Sustainable fisheries are fundamental to achieving the United Nations Sustainable Development Goal 14 (SDG 14), yet the alignment between scientific research and fisheries governance remains fragmented. To elucidate the academic evolution and policy orientation within sustainable fisheries, this study follows the PRISMA guidelines to analyze 1700 publications retrieved from the Web of Science Core Collection published between 2001 and 2025. By integrating quantitative bibliometrics with qualitative text mining, we systematically review the field's research trajectories and intellectual hotspots. The findings reveal a steady but uneven expansion of the research field, with scientific outputs dominated by the natural sciences, such as marine biology, ecology, and environmental science, while social science perspectives on policy design, economic mechanisms, and institutional governance remain underrepresented. The United States, China, and Canada lead in publications, but international collaboration networks are extensive yet characterized by weak ties. Keywords analyses identify six major thematic clusters: fisheries management and governance, small-scale fisheries, climate adaptation, marine ecological management, species-level studies, and the use of emerging technologies such as artificial intelligence. Despite progress toward sustainability-oriented research, empirical and interdisciplinary frameworks connecting science with policy application remain limited. This study calls for more targeted financial support, stronger cross-disciplinary collaboration, more open data sharing, greater engagement from developing economies, and stronger link between scientific research and policy in the sustainable fisheries. By elucidating the global knowledge structure and governance gaps, this work provides an evidence-based foundation for integrating scientific insights into effective and equitable fisheries management.

Energy resilience has become a central requirement for sustainable energy transitions at high latitudes, where climate hazards and geopolitical shocks intersect. This study develops an integrated measurement-evolution-mechanism framework to assess energy resilience in the eight Arctic states from 2014 to 2023, combining a three-dimension indicator system (resistance, adaptability, recovery), projection-pursuit-based composite scoring, distributional dynamics, and data-driven driver identification. Using official statistics, we estimate annual national resilience scores, track spatiotemporal patterns, and quantify the relative importance of structural, economic, and governance factors. Three main results emerge. First, the region exhibits a steady rise in resilience, but persistent stratification: the United States and Russia remain consistently highest; Canada and Denmark are stable at mid-high levels; Finland trends downward; Sweden, Iceland, and Norway remain lower, with renewed divergence after 2021. Second, three archetypes are identified: a recovery-resistance-dominated group with weak adaptability (Iceland, Sweden, Finland); an adaptability-advantaged group (Canada, United States, Denmark, Russia); and a low comprehensive-resilience case (Norway). Third, energy import dependence is the dominant determinant, followed by green policy intensity, government effectiveness, and human capital investment; the random forest model explains approximately 88% of cross-country variation, underscoring robust, nonlinear driver effects. Policy priorities include reducing import exposure and single-fuel risks, accelerating renewable and flexibility investments, diversifying industrial structures, and strengthening fiscal-institutional capacity and education to support adaptive and rapid recovery. The framework is transferable beyond the Arctic, linking quantitative diagnostics to actionable pathways for renewable-led, climate-robust energy systems. (c) 2026 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

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.

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.

The Arctic’s vast oil and gas reserves position it as a strategic energy frontier, yet its development raises sustainability concerns amid climate change and fragile ecosystems. Despite growing attention to SDG-oriented governance, how Arctic hydrocarbon research aligns with the UN Sustainable Development Goals (SDGs) remains unclear. Drawing on 276 peer-reviewed papers (2000-2025) from the Web of Science, this study conducts a semantic analysis to explore research evolution and SDG relevance. Breakpoint detection identifies four stages of Arctic oil and gas research. Natural language processing techniques including vector embedding, stop-word removal, using cTF-IDF for word frequency counting, and “Subject-Action-Object” (SAO) extraction are applied to abstracts. A BERT-based model combined with K-means clustering reveals major research foci and temporal trends. Thematic mapping shows five dominant clusters: (1) geological and geophysical assessment, (2) environmental monitoring and ecological impacts, (3) risk evaluation and emergency response, (4) technological innovation and modeling, and (5) Arctic governance, socioeconomics, and resource management, reflecting the core challenges of Arctic oil and gas studies. Through semantic similarity analysis, the research corpus is matched with the 17 UN SDGs, indicating strongest relevance to SDG 14 (life below water), SDG 13 (climate action), SDG 15 (life on land), and SDG 7 (affordable and clean energy). Although overall alignment remains limited, the findings suggest increasing consideration of sustainability frameworks in Arctic exploration. Yet significant gaps persist in addressing climate justice, indigenous participation, and long-term ecological resilience. This study thus provides both methodological innovation and policy insight to foster more integrative, sustainable Arctic energy governance.

