Section VII PRO: Basic Environmental Processes

Green Processes and Projects: Systems Analysis


Given the level of inadvertent impacts generated from human breakthroughs in science and technology post industrial revolution, be it from the combustion engines based on coal/fossil fuel or the use of plastic in modern day commodities, it is imperative to develop a systems perspective on the sustainability of a process or project. This chapter introduces a holistic approach to environmental appraisal for minimizing the impacts from transboundary interactions between the material flows of a system, thereby ensuring efficient management of any potential environmental impacts. This approach combines application of “dynamic science-based” geo-spatial analysis with “static inventory-based” life cycle assessment (LCA) to understand the full-scale of impacts generated from a proposed process or project at the systems level, thereby ascertaining their true “greening” potential. Hereafter a “process” implies a set of activities targeting a specific output (e.g., 1 MW electricity, 1 ton of a material), whereas a “project” implies a combination of cross-cutting processes to achieve a specific objective (e.g., constructing a new building, greening of transport network). Two case studies are presented, one each for a green process and a green project, to enable the readers to get a grasp of the usefulness of this tool in global environmental management, more so in the context of supply chain spread diffusively over numerous geographical regions.

Systems Approach in Environmental Management

In the context of fool-proofing human existence from untoward climate change feedbacks, management of both built-up and natural environments has gained center stage in recent years. It is widely agreed now that effective environmental management involves striking a fine balance between the technological innovations underpinning its viability (essential for economic growth) and the constraints to long-term sustainability (Figure 1). This chapter deals with how a systems approach can be applied to achieve this goal.

Traditionally, systems approach has its roots in industrial (or laboratory) processes that simply comprise a series of related activities aimed at optimizing production (and hence the profit). However, in the face of climate change, much of the environmental research conducted over the last decade has changed our notion of the scale of management problem we face. Take, for instance, the case of cheap biofuel provisioning in the first decade of the 21st century—the demand for palm oil in the Western world has caused massive deforestation in Indonesia and Amazonia. Having a wider scope to environmental management, beyond what can be considered as local activities within a country’s borders, has given way to the systems approach in environmental management. Although simple in principle and rigorously tested in processing industries, its adaptation to the wider environmental problems comes with extreme operational challenges. The latter is mainly highlighted in terms of data availability, essential to the success of this vision. Despite this, it is considered as an efficient framework to facilitate a more holistic management of material, energy, and pollution across a range of related activities, usually spread over large geographical areas.

The main advantage of using a systems approach is to figure out the hotspots of environmental concerns (greenhouse gas emissions, ecotoxicity, air pollution, etc.) within a system. For this purpose, the scope of environmental management has now crossed local boundaries to a systems scale, encompassing the whole range of supply-chain spreading over several countries to ensure global sustainability. These associated activities form what is known as a “life cycle” in terms of environmental appraisal. It facilitates mapping of the “stocks” and “flows” of emissions (also called environmental burdens) through different stages. The concept has been used to develop the LCA and Material Flow Analysis (MFA) methods. Both these techniques have proven to be useful diagnostic tools. Detailed texts describing the LCA/MFA methods exist in the literature. Interested readers can find some useful sources listed in different parts of this chapter; in particular, The Hitch Hiker’s Guide to LCA1'1 serves as a good starting point to develop fundamental understanding of these concepts.

The main objective of applying LCA/MFA is to assess the critical environmental burdens and impacts contributing to the adverse effects on human and ecological health. These critical pollutants can then

Constraints to environmental management at a systems scale

FIGURE 1 Constraints to environmental management at a systems scale.

be dealt with, and their impacts mitigated, through implementation of effective control measures. This ensures a solution to an environmental problem at the local scale while considering its footprints at the global scale, literally over the whole life cycle. As a consequence, this approach offers a robust framework for implementing holistic sustainability.

Best Practice Guidelines in Systems Analysis

Standard Protocols

In essence, the International Organization for Standardization ISO 14044121 specifies four mandatory steps for quality assurance in LCA, namely—goal and scope definition, inventory analysis, impact assessment, and interpretation. To ensure that the environment as a whole is protected, the process chain forming the system can be scoped in two ways. The first comprises pre-chains involved in excavation of the resources and their transportation to the industrial site, the end-of-pipe emissions from the plants, and the disposal of the wastes at the end of the cycle. This approach in life cycle thinking is usually referred to as “cradle-to-gate” since it follows an activity from the extraction of raw materials (i.e., cradle) to the delivery of the product or service (i.e., the exit gate). The other, in addition to accounting for all the above, also includes one-off construction and demolition of the infrastructures or end-of- life processing and disposal of the equipment/commodities used. Appropriately so, it is then called a “cradle-to-grave” system.

