Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness

Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development

This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction. Nonlinear analysis indicates several key insights: (i) a higher proportion of the industrial sector diminishes the inhibitory effect of AI on ecological footprints and carbon emissions but enhances its positive impact on energy transitions; (ii) increased trade openness significantly amplifies AI’s ability to reduce carbon emissions and promote energy transitions; (iii) the environmental benefits of AI are more pronounced at higher levels of AI development, enhancing its ability to reduce ecological footprints and carbon emissions and promote energy transitions; (iv) as the energy transition process deepens, AI’s effectiveness in reducing ecological footprints and carbon emissions increases, while its role in promoting further energy transitions decreases. This study enriches the existing literature by providing a nuanced understanding of AI’s environmental impact and offers a robust scientific foundation for global policymakers to develop sustainable AI management frameworks.

In the context of trade protectionism impacting economic and environmental sustainability, a more comprehensive understanding of the impact of trade on carbon emissions is critical to economic and environmental sustainability. Existing literature mainly explores the impact of trade on carbon emissions from the perspective of trade openness, neglecting the perspectives of trade diversification and trade direction. This study aims to fill this gap by investigating the impact of trade openness (measured by trade volume, import, and export), and trade diversification (measured by import diversification and export diversification) on carbon emissions based on data from OECD and G20 countries between 1997 and 2019. The study further explores the heterogeneity, asymmetry, and mediation effects. The results demonstrate that (i) the impact of trade on carbon emissions is heterogeneous, with trade openness leads to an increase in carbon emissions, while trade diversification leads to a reduction in carbon emissions. Moreover, import diversification has the strongest effect on reducing carbon emissions. (ii) The impact of trade openness on carbon emissions is asymmetry. Trade openness increases carbon emissions at 10%–50% quantile levels and reduces carbon emissions at 80%–90% quantile levels. However, the impact of trade diversification on carbon emissions is consistent. (iii) The impact of trade openness on carbon emissions is mediated by technology effect and structural effect. On one hand, trade openness leads to an increase in carbon emissions by the industrial structure. On the other hand, it contributes to the reduction of carbon emissions by technological progress. These findings could serve to better understand the complexity of free trade's impact on economic and environmental sustainability.

Could trade protectionism reshape the nexus of energy-economy-environment? Insight from different income groups

Using the threshold approach, this study provides new insights into the correlation between trade openness and carbon emissions in a sample of Asian countries. The sample includes 22 Asian countries covering the period from 2000 to 2019. The empirical results demonstrate the existence of a threshold effect in terms of trade openness and its relationship with carbon emissions. In particular, the study shows that trade openness has a positive effect on carbon emissions only until a certain threshold is reached; exceeding this threshold leads to a reduction in carbon emissions associated with greater trade openness. The resulting representation of an inverted U-shaped correlation between trade openness and carbon emissions stands up to scrutiny by panel quantile regression and the quadratic methodology (U-test) and confirms its robustness. These findings emphasise the importance of determining an "optimal" level of trade openness, which is essential to effectively govern the mitigation of carbon emissions, as well as to challenge the notion that trade openness inevitably leads to increased carbon emissions. The implications of the Environmental Kuznets Curve (EKC) theory, when accompanied by empirical evidence of knowledge spillovers resulting from trade openness, support innovation pathways that lead to the adoption of greener practices, promoting a cleaner and healthier societal environment.Graphical

This study aims to test the Environmental Kuznets Curve (EKC) hypothesis in ASEAN member countries from 2015 to 2022 and to analyze the effect of per capita GDP, population size, energy transition, foreign investment, and trade openness on carbon emissions in ASEAN member countries from 2015 to 2022. This research uses a quantitative approach with data sourced from the International Energy Agency and the World Bank. The analytical method applied is multiple linear regression using panel data from 10 ASEAN countries for the period 2015-2022. The results show that the Environmental Kuznets Curve (EKC) hypothesis is confirmed in ASEAN member countries for the period 2015-2022, with a turning point for the relationship between per capita GDP and CO2 emissions in the ASEAN region at 17.11 trillion dollars. Singapore and Brunei Darussalam are the two countries among 11 in the ASEAN region that have passed the scale and structural effect phase on the Environmental Kuznets Curve, while others remain on the left side of the EKC. Per capita GDP can significantly increase or reduce carbon emissions in the ASEAN region depending on whether the member country has reached the EKC turning point phase. Population size, foreign investment, and trade openness significantly increase carbon emissions in the ASEAN region. The energy transition can reduce carbon emissions in the ASEAN region, though not significantly.