Technological innovation capability (TIC) is critical for decarbonising transport systems and advancing regional sustainability in China. However, existing research primarily focuses on the isolated direct impacts of TIC on transport carbon emissions (TCE), neglecting complex spatial interdependencies and typically decoupling the production and consumption sides. To bridge this research gap, this study develops a novel integrated production-consumption analytical framework to elucidate how TIC generates synergistic emission reduction benefits through spatial interactions. Using panel data from 30 provincial-level administrative regions in China, this research introduces methodological innovations by combining social network analysis to map inter-provincial emission topologies with a Spatial Durbin Model (SDM) evaluated across multiple weight matrices. This approach captures the direct effects, spatial spillovers, and interaction effects of TIC alongside key production-side (transport structure, energy efficiency) and consumption-side (industrial structure, household consumption) factors. The results indicate that China’s provincial TCE display significant strengthening of spatial agglomeration based on the adjacency weight matrix. While TIC increases local TCE, it notably generates negative spatial spillovers. Crucially, production-side interactions extend carbon reduction benefits to surrounding provinces via a "hard technology" diffusion mechanism. By contrast, consumption-side interactions, constrained by rebound effects and high-frequency logistics demand, tend to offset TIC’s mitigation potential and may even induce positive emission spillovers. These findings conclude that achieving carbon-neutral transport requires transitioning from isolated provincial governance to cross-regional collaborative strategies. Differentiated, regionally integrated policies that align "hard" production-side innovation incentives with "soft" consumption-side behavioral interventions are essential to unlock the spatial spillover potential of TIC.

The rapid advancement of artificial intelligence (AI) is reshaping technological pathways in the global energy transition. While AI holds great potential to enhance the efficiency, resilience, and integration of renewable energy systems, its effective deployment depends heavily on supportive financial structures. Moreover, empirical assessments of AI's macroeconomic impact are limited by the absence of standardized, internationally comparable measures of AI development. To address this problem, we construct a multidimensional AI composite index using the projection pursuit method, integrating eleven indicators across scientific innovation, infrastructure support, and global competitiveness. The index spans 119 countries from 2010 to 2022, offering improved spatial-temporal coverage for global analysis. Using this dataset, we empirically investigate the effect of AI on renewable energy transition and explore the mediating and threshold roles of financial development. We apply a suite of econometric models, including fixed-effect regression, mediation effect regression, dynamic threshold effect regression, and robustness check using industrial robot proxies and GMM. Results show that AI development significantly promotes the energy transition toward renewables. This finding holds true in both low-income and high-income countries. However, the role of financial development is twofold: it mediates AI's impact, but in its current fossil fuel-biased form, may also exert a suppressive effect on green transformation. Notably, once financial development surpasses a critical threshold, the influence of AI on energy transition is amplified. Our findings offer new insights into the interaction between digital innovation and sustainable finance, and provide timely evidence for designing financial and technological policies that support AI-driven renewable energy transition.

Achieving a sustainable and inclusive energy transition remains a pressing global challenge, particularly for resource-dependent economies where energy structures and income distribution are closely intertwined. While prior studies have typically examined either the link between natural resource rents and energy transition or that between inequality and energy transition, few have analyzed the joint relationship among these three factors or explored their nonlinear interactions. Addressing this gap, this study investigates how natural resource rents and income inequality jointly influence renewable energy consumption, thereby contributing to the realization of SDG 1, SDG 7, and SDG 10. Using a balanced panel of 93 countries from 2012 to 2021, this study employs a comprehensive econometric framework that tests for serial correlation, cross-sectional dependence, and stationarity, and further applies panel threshold models to capture potential nonlinearities. The results show that natural resource rents exert a significant negative effect on renewable energy consumption, confirming the "resource curse" hypothesis. Importantly, income inequality intensifies this negative relationship, suggesting that unequal income distribution magnifies the obstacles to energy transition. Furthermore, results stratified by income level and resource dependency indicate that the adverse impact is most severe in high-income, highly resource-dependent countries under high inequality, while middle-income and low-dependency economies display more nuanced patterns. Overall, the study underscores the urgency of tailored, inequality-sensitive policies to promote sustainable development.

