Digitalization and digital technologies are buzzwords in today’s building industry. Because of their promising opportunities to improve (among others) the sustainability footprint of the built environment, they have emerged as an important topic for policymakers, managers, and researchers. Yet, the debate is dominated by references to Building Information Modelling (BIM) and to the success of digital businesses in other industries; it thereby fails to consider other promising digital building technologies and ignores that—in the building industry—many digital technologies require alignment with buildings’ physical components. For these reasons, it is unclear how the implications of digital transformation of the building industry for policy and business. In this paper, we develop a typology of digital building technologies, and categorize and assess 29 important building technologies. The substantive differences among different types of building technologies provide valuable insights into how digital building technologies affect the functioning, structure, and competition in the building industry and where digital building technologies offer opportunities to remedy the industry’s sustainability footprint. Based on our findings, we offer recommendations to policy makers, companies, and researchers interested in digital building technologies.
Reducing inequality is a major goal of the Sustainable Development Goals. Inequality is many-sided and often appears across geographic boundaries. Urban inequality refers to inequality between urban neighborhoods. Despite close distances, it reveals considerable disparities in income level, unemployment rates, and other socio-economic indicators and is highly dangerous for democratic societies. However, little is known about determinants indicating urban inequality. Here, we propose to explain urban inequality based on point-of-interest (POI) data from the online platform Open Street Maps. For this, we leverage machine learning to predict three major indicators of urban inequality, namely, unemployment rate, income level, and foreign national rate. We evaluate our machine learning approach using POI data for neighborhoods in Paris, Lyon, Marseille, Berlin, Hamburg, and Bremen. We find: (1) POIs are highly predictive of intra-city inequality explaining up to 75% of out-of-sample variance of urban inequality. (2) POIs generalize across cities and, thereby, can help to explain urban inequality in other cities, where no socio-economic data is available. (3) Important POIs for the prediction model are, e.g., banks and playgrounds. To the best of our knowledge, our work is the first to show urban inequality through POIs. As such, POIs can be used to infer granular mappings of urban inequality and thereby provide cost-effective evidence for policy-makers.
Net-zero targets have significantly increased carbon offset demand. Carbon offsets are issued based on ex-ante estimates of project emissions reductions, though systematic evidence on ex-post evaluations of achieved emissions reductions is missing. We synthesized existing rigorous empirical studies evaluating more than 2,000 offset projects across all major offset sectors. Our analysis shows that offset projects achieved considerably lower emissions reductions than officially claimed. We estimate that only 12% of the total volume of existing credits constitute real emissions reductions, with 0% for renewable energy, 0.4% for cookstoves, 25.0% for forestry and 27.5% for chemical processes. Our results thus indicate that 88% of the total credit volume across these four sectors in the voluntary carbon market does not constitute real emissions reductions. This offset achievement gap corresponds to almost twice the annual German CO2 emissions. We complement evidence from offset projects with 51 additional studies conducting ex-post evaluations of field interventions with settings comparable to offset projects. For cookstoves and forestry projects, these field interventions were more effective at reducing emissions than the voluntary offset projects, likely due to more careful intervention targeting, stricter monitoring and enforcement of intervention protocols.
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