Developing countries face mounting pressure to undertake environmental transformation. At the same time, technological, financial, and institutional constraints complicate policy design, leading many governments to experiment with demonstration-based policies. Much of the research on green-transition policies focuses on firms selected for demonstration programs. This study instead turns attention to non-selected firms and examines how demonstration policies affect them under an asymmetric incentive structure. Selective environmental recognition can influence designated firms and alter the investment decisions of local industry peers, yet the durability of these peer responses remains unclear. This study examines China’s national Green Factory Program using a panel of A-share manufacturing firms from 2014 to 2024. A staggered difference-in-differences design identifies how the first green factory in a city-industry group affects capitalized environmental investment among non-selected peers. The preferred estimate is 0.1596 (p = 0.0015) and remains positive after excluding designated and green-supply-chain firms, while the specification passes joint pre-trend tests. The response occurs primarily on the intensive margin, is concentrated within 30 km, and is strongest in the certification year and the following two years. Cohort and mechanism evidence reveal a temporal transition. Early certifications activated government support, capital-market feedback, supply-chain pressure, and local learning simultaneously. As the program expanded, estimates for the general government-support and average supply-chain channels became less precise, although selected-firm valuation and energy-saving investment remained responsive. Green-factory recognition therefore operates as a multi-channel catalyst at program launch but evolves toward more selective diffusion as certification becomes less scarce.
This study examines the effect of financial technology (FinTech) on environmental sustainability, emphasising the moderating role of financial inclusion in Africa. While existing studies focus on the relationship between financial inclusion and FinTech, the moderating mechanism through which financial inclusion influences the FinTech–environmental sustainability nexus remains underexplored. To address this gap, the study explores the interrelationships among FinTech, financial inclusion and environmental sustainability across 20 African countries from 2010 to 2025. Anchored in Rogers’ Diffusion of Innovation Theory, the study employs advanced panel econometric techniques, including the Hadri LM test, System Generalised Method of Moments (SGMM), the Dumitrescu–Hurlin panel causality test, the Difference-in-Hansen test, and Wald tests. Institutional quality, economic growth and foreign direct investment are included as control variables. The findings show that financial inclusion significantly moderates the impact of FinTech on environmental sustainability, providing strong empirical and theoretical support for the study’s central proposition. When considered independently, financial inclusion contributes negatively while FinTech contributes positively to environmental sustainability. However, their interaction/moderation yields a complementary effect that strengthens environmental sustainability by reducing carbon emissions. Furthermore, the study establishes a bidirectional causal relationship among FinTech, financial inclusion, and environmental sustainability. The study concludes that the African continent should systematically expand financial inclusion to amplify the environmental benefits of FinTech innovations by leveraging sustainable digital finance.
Post-socialist economies in Central and Eastern Europe, the Western Balkans, the Eastern Partnership, and Central Asia inherited energy-intensive industrial structures and now diverge widely in digital adoption and decarbonisation, which makes them a revealing setting for the twin transition. This study examines how the digital and green transitions relate to real GDP per capita growth in 24 transition economies over 1996–2023 and whether industrial competitiveness conditions these relationships. Composite indices are constructed through principal component analysis. Following first- and second-generation diagnostic tests, the analysis employs fixed effects with Driscoll–Kraay standard errors, panel-corrected standard errors, system GMM, common correlated effects, and cross-sectionally augmented ARDL estimators, together with panel Granger non-causality tests robust to cross-sectional dependence. The digital transition, the green transition, and industrial competitiveness are each positively associated with growth. The central finding is an asymmetric moderation pattern. Industrial competitiveness strengthens the growth association of digitalisation but weakens that of greening, so that the green growth association appears concentrated in less industrialised economies. Granger predictive relationships run in both directions between each transition and growth and from industrial competitiveness to the green transition. The pattern is robust across subregions, EU and non-EU groups, the 2005–2023 period, alternative index constructions, and alternative treatments of missing data, although the green coefficient and its interaction with industrial competitiveness are not statistically significant in the 1997–2004 subsample. The system GMM and CS-ARDL estimates are treated as supplementary evidence. The evidence, which consists of conditional associations rather than causal effects, suggests that twin transition support may be more effective when it takes the industrial conditions of each economy into account rather than being applied as a uniform package.
The shift toward renewable energy is a priority for many countries seeking to mitigate climate change, support sustainable economic growth, and enhance energy security. This study examines the relationships between fossil fuel energy consumption, renewable energy, CO2 emissions, CH4 emissions, and economic growth in Saudi Arabia over 1990–2022, using an Autoregressive Distributed Lag (ARDL) model and a Threshold Regression (TR) model. The bounds test indicates a long-run relationship among the variables, while the error-correction term suggests that approximately 52.7% of short-run deviations from long-run equilibrium are corrected within one year. Renewable energy is positively and significantly associated with economic growth in both the short and long run. However, the threshold analysis provides insufficient statistical evidence of a nonlinear relationship: the estimated split at 0.0425 yields a wild-bootstrap p-value of 0.943, while the levels specification yields a p-value of 0.483. Accordingly, the estimated split is not established as a statistically significant threshold. CO2 emissions are positively associated with economic growth in both renewable-energy regimes, whereas CH4 emissions show a positive and significant long-run association but negative short-run dynamics, with no statistically significant regime-specific effects. Overall, the findings support a positive association between renewable energy and economic growth in the linear model but do not establish a significant threshold effect. These results inform the discussion of energy-transition policies in Saudi Arabia.
The growth payoff of the digital economy may depend on complementary national capacities. This study quantifies the growth effect of deepening the digital economy. It tests how financial development and human-capital investment moderate it in 58 OECD, accession-candidate, key-partner, and EU candidate economies. A composite Digital Economy Index (DEI) aggregates digital-trade participation, infrastructure, and using the Z-score method. The index was computed through 2023, while the estimation covers 2014–2022 owing to the human-capital moderator’s reporting lag. The dependent variable is the natural logarithm of real GDP per capita (constant 2015 US$). Financial development is measured by domestic credit to the private sector (% of GDP) and human-capital investment by government education expenditure (% of GDP). In two-way fixed-effects models, the DEI coefficient is positive but imprecise and is significantly positive at below-average levels of credit and education spending. Within-country digital deepening is robustly positive under country fixed effects. The dividend is smaller where private credit is abundant (“too much finance”). We find no significant interaction with a tertiary-enrolment stock proxy, indicating that moderation operates at the fiscal input margin rather than through the measured skill-stock proxy. The results are robust to instrumentation, aggregation, fixed-effects structure, and sample windows. They are not robust to country-specific linear trends, because these trends absorb most within-country variation in the index during this panel period, rather than necessarily overturning the underlying finding.
