Measuring how much semantic information a text conveys remains an open challenge: classical information theory quantifies uncertainty reduction but is agnostic to meaning. This thesis proposes a practical approach to quantify topic-specific semantic information in short texts. We target the domain of product labels and focus on environmental information, a context of high societal and regulatory relevance (e.g., Agenda 2030 and the forthcoming Digital Product Passport). This domain was selected as product labels usually contain concise and well-defined statements, often limited to a single claim, which minimizes ambiguity and makes them especially suitable for the quantitative analysis of semantic information. Our key idea is to estimate information coverage with respect to reference sentences, crafted in accordance with the Green Claims Directive issued by the European Union, that are assumed to be maximally informative.