The article empirically explores the role of e-business digital technologies in explaining the export propensity of Italian SMEs and focuses on the adoption and implementation of e-business technologies that enable firms to share information along the supply chain in order to take into account potential heterogeneities in paths of digitalisation. Our results, besides confirming the positive role of internet-based technologies already investigated in the current literature, show that e-business technologies have a positive impact on internationalisation only when they are embedded within process and organisational innovations and investments in digital skills have been made. This indirectly supports the argument that as the array of technological choices increases, the need for concurrent internal changes, as well as a detailed assessment of the connectedness between technological alternatives and specific business types, industries, or trading environment become increasingly important.

This paper analyses the relationships between eco-innovation and business performance by exploring the role of external collaborations as a potential factor explaining the persisting heterogeneity of economic effects of eco-innovation. Basing on a large survey of 3,000 Italian manufacturing firms, the study investigates to what extent collaborations with supply chain and quadruple helix actors, including public institutions, universities and civil society, contribute in sustaining firm’s growth by improving the effectiveness of different types of eco-innovations. We find that the relationship between eco-innovation and business performance is moderated by both supply-chain and quadruple helix collaborations. However, external collaborations with actors of the quadruple helix show a pronounced heterogeneity. While process eco-innovation may in particular benefit from establishing collaborations with public institutions, product eco-innovation relies more on interactions with civil society suggesting that market-driven factors still exert a main role in influencing the market success of this type of eco-innovation.

Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context.

Using a multiple indicators and multiple causes (MIMIC) model, this paper estimates the extent of illegal gambling in Italian regions over the period 2013–18. By treating illegal gambling as an unobserved latent variable directly related to its causes and effects, this model gives information about the relationship between cause and indicator variables and the latent variable from covariance structures. From the analysis, it emerges that the share of illegal gambling increases with the value of the winnings paid; it decreases when the number of authorized machines increases. We also find that individuals with low levels of education and low income show a greater propensity to gamble illegally.