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Things to check
Key
IMPUTATIONFlags
ignore-inconsistent
<p>Missing values for individual fuels within a survey are automatically imputed by the model (Stoner et al., 2020). For surveys where fuel use is only reported for the whole population (i.e. with no urban or rural disaggregation), the urban and rural values are automatically imputed by the model (Stoner et al., 2020).</p>
<p>No estimates are reported for low- and middle-income countries with no data (Bulgaria, Lebanon, Libya). All central estimates are reported alongside measures of uncertainty. Where countries have very limited survey data (e.g. only one survey suitable for modelling within 1990-2020), the measures of uncertainty are naturally wider. High income countries are assumed to have transitioned to clean fuels and technologies, and are reported as >95% of their population using clean fuels and technologies.</p>
<p><strong>• At regional and global levels</strong></p>
<p>Low- and middle-income countries with no data were excluded from regional and global aggregations, and values of 100% clean fuel and technology use were used for High income countries for regional and global calculations.</p>
<ul>
<li><strong><em>Au niveau national : </em></strong></li>
</ul>
<p>Pas de rapport pour les pays à faible et moyen revenu sans données. </p>
<p>On suppose que les pays à revenu élevé qui ne disposent d'aucune donnée sont passés aux combustibles et aux technologies propres et que, par conséquent, >95% de leur population utilise des combustibles et des technologies propres.</p>
<ul>
<li><strong><em>Aux niveaux régional et mondial : </em></strong></li>
</ul>
<p>Les pays à faible et moyen revenu ne disposant d'aucune donnée ont été exclus des agrégations régionales et mondiales.</p>
<p> Les pays à revenu élevé qui ne disposent d'aucune donnée sont supposés être passés aux combustibles et technologies propres, et on suppose donc que 100 % de leur population utilise des combustibles et technologies propres aux fins du calcul des agrégats régionaux et mondiaux.</p>