Originally published on LinkedIn
A paper published in 2020 in the journal Technological Forecasting and Social Change described international job creation estimates during the global energy transition towards 100% renewable power systems. Worldwide, renewable energy is projected to account for 80% of total direct energy jobs by 2050. It can be difficult for the public to understand the process of creating a job, renewable or otherwise. The related idiom of giving somebody a job is a bit less problematic, since it implies one is matching an existing need with a skill or ability. Neither wording implies the cost or the price of the endeavor, or what it takes to do it, nor does it imply a timeframe. In the context of discussions surrounding a clean energy economy and strategies to address climate change, policymakers and leaders throughout the world often speak of their proposed programs in terms of millions of jobs being created, induced and saved. Just how would one go about qualifying that with some degree of certainty so that it can be conveyed to the general public for their support?
Top Down or Bottom Up
There are two methods that have been used for the specific case of the United States: a top down approach and a bottom up analysis informed by data. An example of the former used measured data reported in tables from the US. Bureau of Economic Analysis (BEA) to calculate the ripple effects from the ordering of a power plant by investors and/or via some government spending or policy. For example, in my the field of solar energy, deploying a photovoltaic (PV) power plant with a specific nameplate capacity (in MW) would imply a fixed number of PV modules, with their solar cell materials (like silicon or CdTe), including metal frames, backsheet and glass, as well as the racking, mounts, inverters and the power conditioning equipment - in addition to the actual site preparation, construction and, finally, operations and maintenance (O&M). This would send orders down the supply chain to companies that provide each aspect.
The peer reviewed paper was published by a researcher now at Boston University (Peltier, 2017) that examined various energy-related technologies including: wind, solar, bioenergy, geothermal, hydroelectric, weatherization, energy efficiency, smart grid, mass transit & freight rail, oil and gas, and coal. Its methodology utilized BEA data, an Input-Output (I-O) approach and matrix operations that are relatively easy to code (i.e., in Python). The output from such studies is given in terms of a Keynesian multiplier called a jobs multiplier, which is the jobs created, or that result, from an investment in millions of dollars (jobs/M$). It was concluded that each 1 million US$ shifted from brown (fossil fuels) to green energy will create a net increase of 5 jobs. Since new BEA data is available, a collaboration by several government departments, economists, asset owners, financiers and energy industry professionals is warranted to reproduce and update the calculations, and to compare results to current benchmarks for actual employment. Data from the U.S. Bureau of Labor Statistics (BLS) could provide additional insights regarding salaries and expected income.
The second method to estimate job creation involves an analysis of the data from the U.S. American Recovery and Reinvestment Act (ARRA). Researchers from Sweden looked at jobs numbers, bottom up, from types of specific clean energy projects reported on the Recovery Act website, via the Council of Economic Advisor's (CEA) supplement to the third quarterly report and with input from the Jobs and Economic Development Impact (JEDI) models of the National Renewable Energy (NREL). A paper published in Renewable and Sustainable Energy Reviews (Mundaca & Richter, 2015) summarized the findings and also tabulated the jobs for three types of clean energy projects. ARRA clean energy categories included energy efficiency, grid modernization, advanced vehicles and fuels technologies, traditional transit and high-speed rail, carbon capture and sequestration, green innovation and job training, clean energy equipment manufacturing, and renewable energy (RE) generation. The paper considered only the latter three.
Overall, the results indicated that the stimulus programs had a positive effect on the renewable energy sector. One of the study's key findings was that not only is the support of technological progress and innovation necessary, but also training and workforce development so that the labor force can be prepared for the necessary jobs. The ARRA training programs took longer to set up than was planned. The funding for projects by the Department of Energy (DOE) did not sufficiently coordinate with training programs at the Department of Labor, so some graduates were not ready for the projects when they started. Even an optimized stimulus would not only take some time to implement, but the training for the expected jobs would need to be coordinated over several years. That said, some skills found in one energy industry are also transferrable to emerging energy industries.
Comparing the Results
These are but two of a multitude of different models and studies, each with underlying assumptions and caveats. Surprisingly, the two methods described above yield approximately the same macroeconomic results: using 5-7 jobs/$M for an investment of $2 trillion, one can expect 10-14 million jobs to be created. This of course is a simplistic and static approach, but it can still be a useful starting point applied to each project or policy initiative, with an understanding of limitations and assumptions that can be explored further. One such initiative where this has been done is the Biden administration's recent offshore wind announcement that cited work by Woods McKenzie which included job estimates. Noteworthy is that the plan is to coordinate at least three major departments in a whole-of-government approach.
Implications for the Energy Transition
Of course the devil is in the details for jobs created both by government stimulus and/or private investment. They can both be tracked using geospatial data science to overlay renewable energy resources with locations where new employment opportunities are needed the most. For example, a recent study by the Brookings Institution described how renewable energy jobs can uplift fossil fuel and disadvantaged communities, thereby remaking climate politics. Many fossil fuel hubs could be transformed into renewable energy hubs and fossil fuel workers could be retrained. A brief by the Environmental and Energy Study Institute (EESI) described how coal country in the U.S. can adapt to the so-called Energy Transition. There is no shortage of recommendations and ideas on how transition to a clean energy and carbon neutral economy. One thing is clear: robust and transparent systems should be set up to collect jobs data and track the investments so that better jobs/$M calculations (both bottom up and top down) can result in greater public awareness and increased confidence over time. Keeping carbon dioxide and other greenhouse gases out of the atmosphere shouldn’t have to rely on numbers appearing out of thin air. With studies like those outlined here, transparency and public education are enabled and inevitable.
NB: I have placed a set of draft slides with more details on the calculations here on ResearchGate. The two primary references for this article are:
[1] Heidi Garrett-Peltier, Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model, Economic Modelling 6 (2017) Pages 439-447. https://doi.org/10.1016/j.econmod.2016.11.012
[2] Luis Mundaca, Jessika Luth Richter, Assessing ‘green energy economy’ stimulus packages: Evidence from the U.S. programs targeting renewable energy, Renewable and Sustainable Energy Reviews 42 (2015) Pages 1174-1186. https://doi.org/10.1016/j.rser.2014.10.060