Tuomas Mattila

Quantifying the Total Environmental Impacts of an Industrial Symbiosis - a Comparison of Process-, Hybrid and Input−Output Life Cycle Assessment

Industrial symbiosis, representing resource sharing and byproduct use among colocated firms, is a key concept of industrial ecology. Local co-operation in industrial symbioses can reduce raw material use and waste disposal, but material and energy flows extending outside symbiosis boundaries can cause considerable environmental impacts. These external impacts are often ignored in industrial symbiosis studies. In this study, we compared process, hybrid and input-output life cycle assessment (LCA) approaches in quantifying the overall environmental impacts of a forest industrial symbiosis, situated in Kymenlaakso, Finland. Conclusions from an earlier process-LCA were strengthened by the use of hybrid-LCA as local emissions were found to cause less than half of the global impacts. In some impact categories, the whole impact was caused by supply chain emissions (land use, metal depletion and ozone depletion). The cutoff in process-LCA was found to be less than 25%, except in metal depletion and terrestrial ecotoxicity. Input-output LCA approximated hybrid-LCA results well in most impact categories, but seriously underestimated land use and overestimated terrestrial ecotoxicity. Based on the results we conclude, that input-output based LCA can be used to analyze the global impacts of an industrial symbiosis, but a careful interpretation of the results is necessary in order to understand the influence of aggregation and allocation.

Methodological Aspects of Applying Life Cycle Assessment to Industrial Symbioses

In view of recent studies of the historical development and current status of industrial symbiosis (IS), life cycle assessment (LCA) is proposed as a general framework for quantifying the environmental performance of by‐product exchange. Recent guidelines for LCA (International Reference Life Cycle Data System [ILCD] guidelines) are applied to answer the main research questions in the IS literature reviewed. A typology of five main research questions is proposed: (1) analysis, (2) improvement, and (3) expansion of existing systems; (4) design of new eco‐industrial parks, and (5) restructuring of circular economies. The LCA guidelines were found useful in framing the question and choosing an appropriate reference case for comparison. The selection of a correct reference case reduces the risk of overestimating the benefits of by‐product exchange. In the analysis of existing systems, environmentally extended input‐output analysis (EEIOA) can be used to streamline the analysis and provide an industry average baseline for comparison. However, when large‐scale changes are applied to the system, more sophisticated tools are necessary for assessment of the consequences, from market analysis to general equilibrium modeling and future scenario work. Such a rigorous application of systems analysis was not found in the current IS literature, but would benefit the field substantially, especially when the environmental impact of large‐scale economic changes is analyzed.

Land use indicators in life cycle assessment

PurposeInclusion of land use-related environmental aspects into LCA methodology has been under active development in recent years. Although many indicators have been developed and proposed for different aspects of land use (climate change, biodiversity, resource depletion and soil quality), many of indicators have, as yet, not been tested and compared in LCA applications. The aim of this study is to test the different LCIA indicators in practice in a case study of beer production.Materials and methodsNine different indicators were selected to represent three different impact endpoints of land use: resource depletion, soil quality and biodiversity. The beer production system included all life cycle stages from barley cultivation and the production of energy and raw materials to the serving of beer at restaurant. Several optional system expansions were studied to estimate the possible impacts of substituting feed protein (soybean, rapeseed and silage) with mash coproduct from brewing. A comparison with wine production was also made for illustrative purposes.Results and discussionThe majority of the land use impacts occurred in the cultivation phase, but significant impacts were also found far down the supply chain. The system expansions influenced the overall results markedly, especially for land transformation, soil organic carbon (SOC) and several of the biodiversity indicators. Most of the land use indicators led to results that were consistent with each other. In the inventory and impact assessment phase, challenges were faced in obtaining reliable data. Additionally, the lack of reliable, regional characterization factors limits the usability of the land use indicators and the reliability of the LCIA results, especially of the SOC indicator. None of the studied indicators fulfills all the criteria for an effective ecological indicator, but most have many positive features.ConclusionsAll tested land use indicators were applicable in LCIA. Some indicators were found to be highly sensitive to assumptions on land transformation, which sets high requirements for LCI data quality. Scarcity of land use LCI data sources limits validation and cross-comparison. Interpretation of indicator results is complicated due to the limited understanding of the environmental impact pathways of land use.RecommendationsNone of the tested indicators describes the full range of environmental impacts caused by land use. We recommend presenting land occupation and transformation LCI results, the ecological footprint and at least one of the biodiversity indicators. Regarding soil quality, the lack of reliable regional data currently limits application of the proposed methods. The criteria of effective ecological indicators should be reflected in further work in indicator development. Development of regionalized characterization factors is of key importance to include land use in LCA.

