From the AI community: A panorama of AI/ML applications in green-tech, it's dense, interactive summary at: [https://www.climatechange.ai/summaries](https://www.climatechange.ai/summaries) Contains a nice impact-taxonomy for green-tech solutionism: High Leverage - solutions to known bottlenecks. Long-term - solutions with impact after 2040. High Risk - (i) tech may not succeed, (ii) uncertain impact on GHG emissions (Jevons paradox), (iii) negative externalities. Some take-aways: REBOUND EFFECT (Jevons paradox): The Jevons paradox in economics refers to a situation where increased efficiency nonetheless results in higher overall demand. For example, autonomous vehicles could cause people to drive far more, so that overall GHG emissions could increase even if each ride is more efficient. In such cases, it becomes especially important to make use of specific policies, such as carbon pricing, to direct new technologies and the ML behind them. See also the literature on rebound effects and induced demand. Fossil fuel industry: for instance, innovations that seek to reduce GHG emissions in the oil and gas industries could actually increase emissions by making them cheaper to emit. OPTIMIZE FOR GHG EMISSIONS, NOT COST: Electricity grid: Previous work has used dynamic programming to set real-time electricity prices that maximize revenue, and similar techniques could be applied to create prices that instead optimize for GHG emissions. Supply chains: In 2006, at least two Scottish seafood firms flew hundreds of metric tons of shrimp from Scotland to China and Thailand for peeling, then back to Scotland for sale – because they could save on labor costs. PROJECTION, PREDICITION AND FORCASTING: Protect peatlands: A single peat fire in Indonesia in 1997 is reported to have released emissions comparable to 20-50% of global fossil fuel emissions during the same year. Ecosystem monitoring: For example, tree cover can be automatically extracted from aerial imagery to characterize deforestation. At the scale of regions or biomes, analysis of large-scale simulations can illuminate the evolution of ecosystems across potential climate futures. A more direct source of data is offered by environmental sensor networks, made from densely packed but low-cost devices. FINANCE: Climate analytics: The other main approach to climate finance is climate analytics, which aims to predict the financial effects of climate change, and is still gaining momentum in the mainstream financial community. Since this is a predictive approach to addressing climate change from a financial perspective, it is one where ML can potentially make a greater impact. Climate analytics involves analyzing investment portfolios, funds and companies in order to pinpoint areas with heightened risk due to climate change, such as timber companies that could be bankrupted by wildfires or water extraction initiatives that could see their source polluted by shifting landscapes. Approaches used in this field include: using NLP techniques for identifying climate risks and investment opportunities in disclosures made by companies as well as for analyzing the evolution of media coverage of climate change to dynamically hedge climate change risk; using econometric approaches for developing arbitrage strategies that take advantage of the carbon risk factor in financial markets; and ML approaches for forecasting the price of carbon in emission exchanges.