The last posting ended up with a short discussion about automation vs. manual labor and whether or not that should (or could) influence the effectiveness of green manufacturing strategies. It actually plays into the larger issue of measuring social impacts/benefits/costs of any green manufacturing technology wedge as we move to sustainable manufacturing.
Recall the the “triple bottom line” of sustainability – economic, societal and environmental. This term was, apparently, originally mentioned by John Elkington in 1994 (he called it the 3-P’s: profit, people and planet) according to The Economist and the “people” part referred to “a measure in some shape or form of how socially responsible an organisation has been throughout its operations.” The Economist article sums the approach up using a “balanced scorecard” approach and reminding us of the fundamental principle of “what you measure is what you get, because what you measure is what you are likely to pay attention to. Only when companies measure their social and environmental impact will we have socially and environmentally responsible organizations.”
I’m fine with that. The measure part is the tricky bit – specially for social impact – but folks are working on that. In Bhutan, for example, a former king introduced the concept of “gross national happiness” (GNH) as part of a way to build an economy in Bhutan that would respect the unique cultural and spiritual values of the country (See Wikipedia, for example, or the GNP website for details.) This is in contrast to gross domestic (or national) product, GDP or GNP, that are usual measures of progress.
You can even take a GNH survey on line. Other academics and social scientists pursued this line of thinking and have come up with “index values” of GNH comprised of an average per capita of some rather logical components (again taken from the Wikipedia reference above):
1. Economic Wellness: a measurement of economic metrics such as consumer debt, average income to consumer price index ratio and income distribution
2. Environmental Wellness: a measurement of environmental metrics such as pollution, noise and traffic
3. Physical Wellness: a measurement of physical health metrics
4. Mental Wellness: a measurement of mental health metrics such as usage of antidepressants and rise or decline of psychotherapy patients
5. Workplace Wellness: a measurement of labor metrics such as jobless claims, job change, workplace complaints, etc.
6. Social Wellness: a measurement of discrimination, safety, divorce rates, complaints of domestic conflicts and family lawsuits, public lawsuits, crime rates
7. Political Wellness: a measurement of political metrics such as the quality of local democracy, individual freedom, and foreign conflicts
You may recall that in the IPAT (Impact) equation from previous postings, the “impact/GDP” was referred to as our core concern in manufacturing since if we can reduce the environmental impact per unit of product value to the customer we are on the road to improvement. Some disagree that GDP is a good measure of progress. Hence the alternate measures, like GNH.
For sure, a measure of happiness must be employment and reward for that employment – number 5 above. In addition, economic wellness, number 1 above, as well as related mental and physical wellness can be associated with quality employment. But how do we (or should we) factor this into our technology wedge discussion?
In the last posting the question was asked – if a company reduces the amount of machinery used in manufacturing and replaces that machinery with manual labor does that help? The response was typically academic – it’s complicated. Clearly there are some products and processes that don’t lend themselves to this “conversation.” But, for assembly tasks, one might make the argument that more human labor (replacing automation) might produce the product using less energy and resources and, ultimately, making the product easier to disassemble at its end of life. And then we need to consider the quality of the labor (meaning is it dull and repetitive or intellectually stimulating and, for sure, is it free from danger or other safety issues.)
So, this long lead-in is to set the stage for a discussion of the “energy of labor.” This discussion is based on a paper we published some time ago and included in the recent thesis of the student co-author. The paper, titled “Energy Use per Worker-Hour: A Method of Evaluating the Contribution of Labor to Manufacturing Energy Use,” was written by Teresa Zhang in my lab and presented at the 14th CIRP International Conference on Life Cycle Engineering in 2007 and published in the proceedings of the conference “Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses” edited by S. Takata (it’s on Amazon!).
Energy is an important metric of environmental impact and manufacturing efficiency. We know that in life-cycle assessment (LCA) analyses, energy consumption as a key parameter that can dominate environmental impacts such as global warming potential, carcinogenic emissions, and acidification potential. Energy assessment is also effective as an indicator of manufacturing efficiency. As yield, manufacturing cycle efficiency, process capability, and other manufacturing performance metrics improve, energy use per unit output decreases accordingly.
The metric of energy use was popularized largely due to the work of Howard Odum, who has written numerous books on energy and environmental accounting since the 1970’s (for example, Odum, H. T., 1971, Environment, Power, and Society, Wiley-Interscience, New York). In a publication in 1996 (Environmental Accounting: Emergy and Environmental Decision Making, Wiley, New York), he presented several methods of quantifying the energy use of labor, in terms of metabolic energy, national fuel share, national emergy share, and as a function of the level of education enjoyed by a worker. Others have also discussed the energy use of labor in the form of caloric content of food consumed. Calculated as such, the conclusion is that the energy contribution of human labor to energy use is negligible. But there is more to the story.
The methodology presented in this posting from the paper is related to economic input-output (EIO) LCA, in that both methods aim to quantify environmental impacts that may not be included in process-based LCA. Because both methods take a top-down approach, presenting averages for an industry or country, they do so without tremendously increasing the work of LCA practitioners.
Energy of labor and EIO-LCA should not be applied at the same level of analysis because many sources of energy use would be double counted. However, energy of labor can be very effective if incorporated into hybrid process-based EIO-LCA, as shown in the figure below, where EIO-LCA is used to assess activity upstream of the process-based analysis. The energy use of labor enriches the horizontal scope of process-based LCA, while EIO captures vertical supply chain impacts.
Schematic of process based LCA and energy of labor used in series with EIO LCA.
In addition to improving the accuracy of LCA, evaluating the energy of labor can be applied to extend the decision making capabilities of LCA. The energy of labor enables us to quantify and inform decisions that introduce or reduce the degree of automation, deal with the location of a plant, or involve labor intensive process steps.
We’ll continue the discussion from here in Part 2 next time.
David Dornfeld is the Will C. Hall Family Chair in Engineering in Mechanical Engineering at University of California Berkeley. He leads the Laboratory for Manufacturing and Sustainability (LMAS), and he writes the Green Manufacturing blog.