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.

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.

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.

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.

In order to better understand the impact of different geopolitical factors on energy transition, the impact of geopolitical threats (war threats, peace threats, military buildups, nuclear threats and terror threats), geopolitical acts (beginning of war, escalation of war and terror acts), and geopolitical risks on energy transition were systematically investigated. Green technologies, natural resource rents and trade openness were incorporated into the analytical framework, and a dynamic panel threshold model was utilized to explore the impact of geopolitical risks on energy transition across different income levels. To this end, data on geopolitical threats, geopolitical acts, geopolitical risks, energy transitions and other key social economic factors for 38 countries from 2000 to 2022 were collected. The heterogeneity simulation results show that there is a negative correlation between geopolitical threats, geopolitical acts, geopolitical risks and energy transition. Moreover, geopolitical threats have more significant hindrance to the energy transition than geopolitical acts. The results of the nonlinear panel simulation show that there is a double threshold effect of geopolitical risks on energy transition. When geopolitical risk crosses the threshold (0.5197), the coefficient decreases to -0.29, which means that the rising geopolitical risk increases the inhibition on energy transition, and the inhibitory effect is slightly weakened after a certain level. Finally, policy implications are offered.

Purpose This study is aimed to measure the intertemporal financial efficiency of 16 emerging economy countries (BRICS and N -11) and further to investigate the mechanisms of financial development on energy efficiency covering the period 2008–2020. Design/methodology/approach The dynamic data envelopment analysis model is used to measure financial efficiency dynamically. The generalized method of moments is used to investigate the effects of financial efficiency on energy efficiency. In the proposed approach, energy efficiency is the dependent variable, whereas financial efficiency, GDP per capita, industrial structure upgrade index, urbanization level and export trade structure are the regressors. Generalized moment estimation is performed. Findings There is heterogeneity in the level of financial development at different stages of economic development. The impact of financial efficiency on energy efficiency is related to the type of industries to which financial institutions are allocated. With the financial development of emerging economies, enterprises in technology-intensive industries are becoming the main contributors to higher profits for financial institutions, the products and results of these enterprises reduce energy consumption and increase energy efficiency. In addition, residents with rising levels of wealth holdings prefer low-carbon and environmentally friendly products, which indirectly improves energy efficiency. Per capita GDP and urbanization have no significant impact on the energy efficiency of emerging economies. The optimization and upgrading of the industrial structure of emerging economies has played a role in promoting energy efficiency. The export trade structure has a restraining effect on energy efficiency. Originality/value The findings contribute value by supporting a positive link between Financial Development and Energy Efficiency in the emerging economies. Enterprises in technology-intensive industries have gradually become the main force that brings higher profits to financial institutions. The products and achievements of these enterprises will reduce energy consumption and improve energy efficiency. The findings of this study provide emerging economies with an objective view of their financial development and energy efficiency, while also providing governments and policymakers with ways to improve energy efficiency and achieve sustainable development.

The COVID-19 pandemic has also caused an environmental challenge, especially plastic pollution. This study is aimed to provide a systematic review of the current status and outlook of research on plastic pollution caused by the COVID-19 pandemic using a bibliometrics approach. The results indicate developed countries were the first to pay attention to the impact of plastics on the ocean and ecological environment during COVID-19 and conducted related research, and then developing countries followed up and started research. Research in developed countries is absolutely dominant in plastic pollution induced by the COVID-19, although the plastic pollution faced by developing countries is also very serious. The author’s co-occurrence analysis shows the Matthew effect. Keyword clustering shows that plastics have a harsh chain-like impact on the ecological environment from land to ocean to atmosphere. The non-degradable components of plastic bring a serious impact the ocean ecosystems, and then pose a serious threat to the entire ecosystem environment.

Increased geopolitical risks are impacting the sustainable development of the ecological environment. To better understand the impact of geopolitical risk on ecological sustainability, this study develops a research framework for the impact of geopolitical risk on ecological efficiency. (i) Measuring ecological efficiency by data envelopment analysis. (ii) Examining the relationship between geopolitical risks and ecological efficiency using the extended STIRPAT. (iii) Heterogeneity analysis and mediation test were used to further explore the impact mechanism of geopolitical risks. The research results show that: (i) There are obvious differences in the ecological efficiency of countries with different income levels. The ecological efficiency of countries with higher income levels is generally higher, while the ecological efficiency of countries with lower income levels is lower. (ii) Geopolitical risks reduce ecological efficiency, which is bad for ecosystem sustainability. (iii) The magnitude of the adverse impact of geopolitical risks on ecological efficiency is different among different income groups. The negative impact of geopolitical risk on eco-efficiency is worse in high-income countries than in low-income countries.

