AI News, Can Artificial Intelligence Help Build Better, Smarter Climate Models ... artificial intelligence
Welcome to CHANCE
16 December 2018 Our understanding of how forests change remains limited, due to our inability to observe them over time.
In a study by Hansen et al., satellite imagery (Landsat) was used to map global-scale forest cover changes from 2000 to 2012 at high spatial resolution.
With this information, they determined how forests change in different countries and biomes (tropical, temperate, etc.) and were able to investigate the major causes of forest change around the world.
The methods and results provided here can be used by countries and agencies that are interested in monitoring forest change for management and conservation.
24 November 2018 Developed by the North American Marine Environment Protection Association, this easy-to-use guide introduces K-12 audiences to the marine environment and fosters ocean literacy through lessons exploring themes of Ocean health, Ocean Acidification, Ocean Exploration, and Marine Industry.
Use the resource with middle and high school students to demonstrate the many ways satellite imagery is being used to help us observe geological processes at work, enhance our understandings of interactions at play within various ecosystems, and expand our perspective about the human connection to and cultural importance of national parks and monuments.
For this reason, the University Water Council seeks nominations of outstanding undergraduate and graduate students engaged in water research and education at Penn State to be members of the Council.
A Letter of Nomination from a Water Faculty member describing the student’s strengths and qualifications to be a member of the Water Council, including the student’s interest in water related research and education. A
The Resume/CV should clearly state the current standing and academic pursuit of the student (undergraduate/graduate, semester of study/degree pursued, major/minor/concentration of study).
How artificial intelligence will affect the future of energy and climate
In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT—in the traditional industries of oil, gas, and electricity.1 That’s because IT was transforming how those industries function.
Other essays in this series explain what’s happening with AI and why it is such an important technical revolution.2 In this essay, I’ll look at how AI might be affecting the supply and demand for energy and the implications of AI for how modern society uses energy: climate change.
Even a big effort to control emissions will leave a lot of climate change—meaning that, in the future, much of “climate policy” will be focused on adapting to climate impacts and implementing quick responses in case of climate emergencies.3 Extremely intelligent systems for adapting to climate change impacts may make the cost of that adaptation more transparent and thus politically difficult to muster.
The impacts of AI are numerous, but four clusters of impacts seem most likely to affect energy and climate—two will alter the supply and demand for energy, and two will affect the ability of societies to understand how emissions are affecting the climate and how to manage those impacts.
This is a hard question to answer because it requires disentangling the effects of many other technological changes (e.g., improved drill bits, control systems for horizontal drilling, better materials for wind turbine blades, and less costly solar cells) from the specific effects of AI.
At the moment, my sense is that AI is having a bigger impact in oil and gas than in renewables because the kinds of activities that are unlocking new hydrocarbon resources—notably the shale revolution in oil and gas which requires mapping complex underground reservoirs and tailoring drilling methods4—are particularly well-suited to the recursive, complex learning processes that AI is well-suited to deliver.
The effects are likely larger than one percent—already, simple “nudge” interventions in power markets, for example reminding customers about the need to reduce energy consumption during peak periods and changing the default settings on thermostats—yield energy savings up to a few percentage points.
A few utilities are experimenting with systems, some large customers are actively managing energy systems with AI-based systems (because they can afford to amortize the cost over large savings), and some firms like Stem are emerging as intermediaries—making explicit AI offers to customers and providing the expertise needed so that even small customers can utilize these systems.
If the central message from the above discussion is that AI makes it possible for energy markets to reflect real-world conditions—and to be more efficient in matching consumer preferences with supplies—then there is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem.
Better and more efficient markets that can help consumers become more responsive to real-world conditions could help tamp down that enthusiasm for regulation and make practical a greater reliance on market-based instruments—such as carbon taxes.5 There is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem.
The downscaling process is complex and imperfect, in part because lots of local factors affect how broad changes in the climate are manifest where people actually live—along coastlines, near wildfire zones, in cities struggling with heat stress, and the like.
Existing research shows that there is a huge difference in the impact on public welfare from scenarios where climate change affects a society that doesn’t have an adaptation plan compared with a society that takes active adaptive measures.
For example, the most recent U.S. climate-impact assessment released in November 2018 demonstrates that active adaptation measures can radically reduce losses from some climate impacts—often with benefits that far exceed the costs.8 Extreme climate change is going to be ugly and will require hard choices—such as which coastlines to protect or abandon.
Such techniques might also make it possible to rely more heavily on market forces to weigh which options generate private and public welfare—if so, AI could help reduce one of the greatest dangers as societies develop adaptation strategies, which is that they commit vast resources to adaptation without guiding resources to their greatest value.
High levels of uncertainty, along with acute private incentives that can mis-allocate resources—for example, local construction firms and organized labor might favor some kinds of adaptive responses (e.g., building sea walls and other hardened infrastructure) even when other less costly options are available—mean that adaptation needs could generate a massive call on resources and thus a massive opportunity for mischief and mis-allocation.
As a matter of geophysics, climate change harms public welfare when general perturbations in the oceans and atmosphere get translated into specific climatological events that are manifest in specific places—specific coastlines, mountainous regions, public lands, and natural ecosystems.
Climate change is perhaps the largest market failure the world has seen so far—emissions of warming gases have global external consequences and the failure to impose emission taxes or other incentives means that firms and individuals are causing higher emissions and greater externalities than warranted.
For example, one way to cut the impacts of climate change on agriculture is to adopt early warning crop forecasting systems—so that farmers can adjust seeds, cropping methods, and planting times (among other variables) to reduce harmful impacts from the vagaries of weather.
- On 14. april 2021
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