Net Analysis: Switching to Solar Energy


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. 


Let’s look at an example.  Barlow Energy of South Carolina is looking to move $100M in production from fossil fuels to solar in 2022.  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 $100M in Industry 42 - Electric power generation - Fossil fuel and a positive $100M in Industry 44 - Electric power generation - Solar. The Group is set up using South Carolina Data Year 2020 with the Dollar Year set to 2022, 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 $100M impact.


We also see that this change will see a gain of 24 Indirect jobs and $8,853,150 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 $100M in fossil fuel energy would have a negative employment impact of 298 jobs. Of those jobs, 73 of these jobs are Direct jobs lost from the Fossil fuel industry, 144 of these jobs are Indirect jobs, and the remaining 80 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 73 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 $100M in solar energy would have a positive employment impact of 369 jobs.  Industry 44 - Electric power generation - Solar will see a Direct Employment impact of 106 jobs. The Indirect Employment impact is 169 jobs and the Induced Employment impact is 95 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 (-144 + 168 = 24).  

The overall net change in jobs is 71 (-298 + 369 = 71).  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.


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 Greenhouse Gas Satellite Account, Region Summary. By default it will show the net effect of the two Events, use the Event Name filter to select only the Solar Event. 

The largest greenhouse gas emission from the new solar power generation plant is 3.7M kgs of Carbon Dioxide (co2). The solar plant will also emit 68 kgs of methane (ch4). The rest of the GHG emissions primarily come from supply chain purchases (indirect effects) and a little from household spending (induced effects).


Next, we change the Event Name Filter to Fossil Fuel to compare the results. The loss of $100M in Output for the Fossil Fuel power plant will reduce co2 emissions in South Carolina by more than 1.3 billion kgs! If the power plant had continued operating in 2022, it would have generated 1.2 billion kgs of carbon dioxide, 108,230 kgs of methane, and 18,598 kgs of dinitrogen monoxide. 


Considering we modeled the loss in Output at the Fossil Fuel power plant as a negative event, if we remove the Event Name filter it will default to the total net effects as shown below. 


If Barlow Energy was interested in drilling down further into the co2 emissions of their supply chain, they could use the Environmental tab to select the Greenhouse Gas Details table, then set the Environment Tag to “co2” and the Impact to “Indirect”. We can use the arrow on the Unit Value column to sort by ascending. Then we can see the top ten Industries in both power plants’ supply chains that would contribute less co2 emissions in 2022: 


If we sort Unit Value to descending, we can see the top ten Industries that will increase co2 emissions in South Carolina due to the transition from fossil fuel power generation to solar power:


As evidenced in the tables, the transition from a Fossil Fuel power generation to a solar power generation plant will have both economic and environmental benefits to the South Carolina economy. Barlow Energy could further impact their co2 emissions by exploring the current supply chain for the proposed solar power plant to see if there is potential for substitution of intermediate inputs which could reduce their environmental impact.


It is also a responsibility of the analyst to assess the capacity of the local workforce. In the example, we analyzed a shift of $100M in Output leaving the fossil fuel industry (-$100M Industry 40 Output Event) and being gained in the solar energy industry ($100M Industry 42 Output Event). IMPLAN will estimate the loss of fossil fuel jobs (73) and gain of solar energy jobs (106), and reflect the net Direct Employment impact (32). IMPLAN assumes this net Employment effect is local, but it does not consider if the 106 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 73 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 18, 2023

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