INTRODUCTION
A Net Analysis reports a holistic look at the effects resulting from a change in production or spending in the economy, both the positive and negative. Net Analyses that involve two different Industries will have net winners and net losers beyond just the two directly impacted industries. It can be useful to examine these sides in an analysis and can help create a truer impact picture. It is easiest to do this when you create multiple Events and analyze them in the same Group.
Think about this example. When a new store opens up, local purchasing power doesn’t automatically increase to support it. Local residents and visitors are still likely to spend the same amount of their disposable income. These dollars must now, however, be split between more retail options. The new store will win in terms of selling goods, but the older stores will likely lose sales.
This is, of course, unless it can be argued that the new store is actually filling in “import substitution.” For example, if this was the first furniture store in the Region, people no longer have to leave to buy a bookshelf and therefore the money is no longer leaked out of the local economy.
EXAMPLE
Let’s look at an example. Barlow Energy of South Carolina is looking to move $200M in production from fossil fuels to solar in 2026. So we will see a decrease in fossil fuels at the same time we see an increase in solar operations. We want to look at the Net Analysis; the overall change in the economy because of both of these Events.
To set this up, we create two Events in South Carolina: a negative $200M in Industry 35 - Electric power generation - Fossil fuel and a positive $200M in Industry 37 - Electric power generation - Solar. The Group is set up using South Carolina Data Year 2020 with the Dollar Year set to 2026, the year of the proposed switch.
When we run this analysis, the Results screen will default to show us the net effects on South Carolina resulting from the change from fossil fuels to solar power generation - the net effect of both Events. We see that the Direct Output is $0 because we had both a negative and a positive $200M impact.
We also see that this change will see a gain of ~61 Indirect jobs and ~$30M in Indirect Output. The Direct Effects, aside from Output, and Induced Effects all see increases; but why?
We can look at just the negative effects of the loss of fossil fuel power or just the positive effects of solar power by applying the Filter. If Barlow Energy wants to focus only on what jobs will be lost, we can Filter for our fossil fuels Event. Overall, this loss in -$200M in fossil fuel energy would have a negative employment impact of 403 jobs. Of those jobs, 105 of these jobs are Direct jobs lost from the Fossil fuel industry, 185 of these jobs are Indirect jobs, and the remaining 113 are Induced jobs.
We can see which Industries are experiencing negative job impacts by viewing the Industries by Impact in the Employment Results. Notice we see the same negative Direct employment impact of 105 in the fossil fuel sector.
If, on the flip side, they only want to show job gains from the switch, Filter for only the solar event. Overall, this gain in $200M in solar energy would have a positive employment impact of 725 jobs. The Electric power generation - Solar industry will see a Direct Employment impact of 254 jobs. The Indirect Employment impact is 246 jobs and the Induced Employment impact is 224 jobs.
We again can dig into which Industries these jobs impact by looking at the Detailed Employment Results.
As a net effect, the switch will have a positive effect on Indirect Employment because the employment loss from fossil fuels supply chain is less than the employment gain from the solar supply chain (-185 + 246 = 61).
The overall net change in jobs is 71 (-403 + 725 = 322). We can therefore conclude that in South Carolina, investing in solar energy is better for overall employment than that of fossil fuel energy. Note that this might be a very different answer if we examined West Virginia where a significant amount of coal is mined or in New Hampshire where they have far fewer sunny days than South Carolina.
ADDING ENVIRONMENTAL IMPACTS
Barlow Energy of South Carolina is transitioning their energy to renewable sources to comply with the new Energy Freedom Act signed into law by the state legislature. The company is aware solar energy production produces lower emissions, but they want to quantify the total net effect on Greenhouse Gas (GHG) emissions from making the switch. Using IMPLAN’s Environmental Data, developed using the EPA’s Environmentally Extended Input-Output data (EEIO), Barlow Energy was able to explore the estimated GHG emissions from their proposed transition.
From the Results screen, select the Environmental tab right below the navigation bar. This will open up a dropdown menu where you can navigate to the Impact Industry Details. By default it will show the net effect of the two Events, use the Event Name filter to select only the Solar Event and the Impact filter to Direct.
The largest greenhouse gas emission from the new solar power generation plant is 20.7K kgs of Nitrous oxide. The solar plant will also emit 64 kgs of Sulfur hexafluoride.
We can see the rest of the GHG emissions from supply chain purchases (indirect effects) and household spending (induced effects) by adjusting our filters.
Next, we change the Event Name Filter to Fossil Fuel to compare the results. The loss of $200M in Output for the Fossil Fuel power plant will reduce co2 emissions in South Carolina 2.2K kgs! If the power plant had continued operating in 2026, it would have generated 2.2K kgs of carbon dioxide, 20.7K kgs of nitrous oxide, and 64 kgs of Sulfur hexaflouride.
Considering we modeled the loss in Output at the Fossil Fuel power plant as a negative event, if we remove the Event Name and Direct filters it will default to the total net effects across both events and all impact types as shown below.
NET EMPLOYMENT CONSIDERATIONS
It is also a responsibility of the analyst to assess the capacity of the local workforce. In the example, we analyzed a shift of $200M in Output leaving the fossil fuel industry (-$200M Industry 35 Output Event) and being gained in the solar energy industry ($200M Industry 37 Output Event). IMPLAN will estimate the loss of fossil fuel jobs (105) and gain of solar energy jobs (254), and reflect the net Direct Employment impact (149). IMPLAN assumes this net Employment effect is local, but it does not consider if the 149 new positions will be filled by unemployed people living in the region, employed people from other local industries, new people moving into the region, or by an increase in workers commuting into the area for those positions. Instead, IMPLAN relies on an average regional commuting to determine the portion of new income that is earned by in-commuters. IMPLAN, as an I-O model, cannot dynamically determine how new jobs will be filled or cause turnover, potentially affecting employment and production in another areas of the regional economy. However, IMPLAN's Occupation Data is a great resource to supplement your knowledge of a regional workforce. For example, we might reference Occupation Data to research job placement options for the 105 fossil fuel workers who lost their job, and to evaluate the workforce that can fill the new solar energy jobs.
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Written September 19, 2019
Updated October 1, 2026