Qiang Wang

Advancing sustainable development requires reconciling economic growth with carbon reduction. This study investigates carbon emission efficiency (CEE), growth–emission decoupling, and digital–structural transformation in 35 early OECD member countries from 1995 to 2023. It combines the IGT decoupling model, a three‐stage DEA framework using the Super‐EBM model, the GML index, and truncated regression to evaluate decoupling patterns, static and dynamic CEE, and the drivers of low‐carbon transition. The results show that OECD countries have generally shifted from relative toward absolute decoupling, indicating progress in separating economic expansion from carbon emissions. After controlling for external environmental factors and random noise, third‐stage CEE values are consistently higher than first‐stage estimates. Income‐based heterogeneity is evident: very high‐income and high‐income OECD countries generally perform better, while several lower‐income members also show strong managerial efficiency after adjustment. Dynamic GML results confirm that most OECD countries have improved their low‐carbon transition performance over time. The truncated analysis indicates that demographic scale negatively affects CEE, whereas artificial intelligence and service‐sector expansion significantly enhance efficiency. These findings highlight the importance of AI‐enabled innovation and service‐oriented industrial upgrading for sustainable low‐carbon development in OECD countries. The study offers evidence‐based guidance for economies seeking to align digital transformation, structural change, and climate objectives effectively.

Artificial intelligence (AI) is increasingly linked to sustainable energy transitions, but its role can be highly contextual on country‐specific technological pathways and macro‐level stability. Using balanced panel data for 60 countries from 2010 to 2022, this study examines the relationship between AI indices and renewable energy consumption outcomes through fixed‐effects models, dynamic panel threshold models, and scenario‐based regressions. The results support the positive association between AI development and renewable energy consumption share, with stronger effects in high‐income countries than in middle‐income countries. The technology channel yields a stronger impact than the application channel, while AI application is insignificant in middle‐income countries, suggesting constraints from weak digital infrastructure and absorptive capacity. Threshold results indicate that political and financial stability strengthen the AI–energy transition relationship, whereas economic stability shows a diminishing marginal pattern. Scenario analysis further suggests that AI is most effective under jointly low political, financial, and economic risks. These findings highlight that intelligence‐enabled energy transition requires not only AI development, but also stable governance, resilient finance, and adaptive macroeconomic policies.

This study examines the associations between artificial intelligence (AI) technology innovation and the three dimensions of the global energy trilemma of security, equity, and environmental sustainability. Drawing on a balanced panel of 61 economies from 2000 to 2023, we utilize the stock of AI patent applications to proxy for R&D intensity and employ two‐way fixed effects models to estimate conditional correlations. The results reveal notable asymmetries. AI innovation is positively associated with energy security and environmental sustainability but negatively correlates with energy equity, underscoring a tension between innovation‐driven efficiency and distributional justice. Mechanism analyses suggest that renewable capacity expansion and industrial structure upgrading serve as primary pathways supporting energy security and sustainability, whereas green technology innovation plays a key role in improving energy equity. Furthermore, climate risk acts as a pivotal boundary condition, dampening the innovation dividends for security and sustainability while partially offsetting equity losses. While AI expands the aggregate performance of energy systems, integrated index analyses show it fails to significantly improve the internal coordination among the trilemma's dimensions. The findings underscore the need to integrate innovation‐driven efficiency gains with affordability, inclusiveness, and climate‐resilient infrastructure to ensure that AI supports a balanced and equitable global energy transition.

Enhancing energy resilience contributes to ensuring energy security and stable operation, and artificial intelligence (AI) offers a new technological path. This study explores the relationship between AI and energy resilience using data for 42 countries from 2010 to 2022. First, based on five dimensions—total energy consumption, energy access, energy efficiency, renewable energy, and energy security—this study constructs a comprehensive evaluation system for energy resilience. The projection pursuit model based on a genetic algorithm is applied to measure resilience levels, revealing the nonlinear relationship between AI and energy resilience. A quadratic moderating effect model and curve simulation techniques are used to analyze the mechanisms of external risks and clean energy impact. The study shows that AI initially enhances energy resilience through prediction, fault diagnosis, and intelligent decision‐making. However, technological risks such as data breaches, communication disruptions, cyber threats and attacks weaken the resilience‐enhancing effect. Compared with developing countries, developed countries exhibit a more significant effect of AI technology on improving energy resilience. Furthermore, rising geopolitical risks will reshape the effects of AI technology, and the synergistic effect of AI and clean energy can further strengthen its positive effect on energy resilience. This study provides new insights into optimizing the application of AI, contributing to the construction of a safer and more stable energy system.