The nonlinear effect of population aging on carbon emission-Empirical analysis of ten selected provinces in China

The environmental impact of trade openness has been a subject of extensive research, but gaps exist in understanding how green financing interact with trade openness on carbon emissions in emerging economies. Thus, this research aims to investigate the moderating effect of green financing on the relationship between trade openness and carbon emissions in emerging countries. The study uses a balanced panel dataset comprising BRIC and CIVETS countries spanning 1998 to 2022 years. Employing threshold effect model, we uncover involved patterns and critical thresholds that influence the environmental outcomes of trade dynamics. Finally, this paper employs different econometric models fortifying the methodological underpinning of the study. We find that green financing and trade openness lead to a reduction in carbon emissions as they are negatively associated with emissions. Further, our study finds that green financing interacted with trade openness to reduce carbon emissions because interaction of trade openness makes stronger the relationship to reduce emissions. When these two factors interact, their combined effect is even more potent. Additionally, this study identifies a threshold effect in the role of green financing, where its inhibitory impact on carbon emissions intensifies as the level of green financing increases, lead to greater reductions in emissions. This research contributes in identifying the moderating effects of green financing and the threshold effects on carbon emissions at different levels of green financing. Thus, this article implies that increasing both green financing and trade openness, along with understanding their interactive and threshold effects, is crucial for achieving substantial carbon emissions reductions.

In the context of global climate change and the development of artificial intelligence, understanding the impact mechanism of artificial intelligence on carbon emissions is of great practical significance. This paper employs a mediating model and a panel data approach to analyze data from 2010 to 2022 of listed companies in China, analyzing how artificial intelligence affects firms’ carbon emissions at the micro level, while further exploring the intermediary role of green technology innovation. The study found that: First, enterprises can significantly promote carbon emission reduction using artificial intelligence technology, and the association between AI advancement and carbon emissions follows an inverted U-shaped pattern; Second, green innovation serves as an important intermediary channel through which AI affects firms’ carbon emissions; Third, substantial variation exists in the impact of artificial intelligence on corporate carbon emissions, and enterprises in the central and western regions have lower thresholds for carbon emission reduction compared with enterprises in the eastern areas, and the carbon emission reduction effect is more significant. Based on the above conclusions, the following recommendations are put forward: First, rationally plan artificial intelligence investment and accelerate the crossing of carbon emission reduction threshold; Second, AI should be used to empower green technology innovation and give full play to the mediating role of green technology innovation; Third, differentiation strategies should be formulated based on the specific development characteristics of enterprise artificial intelligence.

The Hu-Bao-O-Yu urban agglomeration is an important energy exporting and high-end chemical base in China, and is an important source of carbon emissions in China. The early achievement of peak carbon emissions in this region is particularly crucial to achieving the national carbon emission reduction targets. However, there is a lack of multi-factor system dynamics analysis of resource-dependent urban agglomerations in Northwest China, as most studies have focused on single or static aspects of developed urban agglomerations. This paper analyses the relationship between carbon emissions and their influencing factors, constructs a carbon emission system dynamics model for the Hu-Bao-O-Yu urban agglomeration, and sets up different single regulation and comprehensive regulation scenarios to simulate and predict the carbon peak time, peak value, and emission reduction potential of each city and urban agglomeration under different scenarios. The results show that: (1) Hohhot and Baotou are expected to reach peak carbon by 2033 and 2031 respectively, under the baseline scenario, while other regions and the urban agglomeration will not be able to reach peak carbon by 2035. (2) Under single regulation scenarios, the effect of factors other than the energy consumption varies across cities, but the energy consumption and environmental protection input are the main factors affecting carbon emissions in the urban agglomeration. (3) A combination of the economic growth, industrial structure, energy policy, environmental protection, and technology investment is the best measure to achieve carbon peaking and enhance the carbon emission reduction in each region as soon as possible. In the future, we need to coordinate the economic development, energy structure optimisation and transformation, low-carbon transformation of industry, strengthen research on carbon sequestration technology, and further increase the investment in environmental protection to make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration with an optimal emission reduction.

