Soft Technology: One More Hurdle for Clean Energy Adoption
A research team at MIT has created a quantitative model to differentiate between hardware expenses and soft costs. This distinction is critical for clean energy adoption.
Conventional wisdom dictates that as new technology is adopted in virtually any sector, costs are highest when the tech is introduced and decline significantly as efficiency and innovation improve. The same has held true for clean energy technologies, which have steadily evolved and improved over the last 40 years.
Clean energy technology. Image used courtesy of Adobe Stock
In fact, since 1980, the overall costs of installing solar energy have dropped precipitously, with some savings estimates calculating a 99 percent reduction in spending. However, such a figure can be misleading because of the misguided assumption that the hard work of innovation and accessibility for clean technologies is complete. When considering the future of clean energy adoption, engineers must continue streamlining to reduce soft costs for efficient clean energy deployment.
Understanding the Proportion of Soft Costs
Soft costs are important but often overlooked in favor of hardware concerns. To clarify soft costs with a large-scale example, let’s consider a solar power plant; the design and installation of the plant would be categorized as soft costs, which are separate from the hardware associated with the plant.
A critical observation of current clean energy adoption conditions is how soft costs have become proportionally significant compared to hardware costs. The image below makes it easy to track the expected hardware cost downtrend with one of the most familiar clean energy hardware: solar panels.
Long-term costs of solar panel hardware. Image used courtesy of Homeguide
In those 11 years, the steep decline in solar panel costs suggests that clean energy tech is cheaper and more accessible as the years pass. However, industry complacency with respect to soft costs has become a more salient and significant hurdle as soft costs increase proportionately over time.
Even when examined internationally by country, soft costs are considerably high regardless of location, suggesting an industry-wide neglect of soft cost reduction.
Average solar plant project costs by country. Image used courtesy of RatedPower
With just a cursory glance at this data, it is obvious how soft costs and installation constitute nearly half the total costs for every country. Given this substantial proportion, the only sensible path toward the future of clean energy adoption must address how soft costs impact fiscal bottom lines.
Exposing Inefficiencies in Soft Technology
A professor at MIT’s Institute for Data, Systems, and Society (IDSS), Jessika Trancik, has stepped in to work on understanding the relational dynamic between soft and hard technology and how the stagnation of soft technology can compromise cost efficiency.
Trancik and her team at MIT have developed a quantitative model that can be used to understand the relative cost components of hard and soft technology. Their initial application of this model on solar photovoltaic (PV) systems has confirmed the typical trend in the reduction of hardware costs and exposed the problems presented by soft technologies that are not being improved.
The utility of mapping out these soft tech deficiencies cannot be understated. As the margin for improving hardware technologies shrinks due to the widespread focus on hardware innovations, the primary opportunity for reducing clean technology adoption costs will depend on understanding the role that soft tech plays in total costs. The team at MIT has created a model that can move us toward a more precise understanding of the relative costs and complex dynamic between hard and soft technology.
This modeling will ultimately facilitate strategic decisions that must be made to either improve hardware to minimize any dependence on soft technology variables or researchers can shift their focus to improving soft technology directly.
Financial Stakes Are High
Every year, there are substantial increases in clean energy investments, and in 2022, there was a record $495 billion invested in renewable energy. Trancik and her team are accomplishing critical work that addresses a fundamental concern in the clean energy sector. To continue reducing costs as the world embraces clean energy, engineers will need data on soft costs to make targeted changes, and this quantitative model helps usher in a proactive approach to idiosyncratic cost mapping.