A recent study presents various strategies to effectively eliminate greenhouse gas emissions from the energy sector in the United States by the year 2050. The results offer crucial information for policymakers and industry stakeholders to navigate efforts in combatting climate change.
A recent study presents various strategies to effectively eliminate greenhouse gas emissions from the energy sector in the United States by the year 2050. The results offer crucial information for policymakers and industry stakeholders to navigate efforts in combatting climate change.
“There isn’t just a single method to cost-effectively decarbonize our energy system,” states Jeremiah Johnson, the study’s co-author and a civil, construction, and environmental engineering professor at North Carolina State University. “In reality, we have plenty of technologies at our disposal. Our study aids in clarifying what those options are and how we might prioritize them.”
“Numerous models exist that aim to identify the cheapest method to decarbonize our energy system — essentially locating the best way to cut greenhouse gas emissions across areas like electricity generation, transportation, and industry,” explains Aditya Sinha, the study’s lead author and research scholar at NC State.
“However, these models struggle to fully capture the uncertainties present in such a complex system,” Sinha notes. “There are numerous technologies that could assist in decarbonization, making it challenging to ascertain how much flexibility we have in selecting which tools can lead us to the best results.”
“One approach to tackle this issue is to shift our focus from identifying a perfect solution to exploring alternative options that can get us very close to the most cost-effective path,” he adds.
For this analysis, the researchers defined “very close” as being within 1% of the optimal cost associated with decarbonizing the entire energy system.
In their work, the researchers utilized an existing model named Temoa, originally designed to identify the most economical route to achieving decarbonization. They executed the model to determine what the optimal cost would be, then added 1% to that figure and modified the model accordingly.
“The model then has to make thousands of choices,” Johnson explains. “How much solar energy should be constructed? Should homeowners replace natural gas heating with electric heat pumps? And so on. We ran our modified version of Temoa 1,100 times, directing the model to favor or disfavor specific technologies. This acknowledges that human decisions often stem from various factors beyond just economic considerations.”
“This methodology provided us with a clear range of technologies that could enable us to eliminate greenhouse gas emissions from the energy system while remaining within 1% of the optimal cost,” Sinha states.
The results can be categorized into four groups:
- Category 1 includes technologies that were consistently adopted in all 1,100 model solutions. This category involves the expansion of solar and wind energy generation, alongside increases in energy storage capabilities on the power grid.
- Category 2 contains technologies that were either greatly minimized or completely eliminated. This category particularly notes the substantial reduction in reliance on petroleum within transportation and the discontinuation of coal power generation without carbon capture and sequestration.
- Category 3 covers emerging technologies with various potential outcomes; some model scenarios indicated widespread use of these technologies, while others excluded them entirely. Examples of this category include direct air capture, which removes carbon dioxide from the atmosphere, and the application of hydrogen in transportation and industry.
- Category 4 involves technologies that the model typically did not employ, yet when they were used, they were relied upon heavily. These technologies include synthetic fuels derived from carbon dioxide and coal power plants that integrate carbon capture and sequestration.
“Running the model 1,100 times yielded an immense array of possible outcomes, making it challenging to know where to begin,” Sinha remarks. “Only through an extensive analysis of these outcomes could we identify these categories, providing a clearer understanding of our options and how we might prioritize them.”
“From a practical perspective, these findings indicate several key points,” Johnson explains. “Firstly, we must determine ways to promote the broader adoption of the technologies in Category 1.
“Secondly, we need to strategize on how to transition away from the technologies in Category 2 in a way that is orderly, fair, and timely,” emphasizes Johnson. “Thirdly, while we won’t need all of the technologies within Category 3, some will be necessary. Therefore, we need to invest in research and development to identify which technologies should be prioritized and how to implement them. Lastly, we must also explore research and development to ascertain if any technologies in Category 4 hold true value and, if so, how to maximize their potential.”
