, the growth of the digital economy, whose output is usually immaterial, limits the role of gdp in measuring economic activity and wellbeing. digitalization has dramatically changed how businesses are conducted and goods and services purchased. this brings us to the productivity paradox : productivity in industrialized countries is growing less than in past decades while economies are experiencing rapid digitalization. jerome powell noted that current statistics may understate productivity growth because they miss the value that we increasingly derive from new technologies, such as internet connections and smartphones. the measurement of the digital economy is now an important challenge for statisticians and economists. we need new methodological insights. to answer some of these questions, in 2016 banca d ’ italia put together a team to assess the potential benefits and hidden risks of big data, artificial intelligence and machine learning. despite controversies about privacy issues, despite password hacking and other worrisome aspects of online life, the world continues to embrace the internet and social media. it is estimated that more than 5. 1 billion people ( two thirds of the world ’ s population ) use a mobile phone and 4. 4 billion people take advantage of internet services. in 2018 the number of internet users recorded 9 per cent growth ( see the we are social blog ). so we have plenty of new data which can be mined to improve our knowledge of economic developments. to understand and exploit these data we need to bring together different skillsets : those of economists, statisticians, engineers and computer scientists. what are the advantages of these data? in his lecture on ‘ big data and measurement : from inflation to discrimination ’, the mit professor roberto rigobon said that the biggest advantages of organic data ( i. e. big data ) as against designed data ( such as administrative data ) are that : ( i ) they reduce the cost of collecting and providing information and ( ii ) they do not introduce distortions arising from the behaviour of respondents. people don ’ t lie to their gps or to google. the banking and financial industries have also been profoundly affected by these developments. central banks must react in a timely manner and develop new tools to carry out their missions : monetary policy, banking supervision and payment system oversight. in these two days you have seen many applications which can improve central bank activities. some papers focused on the extraction of quantitative information from texts and images : this can be useful for forecasting financial market variables, predicting headline inflation, and analysing the real estate market. 1

this should serve as a warning, as the number of people worldwide using these tools, even for entertainment, is increasing at an incredible rate. there is the dreadful prospect that, in a high - demand scenario, digitalization - related energy needs could even double in 2026. 5 clearly, these developments call for further progress in increasing the share of low - carbon sources in the power mix, 6 and for measures to improve the energy efficiency of these digital services. 7 luckily, on the other side, there are also many good things that the digital transition can do for the green transformation. ai tools – such as machine learning and deep learning – can, for example, assist economists, investors, and portfolio managers in improving the use of esg data to build efficient portfolios8 as well as help scientists in assessing the extent of biodiversity loss, 9 map methane leaks10 or water availability. 11 in what follows, i would like to mention some practical uses of digital innovation for the green transformation that should be taken into consideration and studied further, on the reinforcing of the green and digital transitions, see muench, s. et al., 2022. ‘ towards a green and digital future ’. eia ( 2024 ). tracking electricity consumption from u. s. cryptocurrency mining operations. luccioni, s., jernite, y., and strubell, e. ( 2023 ). power hungry processing : watts driving the cost of ai deployment? proceedings of the 2024 acm conference on fairness, accountability, and transparency. iea 2024. electricity 2024. analysis and forecast to 2026. according to the above mentioned iea electricity report, low - carbon electricity generation is set to cover all global demand growth over the period 2024 - 26. zhu, h. et al., 2023. future data center energy - conservation and emission - reduction technologies in the context of smart and low - carbon city construction, sustainable cities and society, volume 89, february 2023, 104322. lanza, a. a. g. et al., 2023. machine learning, esg indicators, and sustainable investment, financial risk management and climate change risk, springer nature switzerland. thompson, t., 2023. how ai can help to save endangered species, nature, 623, 232 - 233. o ’ donnel, j., 2024. a new satellite will use google ’ s