Advanced economies are under growing pressure to downscale greenhouse gas (GHG) emissions without undermining growth, yet G7 (Group of Seven) nations, representing almost 10% of the world's population, still generate one quarter of global GHGs. We have investigated the G7's GHG emission problem from 2000 to 2020, by integrating macroeconomic and environmental panel data to determine how stricter environmental policies, higher green tax revenue, resource dependency, trade openness, and globalization can reduce the G7's emission problem. We applied second-generation panel estimators alongside a state-of-the-art quantile-based robust model, called the method of moment quantile regression (MMQR), and employed a two-step generalized method of moments (GMM) to address the endogeneity concern. In doing so, we found the following three findings. First, tougher regulations and higher environmental tax yields are consistently associated with reducing the GHG emissions, with the effect intensifying in all regimes. Second, resource dependence remains a stubborn emission amplifier across the entire distribution. Third, the role of trade and globalization is minimal, sometimes insignificant, referring to the fact that the policy and structural factors dominate trade and integration effects. Policy pathways for the G7 thus focus on (i) synchronizing environmental policy stringency targets to strict carbon-pricing floors, (ii) recycling environmental tax revenue and implementing green globalization with cross-border trade to accelerate clean-tech diffusion, and (iii) deploying resource diversification to neutralize resource rent-driven lock-ins. Our policy mix can help wealthy, integrated economies translate fiscal and regulatory leverage into a rapid and equitable solution to reduce GHG emissions.

Currently, the United States (U.S.) has issues with declining industrial values and supply chain digitalization, which threaten the country's industrialization and Industry 4.0 transformation. The country needs to reverse this trend through effective policy implementation. However, the literature's understanding of effective policies' long-run associations with the industrial value added (INV) of the country remains limited. Thus, this research investigates the long-term cointegrated relationships of several variables, such as artificial intelligence innovation (AIP), trade openness (TOP), supply chain digitalization (SCD), foreign direct investment (FDI), and GDP growth (GDPG), with INV based on the national level data from 1990 to 2023. The autoregressive distributed lag findings suggest that TOP, SCD, and GDPG have significant positive associations with INV in the long run, indicating that the significance of trade openness, supply chain digitalization, and economic growth is associated with the positive performance of industrial values within the country. However, both AIP and FDI exhibit significant negative associations with INV, indicating transitional and adjustment cost pressure of AI innovations and crowding out effects of domestic investments associated with FDI. That particular research suggests that all the variables would play individual effects and interact with one another as an Industry 4.0 transformation system to facilitate the improvement of industrial values in the country over long-run dynamics. Together, these findings can help federal policymakers empirically tailor long-term strategies to turn decreasing industrial value-adding performance into an upward trend and transform into Industry 4.0 by ensuring positive combined associations from technology-driven strategies.

Owing to the material-intensive industrial dependency of the United States (U.S.), reducing domestic material consumption (DMC) is vital for improving resource and material efficiency, addressing environmental challenges, achieving Sustainable Development Goals (SDGs), and advancing dematerialization. To address this important gap in the literature, this study aims to evaluate the long-term associations of green technology innovation (GTI), AI innovation (AIN), renewable energy consumption (REC), trade openness (TOP), and GDP growth (GDPG) with DMC. This study employs the ARDL time series method, which relies on U.S. aggregate national-level data from 1990 to 2023, to explore the long-term cointegrated relationships among them. On the basis of the ARDL long-run estimations, GTI has a significant negative association with DMC, indicating the significance of eco-friendly innovations in dematerialization. Although green technologies reduce material pressure, AIN’s significant positive associations reflect AI innovations’ concern with extensive resource and material consumption in data centers. REC, with its significant negative association, demonstrates the importance of the renewable energy transition for dematerialization. In addition, TOP with a significant negative association indicates the country’s control over trade integration to reduce pressure on territorial material consumption. Moreover, GDPG has a significant positive effect on DMC, indicating that economic growth is associated with scale effects within industries. All these findings remain robust in FMOLS, DOLS, and CCR. Granger causality reveals two unidirectional and two reverse Granger causes, indicating predictive patterns of these relationships. The investigation emphasized implementing action-based policies within the country to succeed with dematerialization.

Despite holding the world's second-largest portfolio of green technology patents, the U.S. is still behind the developed economies in energy efficiency outcomes, which is responsible for creating an innovation allocation dilemma in renewable electricity deployment. This study addresses the fundamental question of the optimal resource allocation among competing innovation pathways by investigating the comparative impacts of artificial intelligence (AI) innovation, green technology innovation (GTI), research and development (R&D) expenditures, and environmental policy stringency (EPS) have on the U.S. renewable electricity contribution rate (ECR) over a period of 33 years (1990-2022). Applying the autoregressive distributed lag (ARDL) model, this study highlights the fact that the interaction between R&D investment and per capita gross domestic product (GDP) significantly influences ECR with a long-term elasticity of about 91%. Second, EPS also has a highly significant and robust elasticity of about 62% for ECR gains. AI innovation, however, shows mixed effects: the initial positive short-run contributions fade away in the long run without sustained complementary investments. With respect to asymmetric effects, negative shocks convey larger benefits to renewable energy than positive ones, a finding that questions the conventional technology deployment. The findings support policymakers making R&D investments a priority over patent-based strategies, reallocating government expenditures from direct spending to market mechanisms.