at banks. while it is still too early to look at lessons learned about resolving such problems from this round of banking difficulties, we can draw on the lessons of the past. the 1980s and early 1990s offer examples of what can be done to resolve banking problems. for example, the united states suffered oil and agriculture - related regional recessions in the 1980s and a real - estate and leveraged buyout - led recession in the early 1990s. severe real - estaterelated problems developed around the same time in many european countries, with serious repercussions for some banking systems. today, banks in most of these countries are much stronger, and until recently, highly profitable. what happened? i would like to suggest five steps that form a common theme in the more successful recoveries. first, banks, often with the assistance of their supervisors, ascertained the true dimensions of their credit problems. the result of the process was realistic assessments of : a total of actual losses in the portfolio that needed to be charged off ; the potential additional losses, both in a “ most likely ” and “ worst case ” scenario ; and the exposures that needed to be considered in “ workout ” mode. the workout loans were those which had sufficiently severe problems that it was time to stop viewing them as “ business as usual ”, but to see them as requiring special attention. second, the workout loans or exposures were assigned to the special expertise of workout specialists. banks built their own staffs or outsourced the activity to another financial institution, a consulting firm or a boutique. in the early 1990s, some banks took an even more drastic step and sold the loans to firms that specialized in managing distressed debt. the key, however, was to get these exposures in the hands of those who could work with borrowers or the markets to maximize the value from problem portfolios. the workout process must not be allowed to interfere with the continued provision of credit to the economy, however. the key policy concern in the early 1990s in the united states shifted quickly from worries about bad banks to a credit crunch as banks aggressively tackled their loan problems. and that is why it is so important to segregate the “ workout ” portfolio from the remaining loan portfolio. when a dedicated staff is working out bad loans, the bank ’ s officers can devote their attention to the continued sound operation of the bank — the current agenda of serving the bank ’ s customers well and the forward agenda of developing and executing a sound business

ai to map methane leaks from space, mit technology review, february 14, 2024. shaikh, m. and f., birajdar, 2024. artificial intelligence in groundwater management : innovations, challenges, and future prospects, international journal of science and research archive, 2024, 11 ( 01 ), 502 - 512. potentially also among economists like you. although at the moment these potential positive uses are still overshadowed by the large amount of energy required by digital technologies, this does not mean that researchers should not push the frontier in these areas. i will start by discussing ai - generated energy demand and supply as well as weather forecasts12 and how they can support climate mitigation and adaptation. i will then turn to the use of digital solutions to reinforce the green transition. ai can help with climate transition and adaptation ai solutions can foster the reliability of energy systems, deliver higher living standards, and ensure safety for populations and production. the power of ai possibilities is something we experience directly : nowadays smartphones and digital assistants provide a number of cheap and simple home automation solutions ; by coupling the internet of things ( iot ) with ai techniques, we can monitor and optimize the heating and cooling of our homes. outside of these day - to - day examples, however, there are many more ways that ai can be seen as a ‘ climate game changer ’. our smart - home solutions are just the tip of the iceberg. gaining a greater understanding of both when power is available and when it is needed is crucial to ensure the efficiency and reliability of the power grid, especially once an increasing share of intermittent renewable sources are integrated. the deployment of smart meters to collect information, and of digital technologies such as machine learning techniques for data analyses can lead to better forecasts of energy demand and supply, leading to improvements in energy efficiency. in addition, by supporting ‘ demand response ’, technology could minimize discrepancies between demand and supply, reducing ‘ peak loads ’ and the need to add capacity to manage those peaks, and in turn supporting the resilience and security of the energy infrastructure. in addition to enhancing energy demand and supply forecast and management, ai technologies can help improve weather forecasts as well as the early warning detection of acute climate events, which are essential tools for mitigating the impact of adverse events on populations and assets. progress in forecasting weather patterns and building climate scenarios could help vulnerable communities and authorities to adapt to climate change13 and could