Exploring the role of nuclear energy in the energy transition: A comparative perspective of the effects of coal, oil, natural gas, renewable energy, and nuclear power on economic growth and carbon emissions

Research over the past three decades has provided rich empirical evidence for the inverted U-shaped EKC theory, but current problems facing advancing climate mitigation actions require us to re-examine the shape of global EKC rigorously. This paper examined the N-shaped EKC in a panel of 214 countries with 12 traditional and emerging variables, including institutions and risks, information and communication technology (ICT), artificial intelligence(AI), resource and energy use, and selected social factors. The two-dimensional Tapio decoupling model based on N-shaped EKC to group homogeneous countries is developed to explore the inter-group heterogeneous carbon emission effects of each variable. Global research results show that the linear and cubic terms of GDP per capita are significantly positive, while the quadratic term is significantly negative, regardless of whether additional variables are added. This means the robust existence of an N-shaped EKC. Geopolitical risk, ICT, and food security are confirmed to positively impact per capita carbon emissions, while the impact of composite risk, institutional quality, digital economy, energy transition, and population aging are significantly negative. The impact of AI, natural resource rents, trade openness, and income inequality are insignificant. The inflection points of the N-shaped EKC considering all additional variables are 45.08 and 73.44 thousand US dollars, respectively. Combining the turning points and the calculated decoupling coefficients, all countries are categorized into six groups based on the two-dimensional decoupling model. The subsequent group regression results show heterogeneity in the direction and magnitude of the carbon emission impacts of most variables. Finally, differentiated carbon emission reduction strategies for countries in six two-dimensional decoupling stages are proposed.

<sec><title>Objective</title> The world is still in a phase of rapid industrialization and urbanization. Excessive carbon emissions has become the primary root cause of various urban or even global environmental problems, further impacting human physiological and psychological health. Cities are the largest sources of carbon emissions and are crucial regions for achieving carbon neutrality goals. Urban blue-green infrastructure (UBGI), comprising natural, semi-natural, or artificial green and blue spaces within cities, is considered as the most important carbon sink space in urban areas and has increasingly attracted widespread attention from researchers. However, there are still many unresolved issues regarding the effectiveness of UBGI in carbon sink enhancement and emission reduction: 1) How is the energy efficiency of carbon sink enhancement and emission reduction measured, and what factors influence it? 2) What are the mechanisms and pathways through which UBGI enhances carbon sink and reduces carbon emission? 3) How can UBGI be regulated to better enhance its effectiveness in carbon sink enhancement and emission reduction? 4) What are the limitations and potential directions for future research? This research aims to address these issues and propose scientifically sound planning strategies for UBGI construction to achieve urban carbon neutrality goals. </sec><sec><title>Methods</title> Through literature synthesis and deduction, this research organizes and analyzes the multi-scale measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction, identifies corresponding influencing factors at each scale, and constructs multi-scale planning strategies for UBGI based on the logical framework of “measurement methods–influencing factors – planning strategies”. </sec><sec><title>Results</title> The research proposes UBGI planning strategies across three spatial scales (site, community and urban area), covering three key aspects: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. Based on current research gaps and planning needs, five major research topics are further identified. This research provides a detailed analysis of the measurement methods and influencing factors of UBGI’s efficiency in carbon sink enhancement and emission reduction from three perspectives: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. The research finds significant differences in the measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction efficiency across different scales. Contradictory results may occur at different scales, and large-scale research often lacks characterization of internal features, leading to unclear mechanisms of influencing factors and obstructing practical planning. Based on the interpretation of UBGI’s mechanisms for carbon sink enhancement and emission reduction at different scales, this research formulates UBGI planning strategies across three spatial scales (site, community, and urban area). These strategies include: 1) At the site scale, for carbon sequestration and sink enhancement – carbon sink at the source, land balance, and ecological design; for emission reduction – symbiosis with buildings and integration into daily life. 2) At the community scale, for carbon sequestration – overall balance of revenue and expenditure, precise positioning, and proper interconnection of the carbon chain; for emission reduction – incorporation of cool islands and co-construction. 3) At the urban area scale, for carbon sequestration – enhancement of ecological space management and establishment of a carbon-safe pattern; for emission reduction – demand-based layout and organic dispersion. Finally, the research proposes five major research topics for the planning of UBGI’s carbon sink enhancement and emission reduction: How to construct unified measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction across scales? How to measure UBGI’s efficiency in carbon reduction based on temperature reduction (or preservation) at the site scale? How to integrate the pathways of carbon sink enhancement and emission reduction for a life cycle assessment of UBGI? How to balance UBGI’s carbon sink enhancement and emission reduction with other functions to achieve the optimal layout for comprehensive benefits? How to achieve urban “carbon justice” through UBGI? </sec><sec><title>Conclusion</title> The carbon sink pathway of the strategy framework requires “carbon sink at the source – precise positioning – safe pattern”, and the emission reduction pathway requires “symbiotic integration – co-construction and sharing – organic dispersion”. The key trade-offs between these two pathways at three spatial scales may provide theoretical support and practical guidance for UBGI construction and management. The five major research topics mentioned above may offer valuable assistance for UBGI construction and future research. </sec>

