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ENVIRONMENTAL ACCOUNTING METHODS: HOW THEY WORK
We concentrate now on the characteristics of “how » EAMs work and how they meet expectations and challenges with the help of an archetypical EAM workflow covering all steps required from EAMs to meet these expectations. This workflow, shown in figure 1, draws on, and extends, the description of a Life Cycle Assessment (ISO, 2006a) and the approach by Mayer (2008) to analyse issues of sustainability indices.Four possible outcomes of EAMs, resulting from the explicit or implicit application of five steps, are considered. The first outcome is an “inventory – direct”. This inventory is a collection of heterogeneous flows of the “direct” type, i.e. a classical inventory by source. This inventory is completed once the “system design” (step 1) and the “data collection and preparation” (step 2) are completed. Information from this inventory can then be re-allocated along global productionFriot Damien. Comptabilité environnementale et mondialisation. Thèse MINES 32 ParisTech, 2009. consumption chains in step 3 “allocation” based on internal relations from the system. This results in a global inventory with a life cycle perspective (outcome 2 “inventory – global life cycle”). In order to reduce the complexity and heterogeneity of the available information, one or several “synthetic indicator” can be generated by aggregating flows at the level of Pressure or Impacts (step 4). Eventually, the aggregated indicator is compared to reference values in the last step “normalization & comparison” (step 5), resulting in a “performance indicator” to ease decision-making.
SYSTEM DESIGN
“System design” consists in describing the socio-economic system considered in the assessment of an entity or the outcome of an activity. The system boundaries must consider environmental-society
interfaces and boundaries between societies. Setting the boundaries of a socio-economic system determines the type of environmental flows that should theoretically be included in an inventory to eventually compute an indicator that is assumed representative of the system described. The relations within a socio-economic system describe how the components of a system are connected through their inputs and outputs.
System design plays a key role since it influences the rest of the EAMs steps, and thus their capacity to meet expectations. Expectations of soundness (exp.6 in Table 1) and comparability (exp.1) in a modelling context, i.e. a context characterized by simplification and truncation are described here. The expectations of a life cycle perspective (exp. 3) and analytical capacities (exp. 9), also strongly influenced by the system design are described in other steps.
A review of the problems of setting boundaries is provided for LCA and Input-Output Analysis by Reap (2008a). We add here additional elements related to the type of rationales applied to set boundaries between socio-techno-economic activities and issues linked to boundaries between these activities and the environment.
Two types of rationales can be applied to set boundaries: physical and socio-economic rationales. Physical rationales are usually established specifically for environmental assessments, e.g. in the ISO 14044:2006(E) for LCA (ISO, 2006b). Suh et al. (2004) recommend to set cut-off criteria depending on the importance of the expected contribution of an activity to the total environmental load, mass or energy within a system. The cut-off is thus applied at the “end” of production chains (upstream or downstream). On the contrary, socio-economic rationales used in approaches like IOA have other original aims; since their original goal was a representation of the economic system alone, the environmental relevance of their system boundary must be considered. In IOA for example, boundaries are set between the activities of economic and non-economic agents or between observed and non-observed economy, i.e. underground, illegal or informal activities (OECD, 2002). If a large Friot Damien. Comptabilité environnementale et mondialisation. Thèse MINES ParisTech, 2009. 33 informal economy is not included, the level of truncation of the modelled system may therefore be large and not be environmentally relevant. In a context of globalization, where underground activities can be a large part of some developing economies, and thus of related environmental flows, this aspect increases in importance. This issue adds to the challenge of setting proper geographic boundaries in top-down approaches that Lenzen (2001) qualifies as potentially as important as issues linked to cutoff in bottom-up approaches.
Comparability between indicators is possible when the indicators cover the same underlying reality:
their system boundaries with respect of socio-techno-economic activities are consistent with one another. Indicators at macro- and upper-meso-scale can be compared between studies because boundaries of socio-economic nature are based on an internationally agreed framework, the System of National Accounts (SNA) (United Nations, 1993). The potential for comparisons at upper-meso-scale is however reduced for two reasons. First, the implementation of this framework varies between countries: the international classification of sectors ISIC is, for example, regionalised differently in the EU (NACE classification) and in the USA (NAICS classification) resulting in potential inconsistencies between studies based on these classifications. Second, even in the case of countries using the same classification, like NACE in Europe, similar activities are classified differently since economic structures are different: the degree of vertical integration of sectors influences, for example, this classification (Eurostat, 2001). International comparisons with monetary values also face the wellkno issues of dealing with market exchange rates and purchasing power parities (Eurostat & OECD, 2006; Peters & Hertwich, 2007). In bottom-up studies, system boundaries that may be similar in theory are implemented on a case-by-case basis and comparability between different studies and with socio-economic indicators is thus strongly reduced.