As a standard practice, the resources utilized and the emissions added to the environment in all these activities are usually modeled on a unit basis, typically annual turnover for the industrial products and annual usage for the services. This is termed as the “functional unit” of analysis in LCA. Typical functional unit for an assessment can be “activities over lyear,” representative of the quantitative metric of the output of products or services that the system is expected to deliver. Likewise, the typical timescale for an assessment could range from hours to 100years. The latter is specifically relevant for assessing long-term environmental impacts such as global warming potential and acidification.

The spatial context of setting up a life cycle model is also essential in order to establish its system boundaries. Typical spatial scales range from local (e.g., urban) to wider environment (e.g., remote excavation site, mines) and possibly representing a global scale. This aspect of the analysis is useful for consistency checks, comparing different LCA results, and ensuring they have been conducted on comparable system boundaries. Given that life cycle approach facilitates assessment of a full spectrum of process chains involved in a process for all the activities, it is recommended to group the burdens and impacts obtained from an analysis into two distinct categories: (1) arising in the direct environment from the main activity/ ies under investigation (commonly known as “foreground”) and (2) arising from a whole series of linked pre- and post-chain activities in the wider global environment (known as “background”). This is clearly shown in Figure 2, which shows a series of activities associated with the production of electricity from biofuel through a schematic representing a power generation system. In this figure, the main activity (i.e., the cogeneration plant) is shown as the foreground (shaded region), whereas the peripheral activities, associated primarily with the sourcing of the raw materials, shipment, and final disposal are shown in the background. On the one hand, this approach enables clear accounting of the environmental impacts and, on the other, it offers insight into the hotspots at each stage of process chain, facilitating effective management of the problem through visualizing the entire system at the same time. This is considered superior to resource- and cost-intensive piecemeal solutions encountered in traditional management approaches, specifically so in the context of achieving global sustainability.

The environmental impacts from LCA are usually calculated from mass balances of the input/output flows on the basis of the problem-oriented (midpoint) approach. It is a baseline method that provides a list of impact assessment categories grouped into obligatory impacts, used in most LCAs. A baseline indicator is considered suitable for simplified studies since it utilizes the principle of best available practice when several methods for obligatory impact categories are available.

Split between foreground and background activities from a systems perspective. Source

FIGURE 2 Split between foreground and background activities from a systems perspective. Source: Tiwary and Colls.131

Uncertainty Assessment

To decide on the future courses of action based purely on results of LCA analysis, one needs to account for the uncertainty in these results—this enables reaching better decisions. The information on uncertainty in LCA is usually contained in the assessment of probability of realistic representativeness of the results. This is expressed in terms of the confidence bounds on LCA results, which illustrate the region within which the true values have an estimated likelihood of falling.

This section covers only the fundamentals of uncertainty inherent in LCA. Readers will be guided to use dedicated literature to pursue a more in-depth uncertainty assessment exercise.

A number of approaches have been suggested in the literature for the integrated consideration of both technical and valuation uncertainties involved in LCA. The former is associated with the uncertainties in data collection, while the latter is inherent to the impact assessment method used. The reason we need to care about uncertainty in LCA is because the statements or assertions we tend to make about the world on the basis of direct LCA outputs may be wrong. These errors have been mainly associated with either uncertainty or variability in the outputs. Whereas variability is considered to be inherent in the real world, uncertainty is mainly associated with inaccurate measurements, lack of data, and/or model assumptions.141 The following can be considered as a rigorous (although not an exhaustive) list of categories of uncertainties identified in the literature: [1]

programming), the results of a parameter uncertainty analysis can be misleading and hence provide no useful information.

  • Spatial variability: Application of spatially averaged data to model specific processes in certain parts of the world leads to these discrepancies.
  • Temporal variability: These are mainly attributed to time-dependent variations in emissions and other technical process characteristics.

  • [1] Parameter uncertainty: This is due to the uncertainty within the large number of parametersused in LCA models. It leads to uncertainty in the final output from the LCA exercise. Empiricalinaccuracy, non-representativeness (incomplete or outdated measurements), and lack of data arecommon sources of parameter uncertainty. • Epistemological uncertainty: The use of the information in databases for life cycle inventory (LCI)introduces epistemological uncertainty since the system where the data is to be used to model aprocess may differ from the system where the data was generated.151 • Model uncertainty: In situations where an LCA model suffers from uncertainty in the underlying model assumptions and the basic model calculations (departure from the default linear
< Prev   CONTENTS   Source   Next >