Environmental indicators for industrial optimization and design

Companies use standard financial indicators to determine their business success and optimize their business opportunities. However, sustainable development demands for an integrated approach to economic, environmental and social indicators. Although a lot of indicator initiatives are under development, methodology of measuring sustainable development is not standardized. Besides, most indicators that have been developed evaluate either the global, regional or national level (the macro-level) or the plant, project or product level (micro-level), which means that the upstream or downstream effects of a company or ”businesses as a whole” are often ignored. This study goes beyond the plant or product level and aims at the industrial system level by attempting to select and translate environmental indicators into business performance indicators for industrial optimization and design. Industrial optimization and design should enable to screen and compare industrial systems (supply chains) on their environmental impacts. The proposed environmental business performance indicators (EBPI’s) from this study in combination with a mathematical optimization model (not part of this study) can serve as a support tool for business decision-making (e.g. adapting and designing supply chains). In the first phase of this study existing indicator frameworks are reviewed on their potential to contribute to the development of the EBPI's. Indicator frameworks can be distinguished between macro, meso and micro-level frameworks. Macro-system frameworks give good insight in the (global, regional or national) state of the environment, but do not describe environmental indicators and their relation to business in such way that it is possible to directly use them as environmental business performance indicators. Meso-level frameworks focus on the industrial system or on the company level. Although this is on the level required for industrial system optimization, the indicator frameworks mainly focus on dematerialization of business processes (production) instead of decreasing environmental impact directly. Dematerialization in itself does not necessarily decrease environmental impacts and can even lead to environmental impact trade-offs. The most effective way to decrease environmental impacts from businesses is to decrease environmental business pressures through technology, or more precisely, through adaptive innovation. Adaptive innovation is the application of newly invented tools or methods to adapt to macroscopical changes in the physical environment. Environmental business pressures that can be influenced by technology are included in micro-system frameworks (e.g. LCA, EIA). Therefore, this thesis has developed an industrial screening method that is based on LCA pressure indicators and that distinguishes between inherent and non-inherent environmental pressure indicators. The second phase of this study elaborates upon the industrial screening and optimization method. In the method supply chains are screened on inherent indicators. Inherent environmental indicators measure environmental pressures that are characteristic of a supply chain and that cannot be prevented by best practice technologies of the specific chain. Contrary, non-inherent environmental indicators are both environmental pressures that are not characteristic of a supply chain (but are inherent to other technology chains), as well as environmental pressures that are characteristic of a specific supply chain and that can be prevented by applying best practice technology of the specific chain. Inherent indicators can than serve as inputs to industrial optimization models that compare multiple supply chains on their environmental impact. Non-inherent indicators can be used to optimize a single supply chain by micro-level methodologies (e.g. LCA or EIA). Environmental business pressures (both inherent and non-inherent) can be structured by LCA impact categories. These impact categories arrange single business pressures into one impact by scientifically based aggregation methods. This results in a concise number of impact indices that can be used in an industrial optimization model. Basically, all LCA impact categories include business pressures that can be inherent to selected supply chains. However, this thesis makes a distinction between standard and optional impact categories. The distinction is based on the scale of impact instigated by underlying business pressures. Provided that underlying indicators are inherent to the specific technology chain, standard impact categories that should always be included in industrial optimization are land use, freshwater withdrawal, climate change, ozone depletion, acidification, photo-oxidant formation, eutrophication and ionizing radiation. These impact categories are of global or glocal nature. Glocal impacts in this case are local impacts that take place on a global scale, such as land and freshwater use or eutrophication. Environmental business pressures with a local impact can be regarded as optional in the environmental optimization method (e.g. human and ecotoxicity, odour and noise). Exceptions to this rule can be made for, for example, particulate matter, which is an environmental business pressure that falls into the impact category human toxicity. Particulate matter is a perfect example of a business pressure with a glocal impact and should therefore be included as an indicator (if inherent) in the screening and optimization method. In the final phase of this study, the industrial screening and optimization method has been applied on the Canadian oil sands industry (in the format of the ISO 14040 protocol). Despite the lack of information on some parts of the oil sands chain and although further studies in this field are required, the study has resulted in a representative overview of inherent environmental indicators of the oil sands technology chain. These inherent environmental indicators (together with financial and social indicators) can serve as input to an industrial optimization model of energy systems.

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