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The research question addressed here is whether the semantic value implicit in environmental terms in an activity description text string, can be translated into economic value for firms in the construction sector. We address this question using a relatively new applied statistical method called Latent Dirichlet Allocation (LDA). We first identify a satellite register of firms in construction sector that engage in some form of environmental work. From these we construct a vocabulary of meaningful words. Then, for each firm in turn on this satellite register we take its activity description text string and process this string with LDA. This  softly-classifies  the descriptions on the satellite register into just seven environmentally relevant topics. With this seven-topic classification we proceed to extract a statistically meaningful weight of evidence associated with environmental terms in each activity description. This weight is applied to the associated firm’s overall output value recorded on our national Business Register to arrive at a supply side estimate of the firm’s EGSS value. On this basis we find the EGSS estimate for construction in Ireland in 2013 is about EURO 229m. We contrast this estimate with estimates from other countries obtained by demand side methods and show it compares satisfactorily, thereby enhancing its credibility. Our method also has the advantage that it provides a breakdown of EGSS output by EU environmental classifications (CEPA/CReMA) as these align closely to discovered topics. We stress the success of this application of LDA relies greatly on our small vocabulary which is constructed directly from the satellite register.

journal of official statistics

A Lexical Approach to Estimating Environmental Goods and Services Output in the Construction Sector via Soft Classification of Enterprise Activity Descriptions Using Latent Dirichlet Allocation