Artificial intelligence (AI) is reshaping labor markets, with particularly disruptive effects in capital-intensive sectors like energy. This study investigates the impact of AI on employment scale and skill composition in China's energy industry, using panel data from 112 listed firms (2011-2023). We construct a novel "industry-region coordinated exposure index" that combines regional AI development with industry-level task automatability, and embed this within a comparative advantage-based double machine learning (DML) framework to identify causal effects. The research results indicate that AI has a significant overall impact on the workforce, leading to a significant substitution effect among college graduate workers; while the labor market for basic education and postgraduate students shows an upward trend, indicating a clear polarization of skills. Significant heterogeneity exists across industries and regions: fossil fuel companies experienced more drastic workforce reductions, while renewable energy companies demonstrated a complementarity between AI and human capital. Regionally, AI led to workforce shrinkage in eastern provinces, promoted moderate skills upgrading in the central region, and had a limited impact in the west. These findings provide valuable evidence for policy considerations regarding the management of AI-driven workforce transformation. Emerging economies should achieve a balance between technological progress and employment structure driven by AI by strengthening workforce retraining, improving vocational education systems, and promoting coordinated regional development.

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.

Desertification remains a critical barrier to achieving the SDGs, particularly in arid and semi-arid regions of Africa, where its impacts undermine efforts toward poverty reduction, environmental restoration, and climate resilience. This study evaluates desertification control efforts in 11 countries participating in the Pan-African Great Green Wall initiative using a novel Real-Coded Accelerated Genetic Algorithm combined with the Projection Pursuit model. The model effectively addresses non-normal, nonlinear data, and high-dimensional, enabling comprehensive assessment across economic, social, and environmental dimensions. The results indicate that countries such as Senegal, Nigeria, Burkina Faso, and Ethiopia exhibit significant progress in desertification control, attributed to robust forest management, policy support, and investment in sustainable land use practices. In contrast, Sudan, Eritrea, and Mauritania face ongoing challenges due to limited resources and institutional capacities. Temporal analysis from 2014 to 2021 highlights the stability of desertification control trends, supported by global cooperation, innovative technologies, and the alignment of national strategies with SDG Target 15.3: attaining land degradation neutrality by 2030. This research provides actionable insights for policymakers, advancing sustainable strategies to combat desertification, enhance ecosystem restoration, and promote resilient livelihoods in Africa.

Artificial intelligence (AI) is reshaping the landscape of sustainable development, offering unprecedented opportunities while introducing systemic risks. This study examines the nonlinear and heterogeneous impacts of AI on sustainable development. Using a projection pursuit model based on genetic algorithms, we quantify national AI development levels and integrate quadratic moderation models and curve simulation to trace the dual trajectories of AI impacts on sustainable development. Our findings uncover distinct non-linear patterns: AI exerts an inverted U-shaped effect on HDI, with structural unemployment, algorithmic bias, and privacy erosion emerging in later stages. Conversely, environmental sustainability follows a U-shaped path: foundational AI technologies, over time, significantly reduce carbon intensity, while applied AI may initially increase emissions due to energy-intensive deployment. Crucially, AI can optimize renewable energy efficiency, while labor market imbalances may undermine AI's positive impact on carbon reduction and social welfare. Income-level heterogeneity further reveals that high-income countries are more capable of translating AI into sustainability dividends, while lower-income economies remain constrained by technological bottlenecks and structural mismatches. This study advances "green AI" by uncovering how AI affects sustainable development, emphasizing the need for joint investments in renewables, skilled labor, and governance to maximize benefits and limit risks.