Energy transition plays a crucial role in supporting sustainable economic growth and the reduction in carbon emissions. In fact, China implemented the national sustainable development experimental zone policy to achieve sustainable development goals, including an energy transition. This paper divided the energy transition dimension into energy consumption and carbon dioxide emissions based on the perspectives of energy input and output. Furthermore, using panel data for 214 cities at the prefecture level in China from 2006 to 2019, the study measured the impact of the national sustainable development experimental zone policy on energy transitions by employing a difference-in-difference (DID) model and an intermediary effect model. The results showed that the national sustainable development experimental zone policy reduced energy consumption and carbon dioxide emissions and accelerated energy transition. The conclusions still held after a series of robustness tests. Additionally, the results of the heterogeneity analysis of different experimental zone types indicated that, compared with prefecture-level experimental zones, county experimental zones play a more obvious role in reducing energy consumption and carbon dioxide emissions. In addition, the results of the heterogeneity analysis of the urban geographical location showed that the national sustainable development experimental zone policy had different negative effects on urban energy consumption and carbon dioxide emissions in different regions, and the impact of policy on energy transition was experienced, in decreasing order, by the western, central, and eastern regions. The results of the mechanism verification indicated that the national sustainable development experimental zone policy can affect energy consumption and carbon dioxide emissions via technological progress and upgrading industrial structure, which had a relatively high aggregation order in the variables deployed.

Purpose This study aims to explore the impact of artificial intelligence (AI), an important avenue for sustainable development in the digital age, on carbon emission reduction and its pathways of influence. Design/methodology/approach The study empirically examines the impact of AI development on carbon emission levels by choosing a two-way fixed effects model based on panel data from 30 provinces in China from 2011 to 2019. Findings The results show that AI has a significant inhibitory effect on carbon emissions. Green innovation, energy efficiency and industrial agglomeration are effective transmission mechanisms for AI to suppress carbon emissions. Based on the provincial heterogeneity, it is found that there is a threshold effect of AI level on carbon emissions, and the carbon emission reduction effect of AI development is more significant in regions with high industrial structure level and high human capital level. Research limitations/implications AI has a significant inhibitory effect on carbon emissions. Therefore, China should emphasize investment in AI development, promote the development of intelligent infrastructure and broaden the field of AI applications, thereby realizing the carbon reduction effect of AI. Practical implications This study reveals the key role of AI in addressing climate change and provides effective practical solutions for achieving the "dual carbon" goals. China should fully consider the empowering role of AI in carbon reduction and balance the relationship between economic development and environmental sustainability. Originality/value This study expands the field of research on the economic consequences of AI and the factors affecting carbon emissions, reveals its intrinsic transmission mechanisms and deepens the theoretical understanding of the effects of AI on carbon emission reduction. This paper not only provides a scientific basis for achieving the “dual-carbon” goals but also provides policy recommendations for the joint response to the challenge of climate change.