The rise of large language models (LLMs) is transforming the trajectory of traditional rule‐based automation, while their global impact on the labor market remains largely unexplored. To assess this transition, we develop a novel bottom‐up framework linking detailed task data to occupational structures across a broad spectrum of economies. Our findings reveal that LLM‐driven automation disproportionately impacts roles centered on information processing, administration, and managerial coordination compared to those in physical or manual domains. At the sectoral level, this impact translates into higher exposure for knowledge‐intensive sectors like finance, education, and professional services, while sectors like agriculture and manufacturing remain more insulated. Because the industrial structure strongly determines a nation's vulnerability, economies reliant on administrative and clerical activities are facing greater exposure. Critically, potential productivity gains from this exposure are concentrated in areas contributing significantly to economic value rather than employment, highlighting a tension between efficiency and equitable labor outcomes. We argue that proactive policies, focusing on fair transitions, skill adaptation, and strategic industrial development, are crucial to avoid this technological shift undermining the sustainable development goals.

Achieving the Sustainable Development Goals ultimately depends on how effectively states are governed. Using panel data for 41 countries from 2010 to 2023, this study examines how government efficiency, corruption control, and political risk influence sustainable development, and whether governance digitalization moderates these relationships. Three main conclusions were drawn from the use of double machine learning algorithms. First, government efficiency consistently enhances sustainability performance. Second, effective corruption control promotes sustainable development. Third, political risk has the most significant negative impact on sustainable development; compared to developed countries, developing countries are more vulnerable to the shocks of political risk. Furthermore, artificial intelligence technology has a dual moderating effect: it enhances the positive impacts of government efficiency and corruption control while mitigating the adverse effects of political risk. The digital transformation of governance thus amplifies the benefits of efficiency and cushions economies against external risks, underscoring its complementary role in sustainable development. Overall, this study provides global empirical evidence on how artificial intelligence technology is reshaping the institutional foundations of sustainability.

Carbon-negative technologies (CNTs) are vital to achieving net-zero climate goals. However, regional disparities in CNT innovation capacity remain insufficiently understood. This study maps the innovation landscape of CNTs across 18 European countries from 2013 to 2023 using a novel multidimensional, data-driven approach. A real-coded accelerated genetic algorithm is integrated with projection pursuit to construct a high-dimensional evaluation model capable of handling nonlinear, nonnormal data distributions. Thirteen indicators across innovation input, output, and environment are selected to quantify national innovation capacities. Results show that Germany, France, and Italy consistently outperform others in CNT innovation, driven by high R&D investment, patent activity, and strong economic foundations. In contrast, countries like Portugal, Slovenia, and Luxembourg exhibit lower innovation capacities. Cluster analysis reveals a clear stratification into high-, medium-, and low-performing countries, highlighting structural differences in policy support and industrial development. It reveals the “club convergence” characteristic of negative carbon technology innovation capabilities across 18 countries, meaning these nations can be clearly divided into three hierarchical innovation echelons (high, medium, and low) rather than exhibiting continuous geographical agglomeration. Further mechanism analysis indicates significant differences in the innovation-driven factors among countries of different echelons: leading nations are driven by their macroeconomic foundation, while catching-up nations rely more heavily on government R&D investment and human resources. Temporal trends indicate overall growth in innovation capacity, with temporary declines linked to Brexit and the COVID-19 pandemic. This research contributes a robust methodological framework and a comparative evaluation of CNT innovation in Europe, offering critical insights for policymakers and stakeholders aiming to strengthen cross-border collaboration and accelerate the deployment of CNTs.