Innovation Allocation Dilemma: AI, R&D, and Policy Effects on U.S. Renewable Electricity
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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.
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