Uncertainty and Sensitivity in the Carbon Footprint of Shopping Bags

Carbon footprints for several shopping bag alternatives (polyethylene, paper, cotton, biodegradable modified starch, and recycled polyethylene) were compared with life cycle assessment. Stochastic uncertainty analysis was used to study the sensitivity of the comparison to scenario and parameter uncertainty. On the basis of the results, we could give only a few robust conclusions without choosing a waste treatment scenario or limiting the parameter space. Given the scenario of current waste infrastructure in Finland, recycled polyethylene bags seem to be the most preferable (−7 to 24 g CO eq./bag) and biodegradable bags the least preferable (38 to 60 g CO eq./bag) option. In each analyzed waste treatment scenario, a few parameters dominated the uncertainty of results. Most of these parameters were downstream of the shopping bag manufacturing (consumer behavior, landfill conditions, method of waste combustion, etc.). The choice of waste treatment scenario had a greater effect on the ranking of bags than parameter uncertainty within scenarios. This result highlights the importance of including several scenarios in comparative life cycle assessments.

An Environmentally Extended Input-Output Analysis to Support Sustainable Use of Forest Resources

The use of environmentally extended input-output analysis was demonstrated for quantifying the overall sustainability impacts of forest industries in the Finnish economy in 2005. Direct greenhouse gas emission, land use, employment and import impacts of economic sectors were transformed into impact intensities of products. The intensities were used to construct a final demand based emission inventory demonstrating the relative importance of export, investment and consumption activities in causing environmental and social impacts. The calculations were presented using an aggregated input-ouput table, which makes it possible to repeat the calculations using standard spreadsheet software. Therefore the study can be used as an accessible primer to the use of input-output methods in sustainability assessment.

Carbon Footprint of Mobile Devices: Open Questions in Carbon Footprinting of Emerging Mobile ICT Technologies

Carbon footprinting is becoming a mainstream practice in product design and marketing. At the same time, consumer products are becoming so complex that their footprinting becomes increasingly difficult. The supply chain of a typical mobile ICT device (i.e., smartphone) contains hundreds of suppliers in several continents and the product itself is composed of several complex subassemblies. The use of the smartphones also has large systemic effects (e.g., cloud computing, server load, increased consumption, and green applications), which are commonly left outside the scope of product carbon footprints. In this chapter, we argue that the parts which are most easily left out of a study are in fact the most significant for the whole product life cycle. The chapter is arranged in subchapters for each topic: components and subassemblies without emission inventory data available, energy consumption of data transfer and storage in clouds, the effect of recycling and consumer behavior, induced consumption, and the potential of green applications.

Developing a Model for Long-Distance Freight Emissions and Energy Consumption

The FORESIGHT process required many quantitative estimates, which were not available for long-distance freight transport. Current emission inventories, impacts of forecasts, and previously published scenarios report usually total transport emissions, without focusing specifically on long-distance freight. Since detailed statistics were unavailable, a quantitative model was developed to estimate the figures from existing data. The model was used to estimate the emissions and energy consumption of future transport systems described in the business-as-usual forecasts and in the backcasts. This chapter describes the model structure and parameterization, with an emphasis on the use of the model to estimate the current status of the freight transport system in 2005.

Use of Input–Output Analysis in LCA

Input–output analysis can be used as a tool for complementing the traditionally process-based life cycle assessment (LCA) with macroeconomic data from the background systems. Properly used, it can result in faster and more accurate LCA. It also provides opportunities for streamlining the LCA inventory collection and focusing resources. This chapter reviews the main uses of input–output analysis (IO) to ensure consistent system boundaries, to evaluate the completeness of an LCA study and to form a basis for in-depth inventory collection. The use of IO as a data source for social and economic sustainability metrics is also discussed, as are the limitations of the approach. All aspects are demonstrated through examples and references both to recent scientific literature and publicly available datasets are provided. The aim of the chapter is to present the basic tools for applying IO in practical LCA studies.