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Nevertheless, to the best of the authors\u2019 knowledge, the problem of dealing with the potential inaccuracy\/missingness of such data at the country and economic sector levels has been overlooked. Thereby, in this article we apply a supervised machine learning technique called Matrix Completion (MC) to predict, for each country in the available database, annual CO<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> emissions data at the sector level, based on past data related to all the sectors, and more recent data related to a subset of sectors. The core idea of MC consists in the formulation of a suitable optimization problem, namely the minimization of a proper trade-off between the approximation error over a set of observed elements of a matrix (training set) and a proxy of the rank of the reconstructed matrix, e.g., its nuclear norm. In the article, we apply MC to the imputation of (artificially) missing elements of country-specific matrices whose elements come from annual CO<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> emission levels related to different sectors, after proper pre-processing at the sector level. Results highlight typically a better performance of the combination of MC with suitably-constructed baseline estimates with respect to the baselines alone. Potential applications of our analysis arise in the prediction of currently missing elements of matrices of annual CO<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> emission levels and in the construction of counterfactuals, useful to estimate the effects of policy changes able to influence the annual CO<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mrow\/>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> emission levels of specific sectors in selected countries.<\/jats:p>","DOI":"10.1007\/s11590-023-02052-2","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T09:02:27Z","timestamp":1695718947000},"page":"2203-2219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of annual CO2 emissions at the country and sector levels, based on a matrix completion optimization problem"],"prefix":"10.1007","volume":"18","author":[{"given":"Francesco","family":"Biancalani","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5427-4328","authenticated-orcid":false,"given":"Giorgio","family":"Gnecco","sequence":"additional","affiliation":[]},{"given":"Rodolfo","family":"Metulini","sequence":"additional","affiliation":[]},{"given":"Massimo","family":"Riccaboni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"2052_CR1","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1146\/annurev-economics-080217-053433","volume":"11","author":"S Athey","year":"2019","unstructured":"Athey, S., Imbens, G.W.: Machine learning methods that economists should know about. 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