The GWO-XGBoost model achieved an R² of 0.991 in Gross Domestic Product (GDP) prediction, exceeding all other machine learning models compared in this study. Thus, GWO-XGBoost provides a transparent and reliable decision-making support system for all those who develop economic policies. Developing accurate macroeconomic predictions can assist in the advancement of sustainable development. However, the non-linear relationships among many macroeconomic variables, such as industrial production, governance, and demand, make it difficult to develop accurate and understandable predictions using conventional macroeconomic models. To address the limitations of these traditional models, we propose a hybrid explanatory model, GWO-XGBoost, that integrates the Grey Wolf Optimizer (GWO) with Extreme Gradient Boosting (XGBoost). This integrated model automatically tunes its hyperparameters. Monthly US data from 1996 to 2020 was utilized in the experiment. These data include: Government Effectiveness, Consumer Demand/Retail Sales, Industrial Production, Trade Balance, and Unemployment. The results of the SHapley Additive exPlanations (SHAP) analysis show that the two most significant factors influencing economic performance were consumer demand and government effectiveness. As a result of being both highly predictive and having clear feature attributions, the proposed hybrid explanatory model will be useful for all institutions involved in developing and implementing evidence-based planning processes that align with economic growth.

This study aims to examine the impacts of AI investment on economic growth, while controlling for labor force participation and gross fixed capital formation in Germany. The analysis is based on data collected on the state of economic development in Germany from the first quarter of 2012 to the fourth quarter of 2022. The study used a nonlinear ARDL bounds approach for these investigations. The outcomes clearly reveal that positive shocks to labor force participation and investment in AI significantly enhance economic growth (GDP) in Germany, whereas a positive shock to gross fixed capital formation (GFCF) has no considerable effect on economic growth. Likewise, negative shocks to gross fixed capital formation and AI investment increase GDP growth. Negative shock to the labor force reduces GDP growth. Recommendations are made that Germany must maintain its measured approach while offering long-term commitment. More spectacular AI expenditure increases are not required; prioritize steadiness and integration. Establish long-time-horizon AI development partnerships between government, universities, and industry with stable funding streams on long-term horizons. The fragile link between traditional capital formation and growth means that Germany needs to redefine productive investment.

Despite increasing investments in environmentally related government research and development (R&D) and renewable electricity in the United States, empirical evidence on their combined effects on environmental quality remains limited, particularly under nonlinear dynamics and structural changes. This study investigates the impacts of environmentally related government R&D expenditure and green electricity on consumption-based CO₂ emissions while controlling for financial development and economic growth using quarterly data from 1990Q1 to 2020Q4. To capture long-run relationships and asymmetric effects, the study employs the Fourier Autoregressive Distributed Lag (FADL) cointegration and Nonlinear Autoregressive Distributed Lag (NARDL) approaches. The findings reveal a long-run cointegrating relationship among the variables. Increased renewable electricity generation and environmentally related government R&D significantly improve environmental quality, whereas financial development and economic growth exacerbate environmental degradation. The study contributes to the literature by integrating these determinants within a nonlinear framework and provides evidence to support policies promoting environmental innovation, renewable electricity deployment, and sustainable environmental management in the United States.

Climate policy in Italy has come to treat energy and resource productivity as central levers, reflecting a broader recognition that how efficiently an economy uses energy and materials shapes its environmental outcomes. This study aims to investigate the dynamic nexus between energy productivity, resource productivity, and carbon dioxide (CO₂) emissions in Italy. To do this, this study uses quarterly time series data from 2000 to 2022 and employs wavelet quantile methods, specifically wavelet quantile regression and wavelet quantile correlation. The wavelet quantile correlation reveals that a euro per kilogram unit change in resource productivity negatively correlates with CO₂ emissions by approximately −0.9 metric tonnes, and a euro per kilogram of oil equivalent unit change in energy productivity negatively correlates with CO₂ emissions by approximately −0.9 metric tonnes across all periods and quantiles, particularly in the long term. Moreover, the findings of wavelet quantile regression reveal that, in the long run, a euro per kilogram unit change in resource productivity and a euro per kilogram of oil equivalent unit change in energy productivity reduce CO₂ emissions across all periods and quantiles by approximately −0.40 and −1.0 metric tonne, respectively. The study recommends that stakeholders in Italy should invest in both short- and long-term energy productivity programs, such as investing in smart appliances that adjust energy consumption and a Combined Heat and Power (CHP) System, as all will be effective. Beyond its methodological novelty, Italy’s dualistic industrial base and EU-driven decarbonisation commitments make it a substantively useful case for a distributional, multi-horizon analysis of this kind.

This research explores the effect of artificial intelligence (AI) investment on carbon dioxide (CO₂) emissions relating to worldwide industrialization and environmental concerns. CO₂ emissions have risen due to fossil fuel usage, leading to global warming, environmental degradation, and climate change. While most previous studies have concentrated on components such as economic progress, energy consumption, and technological improvement, little attention has been given to the impact of artificial intelligence on the levels of carbon dioxide emissions. Artificial intelligence technology enables systems to perform complicated duties including reasoning and decision making. This article intends to fill this research gap by examining the impact of AI on carbon emissions in the eight highest AI-investing countries (the USA, China, the United Kingdom, Israel, Germany, India, Canada, and Korea) from 2012 to 2022. OECD data on average trade, AI investment, and consumption-based CO₂ emissions were analyzed in this study. The Augmented Mean Group (AMG) approach was employed to analyze the study findings, with the results showing that GDP had a beneficial influence on CO₂ emissions in the Top AI-Investing Economies. The results also showed that AI investment and trade had adverse impacts on CO₂ emissions. The findings supported the idea that investment on AI has a positive impact on decreasing CO₂ emissions, thus leading to a more sustainable environment.

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.