DATA COLLECTION & ADAPTATION
The “data collection & preparation” step consists in collecting data on socio-economic activities and
the related environmental flows, and adapting it to conform to the system description, to eventually
generate the final data sets used for the assessment. Data sets can be generated for one or multiple periods. A reliable and complete final data set is a clear expectation for generating sound results with an EAM (exp. 6). Challenges from globalization question the usability of indicators (exp. 8) due to the already mentioned very large data needs. The reliance on few trade data sets of questionable reliability is also a challenge to overcome.
Final data sets are always an approximation resulting from multiple computations steps like the application of conversion factors, e.g. for the conversion from dry to wet biotic resources in MFA, or
the matching between different classifications to join data sets, e.g. to go from categories of environmental requirements by source to categories related to socio-economic activities, or the use of proxies in case of missing data (Eurostat, 2003). The reliability and completeness, without gaps, of collected data as well as the robustness of preparation steps and their assumptions determine thus the reliability and completeness of the final data sets. Since these preparation steps are usually performed before practitioners use data, their documentation is important for evaluating the soundness of an indicator. The full documentation of these steps, including limitations, is however not current practice even if guidelines exist for some EAMs and some data sets include uncertainties. Some methods have been proposed to assess data quality, like the data quality matrix proposed by Weidema for LCA (1998) that could be used for this purpose. Björklund (2002) surveys approaches to improve reliability in LCA with uncertainty analysis and this survey is valid for other EAMs. The application of these methods is however not a current practice according to the literature and further developments are required to ensure that users have a proper understanding of the limitations of the indicators from EAMs.
A global perspective set new challenges for delivering such data sets by extending their spatial, temporal and technological coverage with respect of socio-economic activities and extending the type of flows included to better represent environmental concerns in developing countries. An extensive spatial coverage has two advantages. First, establishing assessments from the viewpoints of a large number of countries, and second a better modelling of traded goods by considering the characteristics of each country of origin along production chains. An extensive temporal coverage means a possible identification of time trends or up-to-date data sets through frequent updates. A frequent update is crucial for the evaluation of countries experiencing rapid structural, energetic or legal changes, or production activities based on emerging or fast evolving technologies like genomics or nanotechnologies. A low spatial and temporal coverage may be compensated through modelling of missing Friot Damien. Comptabilité environnementale et mondialisation. Thèse MINES ParisTech, 2009. 35 data with detailed knowledge of the different technologies and their inputs, for example energy sources, and their outputs. An extensive spatial and temporal coverage is not yet the rule for both bottom-up and top-down approaches even if large efforts have been pursued in this direction for the last few years. Data is also lacking for many technologies, particularly new ones.
Table of contents :
Table des matières
Tables et figures
Glossaire
Chapitre 2 : Diagnostic et analyse des EAMs
2.1 Résumé étendu
2.2 Are environmental accounting methods meeting societal expectations and challenges in a global economy?
1. Introduction
2. Constraints from societal expectations and challenges from globalization
2.1 Societal expectations
2.2 Challenges from globalization
3. Environmental Accounting Methods: What they are
4. Environmental Accounting Methods: How they work
4.1 System design
4.2 Data collection & adaptation
4.3 Allocation
4.4 Aggregation: Pressure & Impacts
4.5 Comparison & normalisation
5. Meeting societal expectations and global challenges: an overview for some EAMs
6. Analytical framework for EAM analysis
6.1 Dimension 1: Inherent qualities of the approach
6.2 Dimension 2: Maturity and auditability
6.3 Dimension 3: Adaptation to global challenges
6.4 Dimension 4: Usability
6.5 Dimension 5: Analytical potential
6.6 Dimension 6: Compatibility, Comparability and integration potential
6.7 Dimensions 7, 8: Improvement potential in environmental accounting and decision-making Friot Damien. Comptabilité environnementale et mondialisation. Thèse MINES ii ParisTech, 2009.
7. Conclusion
8. References
Chapitre 3 : Modèle intégré Economie-Environnement-Impacts
3.1 Résumé étendu
3.2 Delocalization of environmental impacts on human health due to global value chains
Abstract
Methods
References
1. Supplementary Information Guide
2. Supplementary methods
3. Supplementary results
4. Supplementary references
Chapitre 4 : Modèle économique et outils analytiques
4.1 Résumé étendu
4.2 EU carbon tariffs & sharing scheme of the Chinese de-carbonisation costs:
A structural analysis based on Multi-Regional Input-Output models.
1. Introduction
2. Description of the TREI-C MRIO model
3. Sector-aggregated approach & underlying-flows decomposition
3.1 Underlying-flows decomposition
4. TREI-C MRIO model: data sources
5. Case-studies: Carbon tariffs & sharing scheme
6. Discussion & conclusion
7. References
Chapitre 5 : Conclusion
Chapitre 6 : Références (introduction et conclusion)