As global efforts intensify to address climate change, enhancing the carbon emission efficiency of the transportation industry (TCEE) has become increasingly urgent—particularly in rapidly urbanizing economies like China. This study evaluates TCEE across 30 Chinese provinces and 290 cities from 2005 to 2022, employing an integrated methodology that combines the Tapio decoupling model, a three-stage Data Envelopment Analysis (DEA), and Tobit regression analysis. The Tapio model is first applied to examine the relationship between economic growth and carbon emissions. Results show significant regional variation, with most provinces still in a phase of expansive negative decoupling. The three-stage DEA model—incorporating Super-EBM and stochastic frontier analysis (SFA)—accounts for external environmental and statistical noise, yielding adjusted efficiency scores that more accurately reflect true performance. Findings reveal that the eastern, northern, and central regions generally exhibit higher TCEE, while the western and northeastern provinces lag. Tobit regression results highlight several key drivers of efficiency. An increase in the added value of the transportation industry and a higher proportion of railway freight transportation will significantly enhance TCEE. By dividing the country into four macro-regions and 290 cities, the study identifies distinctive efficiency patterns and policy needs. This research offers empirical insights and methodological tools to support region-specific low-carbon transportation strategies in China.

Geopolitical conflicts and other risk events are subtly reshaping the global political and economic landscape, gradually disrupting the balance between economic development and ecological sustainability. Understanding the pathways through which geopolitical risks affect the ecological footprint is crucial for achieving ecological sustainability goals. This study employed dual machine learning models for high-precision analysis to deeply explore the intrinsic patterns of how geopolitical risks impact the ecological footprint. Income heterogeneity was also considered. On the one hand, this research constructed a multi-window kernel density estimation (KDE) model to analyze the spatial and temporal characteristics of the ecological footprint. By incorporating dual machine learning models (DML), it innovatively discovered a positive effect of geopolitical risks on the ecological footprint. On the other hand, this study investigated the moderating mechanisms of energy transition through two paths, finding that under the dual-pathway regulation of energy transition, the positive effect of geopolitical risks on the ecological footprint gradually weakens and may even turn negative. Furthermore, compared to high-income countries, geopolitical risks have a stronger impact on the ecological footprint in lower-income countries. The policy recommendations proposed in this study offer a new perspective for decision-makers and are of significant importance for ecological sustainability.

This study investigates the nonlinear effects of economic efficiency on CO2 reduction and energy transition, with a particular focus on advancing SDGs 7, 9, and 13. Using panel data from 61 countries, the analysis applies a slacks-based measure of directional distance functions (SBM-DDF) and threshold regression models to explore the complex relationship between economic efficiency, CO2 emissions, and energy transition. Key findings reveal that while economic efficiency has generally improved over time, significant regional disparities remain. Improvements in economic efficiency lead to substantial reductions in CO2 emissions and promote energy transition, but the relationship is nonlinear, influenced by factors such as climate vulnerability, global value chain (GVC) integration, digitalization, and energy intensity. Heightened climate vulnerability and deeper integration into GVC amplify the emission-reducing effects of economic efficiency, whereas increased digitalization initially attenuates its decarbonization impact before shifting toward a suppressive effect, while concurrently reversing its influence on energy transition from inhibitive to facilitative. Furthermore, under higher energy intensity, economic efficiency exerts a more pronounced influence on both carbon abatement and the acceleration of energy transition. The study also highlights heterogeneity across income levels, with upper-middle-income countries experiencing stronger positive effects. These findings emphasize the need for tailored, context-sensitive policies that account for regional and sectoral dynamics to optimize economic efficiency for sustainable development.

The accelerating global push for sustainable development has intensified debates over the compatibility between human development and environmental sustainability. While the Human Development Index (HDI) captures improvements in life expectancy, education, and income, it often coincides with heightened ecological pressures, as measured by ecological footprint (EF). This paper revisits the linkage between human development and ecological sustainability by introducing the moderating role of globalization-specifically, under contemporary trends of deglobalization. Based on a balanced panel dataset covering 140 countries from 1990 to 2022, the study first constructs linear models and then introduces the globalization index to develop a panel threshold model. The threshold value is used to quantitatively identify deglobalization, with lower index values indicating a deglobalization phase, in order to examine how the intensity of globalization alters the marginal impact of HDI on the ecological footprint, particularly across countries at different income levels. Across every income level, the analysis shows a strong and statistically significant relationship between human development and ecological pressure, but with heterogeneous magnitudes. More importantly, we identify significant nonlinear threshold effects, where lower levels of globalization-indicative of deglobalization pressures-exacerbate the environmental burden of human development. In contrast, deeper integration into global systems appears to attenuate this burden, particularly in lower- and middle-income countries, through improved availability of environmentally friendly technologies and global best practices. The findings highlight that sustainability trade-offs are highly context-dependent, requiring nuanced policies amid rising protectionism.