A more comprehensive understanding of the impact of renewable energy on carbon efficiency could serve to achieve the win-win goal of carbon reduction and economic growth. This paper develops a data envelopment analysis model to measure carbon efficiency, analyses the non-linear, mediating and heterogeneous effects of renewable energy on carbon efficiency using panel data for 116 countries over the period 2005 to 2020. The results show that: (i) there is a certain difference in carbon efficiency among countries in different income groups. Specifically, the carbon efficiency of low-income and low-middle-income countries is generally in a low-efficiency state. (ii) There is an intermediary effect between renewable energy and carbon efficiency, which means that carbon efficiency can be indirectly improved by promoting technological innovation. (3) With the increase of income level, the positive effect of renewable energy on carbon efficiency is more effective. Finally, targeted policy recommendations are proposed.

With the continuous increase of marine development, intensive economic activities have reduced the marine carbon efficiency and seriously damaged the marine ecological environment, which needs reasonable environmental regulations to guide. This study aims to examine the interactions between marine environmental regulation and carbon efficiency to achieve the goal of carbon emission reduction for the sustainability of marine ecosystem. This study empirically analyzes the heterogeneous effects and mediating effects of China's marine environmental policies on marine carbon efficiency using the mediating effects model and generalized moments estimation. The results show that there is a "U" shaped relationship between marine environmental regulation and marine carbon efficiency in China, marine environmental regulation can indirectly promote marine carbon efficiency through the transmission mechanism of resource allocation efficiency and structural optimization of marine industries. In addition, China's marine environmental regulation policies have regional heterogeneous effects on marine carbon efficiency. This study provides a new perspective for optimizing marine carbon efficiency and sustainable development of marine ecosystem.

Renewable energy is a key component of the Sustainable Development Goals (SDGs) set by the United Nations, and is an important factor in facilitating the energy transition and carbon neutrality of countries. This study aims to examine the interaction between renewable energy consumption and carbon efficiency to achieve the goal of carbon reduction for environmental sustainability. This study measures the carbon efficiency globally and countries in each income group through the data envelopment analysis method. Then, this study empirically analyzes the impact of renewable energy consumption on carbon efficiency by controlling for heterogeneity, cross-sectional dependence and generalized moments estimation. The results show that (1) carbon efficiency enhances with the increase of income level, and the global carbon efficiency improvement is mainly due to the change of technological progress. (2) There is a weak improvement in scale efficiency in LI and LMI, dragged down by the decline in pure technical efficiency, which is the opposite of countries at higher income levels. (3) Renewable energy consumption has a positive effect on the improvement of carbon efficiency at all income levels, and there is a two-way causal relationship between renewable energy consumption and carbon efficiency. The effect of renewable energy on carbon efficiency is most significant in countries with middle income levels. Policy makers in each country should promote the renewable energy development to achieve energy transition and reduce carbon emissions. In addition, this study emphasizes that countries should consider the interaction between the renewable energy consumption and income level in ensuring sustainable development.

Given that improving carbon emission efficiency is the cost-effective measure to achieve carbon peak and carbon neutrality, achieving carbon peak/neutrality with minimal economic costs demands a more comprehensive understanding of the factors affecting carbon emission efficiency. In this work, a dynamic panel data analysis with generalized method of moments (GMM) estimation was developed to investigate the effects of industrialization, and renewable energy on carbon emission efficiency, to uncover the interactive and influence channels among these influencing factors using 131 countries' panel data. The results show that: (i) although urbanization and population aging are not conducive to the improvement of carbon emission efficiency in higher-income countries, they are conducive to the improvement of carbon emission efficiency in lower-income countries. (ii) Trade openness in the global community will have a negative impact on carbon emission efficiency. However, trade openness plays a vital role in improving the carbon emission efficiency of high-income economies. (iii) The rise in renewable energy and industrialization has improved carbon emission efficiency, and the utilization and development of renewable energy in higher-income countries is generally higher than in lower-income countries, and the environmental governance system is relatively sound.

Meeting the huge impact of COVID-19 on the environment requires better research on pandemic and pollution. What is the research capacity of the COVID-19 and environment in developing countries? Can this research capacity support developing countries to deal with the environmental challenges induced by the pandemic? This work is addressed to comprehensively assess the research capacity of the COVID-19 and environment in developing countries using bibliometric analysis techniques and content analysis approach to mining the Web of Science database. The results of data mining were unexpected: the global leader of the COVID-19 and environmental research was not these developed countries, but these developing countries so far, the end of 2020. Developing countries have published more papers on the pandemic and environment than developed countries, and developing countries also dominate pandemic and environmental research in terms of research institutions and authors. The results showed that (i) the impact of COVID-19 and the environment was bidirectional; (ii) energy consumption has posed great impact on environment; (iii) application of big data and artificial intelligence played an important role in improving environmental quality during the COVID-19 pandemic. Finally, policy recommendations such as formulating relevant policies and environmental standards, strengthening international exchanges and cooperation, and adjusting and improving energy consumption structure that were put forward for developing countries to meet the environmental challenges induced by the pandemic were offered. Graphical abstract.