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Contents: Preface -- 1. Defining eco-innovations: characteristics, typologies, and socio-economic approaches -- 2. The drivers of eco-innovations -- 3. Eco-innovations and finance theory -- 4. Eco-innovations and companies' financial constraints: a multilevel perspective model -- 5. Financial constraints and incremental eco-innovations: insights from manufacturing companies -- 6. Financial constraints and radical eco-innovations: the case of the anaerobic digestion and biogas niche -- Conclusions -- Index.

"The speed with which the various economies recover from the Covid-19 pandemic will significantly determine the economic pressure placed on the environment in the medium to long-term. Furthermore, the pandemic has highlighted the strong interrelations between natural and societal systems, with societal resilience depending on a resilient environmental support system. In this context, this book argues that the pandemic represents a wake-up call for financial systems to be better prepared for the climate crisis and social risk and has provided a stimulus to scale down the reliance of the global economy on fossil fuels. The first part of the book provides a deep and creative discussion between leading international researchers and experts on the policy options and financial instruments which can help to catalyze the green finance transition in the post Covid-19 era. The contributions here show that sustainable finance is emerging as a powerful tool to catalyze the transition towards a more environmentally and socially sustainable economic model. Instruments such as sovereign green bonds, green securities, and other sustainability-related securities can play a significant role in the post Covid-19 world to fund economic stimulus and to lead the way to new and more sustainable future. The second part of the book supports the debate by highlighting a number of selected case studies on financing transitions in different regional contexts including Africa, Asia, Europe , and Latin America. This book marks a significant contribution to the literature on environmental economics and finance, climate change and sustainability transitions"--

Foreign direct investment (FDI) from Multinational enterprises (MNEs) can
augment the productivity of domestic firms insofar as knowledge ?spills over?
from foreign investors to local producers. The capacity of local companies to
exploit knowledge from MNEs can be affected by the technology gap between
foreign and local enterprises at both horizontal (in the same industry) and
vertical (in different industries) level. Whereas most of the empirical
literature has focused exclusively on the analysis of horizontal and backward
spillovers (i.e. between MNEs and local suppliers), the present paper also
examines the relationship between FDI-related spillovers and technological
gap in the Italian manufacturing sector at forward level (i.e. between MNEs
and local buyers). Results suggest that at both intra-industry and forward
level, the technological gap is of considerable importance for the spillover
effect, particularly in the case of low-medium gap.

This paper investigates the impact of incoming foreign direct investment on local wages in the Italian manufacturing sector. We find that wage spillovers take place mainly when the technological gap between domestically owned firms and foreign‐owned firms is large. Specifically, a large technological distance between domestically owned and foreign‐owned firms has positive effects on wages paid by domestically owned firms in the same industry and negative on domestic wages in upstream and/or in downstream industries. Finally, inward investment may indeed improve the domestic sectors, although such linkage depends on different characteristics of domestically owned firms and sectors where firms operate.

PurposePublic debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.Design/methodology/approachUsing quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.FindingsThe results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.Originality/valueCompared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

PurposeThis article explores the migration intentions (MIs) embedded in population movements from rural to urban areas in Bangladesh. In this country, urban-centric development policies have made cities epicentres of commerce and industrialisation, offering significant employment and livelihood opportunities. This rapid transformation has generated several socio-psychological factors that are influencing the willingness of rural populations to migrate to cities for better jobs, lifestyles and services.Design/methodology/approachThe present study adopted the theory of planned behaviour (TPB) as a conceptual model to assess the behavioural and psychological factors underlying MIs.FindingsThe results of the structural equation modelling (SEM) indicate that MIs are mainly influenced by subjective norms (SN) and, to a lesser extent, attitudes towards migration (ATM) and perceived behavioural control (PBC).Originality/valueThe analysis drew on an original dataset built through interviews with migrants from rural areas employed in the ready-made garment (RMG) industry in four selective areas of the Metropolitan City of Chittagong.