But in light of current events, fears are mounting that these incumbent forces behind America’s clean energy transition may not be enough to sustain the country’s pursuit of its emissions reduction goals.
Indeed, numerous governments, business, and civil society leaders around the world have voiced concerns to that effect in recent months. Notable figures from institutions such as the IMF, IEA, and the United Nations are urging the U.S. and other governments to make expanding support for climate-friendly industries a central component of their post-pandemic economic relief efforts. And for good reason, too.
Consensus is building among leading economists that “green recoveries” are the most cost-effective means of restoring health and stability to our world’s post-COVID economies and, ideally, fueling their growth for years to come.
More important still, however, is the existential imperative to drastically reduce global greenhouse emissions in major emitting nations in order to avert the catastrophic effects of global climate change.
And while many U.S. renewable energy investors today remain bullish on what they see as “safe havens” in solar and wind farms and other clean power generation assets, the rate at which these emissions-reducing technologies are being deployed still is ultimately insufficient to meet Washington’s nationally determined contributions (NDCs) under the Paris Climate Agreement. And as time under lockdown wears on, some observers are beginning to revise their forecasts for new U.S. wind and solar installations downwards.
Lockdown-Induced GHG Reduction — More Than a Short Relief for Global Climate?
Examples of other post-crisis economic recovery episodes suggest that the historic declines in global GHG emissions we’re seeing as a result of government-imposed lockdowns will be short-lived. Numerous analyses have found that the global economic rebound from the 2008-2009 financial crisis significantly offset the decrease in global CO2 emissions seen in 2009. In the United States, the annual percent change in gross GHG emissions was +3.4 percent in 2010, compared to -6.3 percent in 2009, according to the U.S. Environmental Protection Agency. And in the years since, annual net U.S. GHG emissions have slowly crept back up to levels observed before the financial crisis.
Knowing this gives rise to an important question. How might policymakers, business leaders, and investors in the U.S. renewables sector use the disruption caused by the novel coronavirus to reduce U.S. GHG emissions in accordance with Washington’s NDCs?
The post-financial crisis American Recovery and Reinvestment Act (ARRA) is a useful guide for answering this question. When this stimulus package was signed into law in 2009, it was billed as the largest-ever single investment in the U.S. clean energy sector, with some $21 billion of the total $840 billion allocated to provide direct financial support to the development and deployment of renewable energy technologies. Through the combination of extended production and investment tax credits, cash grants, the establishment of basic innovation research programs, and other provisions, ARRA incentivized innovation and attracted enormous private investment, which precipitated a wave of new solar and wind power generation installations.
More than a decade later, ARRA is widely credited as being one of the driving forces behind the reduction of U.S. GHG emissions below their peak in 2007. However, this is difficult to reconcile in light of emerging evidence, which suggests that, over the long-term, ARRA’s emissions-reducing effect is not significant enough to guarantee the U.S. fulfill its NDCs.
It is, therefore, imperative that U.S. policymakers, business leaders, and investors think outside the proverbial box. These stakeholders must seek alternative means of not only improving the generation capacity and efficiency of renewable power installations but also lowering barriers to private investment in the sector.
Data Intelligence — Still a Buried Treasure for an Efficient Green Recovery
Regardless of whether the attraction of private capital to the renewable sector is met via a “green” stimulus, relevant stakeholders would do well to make better use of the resources already at their disposal, namely the wealth of data generated by installed IoT assets in the renewables sector.
This driving imperative to innovate is behind the recent surge in investment from venture capital, utilities, and private equity players into digital technologies like artificial intelligence (AI) and machine learning to reap fewer risks and get better returns on renewable energy.
Every day, modern wind farms or solar parks produce thousands of pieces of data. Yet, this data is too often neglected by the parties for which it offers the greatest potential to better understand an asset’s real value, status, and present and future performance without physical inspection. The question, then, is how these parties might make better use of the information their IoT assets generate.
After our year-long experience with renewable energy asset management, the answer for us is clear: Cloud-based IoT platforms can be instrumentalized to harness, structure, and analyze the continuous, decentralized, and unstructured data from a wide range of renewable energy assets.
By leveraging SAP Cloud Platform and SAP Data Intelligence, we developed a cloud-based platform that aggregates and structures technical and financial data from wind farms and solar parks and enriches them with externally-sourced information such as meteorological data or, soon, electricity market data, before ultimately translating the results of these analytics into financial indicators. Thanks to the data intelligence provided, investors, financing banks, and supranational organizations gain the necessary insights needed to minimize investment risks and maximize returns.
To take things one step further than that, using AI and machine learning, it’s possible to conduct early failure detection for connected assets. It accomplishes this by monitoring technical data — e.g., performance metrics generated by wind turbine components — and translating it into actionable intelligence. In effect, this platform can identify and report an asset’s physical anomalies to managers long before they would normally be discovered by technicians.
In sum, users can trust that the unique combination of IoT and AI equips them with a more complete understanding of the current and future performance of their renewable power assets. Importantly, these algorithms enable investors and asset holders to optimize their portfolios in real-time. Lending banks, too, can make use of the data intelligence generated by these platforms, which allows them to evaluate future credit loan performance quickly and in advance of covenant breaches.
The Energy Transition — A Reliable Investment Program Thanks to Data Intelligence
The future that greater adoption of data intelligence by the U.S. clean power sector portends is promising. Renewable energy property owners, investors, and financing banks who expand their use of data intelligence can better address some of the prevailing shortcomings in renewable power generation — intermittence, technical failures, etc. — and will, therefore, maximize the output of installed physical systems and improve operational and maintenance cost savings without compromising supply resiliency or security.
AI and machine learning technologies together represent the next stage in the evolution of the U.S. and global renewable energy sectors. Yet, beyond improving an owner’s or investor’s margins, it is critical that all stakeholders recognize data intelligence for what it is: a demonstrated solution for drastically reducing greenhouse gas emissions.