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Summary: The EU countries are facing a burning debate with regard to the nature and amount of decarbonized energy used within the supply mix. This study highlights the role of nuclear power and renewable energy use in achieving economic growth in Europe. Capital, labour, exports, and trade openness series are included within a multivariate framework spanning the 1986 to 2019 period and a two‐step methodology is implemented. First, a panel causality analysis over the top 10 nuclear energy‐consuming EU countries is conducted using the panel non‐causality test from Juodis, Karavias and Sarafidis (2021), robust under large cross‐sectional and time‐series dimensions, as well as heterogeneous coefficients. Second, a sub‐set of three states (Germany, Spain, and France) that are different in terms of long‐run nuclear energy policies is selected and a single‐country time‐series assessment is performed using the vector error correction model, causality tests, as well as impulse response functions. Panel EU results support the "conservation hypothesis" for both nuclear and renewable resources, without feedback effect. The country‐level estimates fail to provide homogeneous results since no causal link is found between economic growth and renewable energy use for Spain and France (confirmation of "neutral hypothesis"). A weak causality from renewable energy use to output growth is observed for Germany, while economic growth enhances the nuclear energy consumption in Spain. [ABSTRACT FROM AUTHOR]

With the EU Green Deal initiatives, European members seek to launch the first climate neutral continent by 2050. This paper assesses the stochastic convergence of per capita energy consumption series for an illustrative sample of 15 EU countries with memberships prior to the 2004 enlargement, using data spanning the 1970–2018 period. Results from the confidence interval subsampling (asymmetric and equal-tailed) highlight that 11 out of the 15 EU series exhibit a long-run memory behaviour, while a diverging pattern was observed for the UK, Germany, Portugal, and Italy. Finally, per capita energy use series persist but fail to reveal one of the above dynamics for Ireland and Spain. Also, findings from the non-parametric Bayesian application (ANOVA/linear Dependent Dirichlet Process (DDP) mixture model) show how economic growth operates as a converging energy consumption-enabler over the long-run, from which the EU membership cannot be excluded. In particular, we highlight how the nature of energy-GDP hypotheses vary with the stochastic properties of energy use (converging behaviour with temporary shocks, converging pattern with permanent shocks, and diverging dynamic). Finally, our conclusions overcome the well-established development stage argument as we claim that countries with similar energy convergence patterns may need to adopt similar energy policies. [ABSTRACT FROM AUTHOR]

• Oil product consumption and CO 2 emissions sharply influence the transport logistics indexes. • A feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. • We found empirical sustain for the "feedback hypothesis" for Germany and Japan. This paper critically assesses the effect of fossil fuel dependence and polluting emissions from the transport sector on the performance of logistics operations in the context of Green Supply Chain Management (GSCM). We collected macro-level time-series data for a sample of 27 European Union (EU) countries over the period 2007–2018. A new Artificial Neural Networks (ANNs) algorithm is adopted in a multivariate framework to investigate the dynamic interactions among a range of Logistics Performance Indexes (LPI), the demand for oil products, and carbon dioxide (CO 2) emissions from fuel combustion in the transport sector. Empirical findings show that oil product consumption and CO 2 emissions sharply influence the transport logistics indexes. However, a feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. Based on our empirical insights, adequate policy recommendations are provided to help turning the logistics sector towards a more sustainable path in the European area. [ABSTRACT FROM AUTHOR]

• The Belgium nuclear phase-out plan is analyzed. • A multivariate model through time-series analyses is implemented over the 1974–2019 period. • We develop and apply an innovative partial differential equations (PDE) algorithm. • Time-series and machine learning findings demonstrate suggests that the nuclear phase-out might neither adversely impact the economy's growth nor threaten the emission reduction target in the long-run. By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO 2 emissions from the power and heating sector is combined with real time-series data spanning the 1974–2019 period. The analysis consists in sequentially assessing two distinct nexuses (energy-economy and energy-economy-environment) over reduced- and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal links among export diversification, per capita income, and energy demand for 20 Asia-Pacific Economic Cooperation (APEC) countries. Human capital, industry share, and Foreign Direct Investments (FDI) are included within a multivariate framework. Using data covering the 1995–2018 period, an innovative sequential Artificial Neural Networks experiment is developed. Empirical results highlight that the export diversification index negatively affects the neural target corresponding to energy demand. Conversely, per capita GDP displays a significant influence on the acceleration of the same energy target. Hence, findings suggest that export diversification plays a mitigating role in the demand for energy, which is congruent with the literature. Moreover, economic growth is found to be substantially driven by export diversification, while no evidence of a causal effect is found among export diversification, human capital, and FDI. Such inferences are finally corroborated by a Decision Tree model, conducted as a final robustness check. In line with the Sustainable Development Goals (SDGs), adequate measures are suggested to improve energy savings along the supply chain, without hindering the economic performance of the Asian region. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO 2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments – through a software capable of emulating a quantum system – to data over the period of 1990–2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany. • An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. • The empirical findings support the powerful role of biomass energy in reducing CO 2 emissions. • GDP represents an important determinant of environmental pollution in Germany. [ABSTRACT FROM AUTHOR]