The amount of money national governments allocate toward defense-related intelligence is enormous and is widely seen as a holdover from decades past. However, the threats to national security that this traditional method of intelligence spending is meant to mitigate are being increasingly overshadowed by climate change-related security risks. To begin to truly face this threat, nations need both tactics and strategy that embrace new tools and bold acumen, and a focus on action.
Help is needed to compensate for the lack of alacrity among humankind to act decisively. Moving forward, the global community must have larger conversations that focus on how technological advancements can be utilized to galvanize increasing investment toward developing nations’ renewable energy goals and mitigating the impact of climate change.
Concerns about climate change have been voiced for years, perhaps making the threat seem less so. That thinking is deadly, as climate change is a true “threat multiplier” — meaning it gains strength and prowess each year.
While there is no silver bullet against climate change, utilizing technologies like artificial intelligence for climate change mitigation can provide green bullets to use in the battle. AI gives access to insights and actionable data that will help nations transition to cleaner sources of energy, and serves to maximize the impact of other distributed energy resources in the energy ecosystem. To check and then reverse climate change, we must weaken its strengths and sap its reinforcements. Nations can do much of that with policies that shift toward renewables and energy intelligence. The world needs faster and more intelligent energy systems, and the resources to pay for them.
While in the past, it was sufficient to do energy planning on the basis of installed capacity (megawatt or even gigawatt), in the future, it will be necessary to know exactly when and where each kilowatt-hour electricity is being produced. When we can directly correlate this to specific electricity demand at a precise location and time, a demand-side-economics approach becomes feasible in the energy sector. This is where AI and the internet of things (IoT) come in.
Nations can use data intelligence to help make political and investment-related decisions. Government funding and support are key in not only attracting investors, but also in creating clear political directives that support a shift toward clean energy sources. In today’s economically competitive renewables energy market, what is needed to maximize usage of these clean energy sources is a coherent energy transition plan that is backed by strong, clear political directives.
The United Nations has warned only a decade remains to act to avoid irreversible levels of global warming. There are new and developing products that produce an optimized match between future energy demand and power generation supply. These kinds of tools can provide insights into what energy sources are needed and where to find new sources, among other uses. These insights are especially crucial for developing nations.
Developed countries, the main contributors to the climate crisis, have the means and access to redefine their obligations into more tangible and achievable goals. Emerging countries lack much of the necessary investment needed to meet the challenge yet must be involved to ensure a whole-world approach.
Fortunately, there are different technologies, processes and systems beyond internet-connected artificial intelligence that governments of both underdeveloped and industrialized nations can explore. Nations that industrialized early on typically rely on centralized grids that, more often than not, use fossil fuels to generate electric power and are therefore incredibly carbon-intensive. Understanding this, governments of developed nations are better suited than, say, under-electrified developing countries to either incentivize or mandate the use of carbon-capture technologies in power generation facilities.
Developing nations have a comparative advantage for certain technologies, too. In sub-Saharan Africa and other developing areas where decentralized grids could soon become the standard, distributed ledger technology — blockchain — affords small-scale producers and end users a mechanism through which they can make transactions without the need for a central utility.
Indeed, it's worth remembering that AI and machine learning are no silver bullets. They are, however, catalysts. AI, machine learning and IoT afford governments the capacity — through procurement practices, subsidization, tax incentives, conducive regulatory frameworks, provision of grants and other means — to optimize the efficiency of their energy ecosystems and uncover additional opportunities to accelerate the emergence of a carbon-neutral reality at rates unattainable by humans.