrocardiogram analysis

Background  

The first stage in computerised processing of the electrocardiogram is beat detection. This involves identifying all cardiac cycles and locating the position of the beginning and end of each of the identifiable waveform components. The accuracy at which beat detection is performed has significant impact on the overall classification performance, hence efforts are still being made to improve this process.     

Assessment of land use impacts on soil ecological functions: development of spatially differentiated characterization factors within a Canadian context

Purpose  

Among other regional impact categories in LCA, land use still lacks a suitable assessment method regarding the least developed “soil ecological quality” impact pathway. The goals of this study are to scope the framework addressing soil ecological functions and to improve the development of regionalized characterization factors (CFs). A spatially explicit approach was developed and illustrated for the Canadian context using three different regional scales and for which the extent of spatial variability was assessed.     

Correlation analysis of life cycle impact assessment indicators measuring resource use

Introduction  

Even though the necessity of a sustainable use of natural resources is widely accepted, there is neither consensus on how “resource use” is clearly defined nor how it should be measured. Depending on the definition, it can comprise raw material consumption only or the consumption and pollution of natural resources. Consequently, lots of indicators can be applied, and the result of a life cycle assessment study aiming to quantify resource use seems to depend on the selection of impact categories. Therefore, this paper aims at analyzing life cycle impact assessment results obtained by means of several indicators to check if different indexes lead to similar results and if the number of indicators can be reduced.     

A conceptual framework for impact assessment within SLCA

Purpose  

This paper aims at spelling out the area of protection (AoP), namely the general concept of human well-being and the impact categories in social life cycle assessment (SLCA). The applicability of the so-called capabilities approach—a concept frequently used for evaluating human lives—is explored. It is shown how the principles of the capabilities approach can be transferred to the impact assessment within SLCA.     

A genetic approach for building different alphabets for peptide and protein classification

Background  

In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.     

Combined application of life cycle assessment and data envelopment analysis as a methodological approach for the assessment of fisheries

Background, aim, and scope  

The synergistic use of life cycle assessment (LCA) and data envelopment analysis (DEA) is proposed as a new methodological approach to link environmental and socioeconomic assessments of fisheries. Therefore, the goal is to combine LCA and DEA in order to increase the assessment ability of both tools when applied to these fisheries. Specifically, the joint inclusion of economic aspects and the consideration of currently underrepresented environmental impact categories are tackled.     

Simplifying a life cycle assessment of a mobile phone

Purpose

The possibilities for full life cycle assessment (LCA) of new Information and Communication Technology (ICT) products are often limited, so simplification approaches are needed. The aim of this paper is to investigate possible simplifications in LCA of a mobile phone and to use the results to discuss the possibilities of LCA simplifications for ICT products in a broader sense. Another aim is to identify processes and data that are sensitive to different methodological choices and assumptions related to the environmental impacts of a mobile phone.

Methods

Different approaches to a reference LCA of a mobile phone was tested: (1) excluding environmental impact categories, (2) excluding life cycle stages/processes, (3) using secondary process data from generic databases, (4) using input-output data and (5) using a simple linear relationship between mass and embodied emissions.

Results and discussion

It was not possible to identify one or a few impact categories representative of all others. If several impact categories would be excluded, information would be lost. A precautionary approach of not excluding impact categories is therefore recommended since impacts from the different life cycle stages vary between impact categories. Regarding use of secondary data for an ICT product similar to that studied here, we recommend prioritising collection of primary (specific) data on energy use during production and use, key component data (primarily integrated circuits) and process-specific data regarding raw material acquisition of specific metals (e.g. gold) and air transport. If secondary data are used for important processes, the scaling is crucial. The use of input-output data can be a considerable simplification and is probably best used to avoid data gaps when more specific data are lacking.

Conclusions

Further studies are needed to provide for simplified LCAs for ICT products. In particular, the end-of-life treatment stage need to be further addressed, as it could not be investigated here for all simplifications due to data gaps.     

Integrating gene expression and GO classification for PCA by preclustering

Background  

Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.     

Gene selection for classification of microarray data based on the Bayes error

Background  

With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.     

Bias in random forest variable importance measures: Illustrations,sources and a solution

Background  

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories.     

Reducing bias through process inventory dataset normalization

Purpose  

This paper explores a computational method to resolve some of the problems of external normalization in the life cycle impact assessment (LCIA) process of midpoint characterized impacts. Problems inherent to external normalization (per capita per year for a defined region) that reduce the ability to accurately calculate the most significant impact categories include

A framework for significance analysis of gene expression data using dimension reduction methods

Background  

The most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important for any number of categorical or continuous responses. We introduce a general scheme for analysis of expression data that combines significance testing with the interpretative advantages of the dimension reduction methods. This approach is applicable both for explorative analysis and for classification and regression problems.     

Metabolic innovations towards the human lineage

Background  

We describe a function-driven approach to the analysis of metabolism which takes into account the phylogenetic origin of biochemical reactions to reveal subtle lineage-specific metabolic innovations, undetectable by more traditional methods based on sequence comparison. The origins of reactions and thus entire pathways are inferred using a simple taxonomic classification scheme that describes the evolutionary course of events towards the lineage of interest. We investigate the evolutionary history of the human metabolic network extracted from a metabolic database, construct a network of interconnected pathways and classify this network according to the taxonomic categories representing eukaryotes, metazoa and vertebrates.     

Life cycle impact assessment and interpretation of municipal solid waste management scenarios based on the midpoint and endpoint approaches

Purpose  

Few studies have examined differing interpretations of life cycle impact assessment (LCIA) results between midpoints and endpoints for the same systems. This paper focuses on the LCIA of municipal solid waste (MSW) systems by taking both the midpoint and endpoint approaches and uses LIME (Life Cycle Impact Assessment Method based on Endpoint Modeling, version 2006). With respect to global and site-dependent factors, environmental impact categories were divided into global, regional, and local scales. Results are shown as net emissions consisting of system emissions and avoided emissions.