Search Results (22)

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition and facilitating sustainable development. This research utilizes a dataset comprising 30 provincial panels spanning from 2010 to 2023. This study found that AI technology can promote renewable energy development, a conclusion that still holds after robustness and endogeneity tests. An examination of the mechanism reveals that AI technology facilitates the advancement of renewable energy through the enhancement of trade openness and the concentration of manufacturing activities. The analysis of the moderating effect indicates that environmental regulation and environmental protection expenditures positively moderated the relationship between AI technology and renewable energy development and climate policy uncertainty negatively moderated the relationship between AI technology and renewable energy development. Further analysis revealed that AI technology has the potential to substantially improve the development of local renewable energy resources while also facilitating the advancement of renewable energy in adjacent areas, exhibiting spatial spillover effects. This study verifies the positive effects of AI technology on renewable energy development and enriches existing research perspectives in the field of energy economics. Full article

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

Against the dual demands of green transformation and digital integration in the manufacturing industry, green value co-creation has become a core pathway for enterprises to achieve sustainable development. However, the role of artificial intelligence (AI) in driving green value co-creation remains under explored, especially in the context of Chinese manufacturing. To enrich this research, this study aims to investigate the impact of AI development on corporate green value co-creation and its intrinsic mechanism. This study draws on panel data of listed manufacturing enterprises listed on China’s Shanghai and Shenzhen A share markets spanning the period 2015–2024, and employs multiple regression and negative binomial regression as research methodologies to empirically examine the impact of AI development on corporate green value co-creation and its underlying mechanisms. The results demonstrate that: AI development exerts a significantly positive effect on manufacturing enterprises’ green value co-creation, which is achieved by enhancing firms’ technological spillover capacity and total factor productivity (TFP); financing constraints negatively moderate the aforementioned relationship, while corporate influence plays a positive moderating role; heterogeneity analysis reveals that this impact is more pronounced for enterprises under voluntary regulation, state-owned enterprises (SOEs), and high-pollution enterprises. This study elucidates AI’s role and mechanism in corporate green development at the micro level, provides empirical evidence for related research, and offers practical insights to promote enterprise AI advancement and green value co-creation. Full article

In recent years, the rapid advancement of artificial intelligence (AI) technology has exerted profound implications for urban green total factor efficiency (GTFE). Drawing on panel data of 279 Chinese cities from 2012 to 2021, this study empirically examines the impact of AI on urban GTFE from multi-dimensional perspectives including green finance and new-quality productive forces. The key findings are as follows: ➀ AI significantly enhances urban GTFE with a nonlinear threshold effect, and this conclusion remains robust after multiple robustness tests incorporating machine learning models and econometric approaches. ➁ Heterogeneity analysis reveals that AI exerts significantly heterogeneous effects across different regional locations, city sizes, urban hierarchies, and between transportation hubs/non-hubs and old industrial bases/non-bases. While an overall positive correlation is observed, the positive effect of AI is not statistically significant in western China, mega-cities, large cities, and central cities; conversely, an insignificant negative effect is detected in central-eastern China and old industrial bases. ➂ Mechanism tests demonstrate that AI facilitates GTFE improvement through channels such as upgrading green finance development and advancing new-quality productive forces. ➃ Spatial spillover effect analysis indicates that AI generates a positive spatial spillover effect on the GTFE of local cities. Based on these findings, targeted policy recommendations are proposed to promote urban GTFE enhancement and achieve sustainable development. Full article

China’s ecological civilization construction and the “dual-carbon” strategy highlight the urgent need for coordinated governance of pollution and carbon reduction. Whether data elements and artificial intelligence integration (DEAII) can serve as a new pathway to achieve this goal remains to be explored. This study investigates the dynamic effects of DEAII on pollution and carbon reduction using panel data from 275 prefecture-level cities in China during 2009–2021. An evaluation index system and a modified coupled coordination degree model are developed to measure DEAII, while an ordinary least squares (OLS) fixed effects model is applied to assess its impacts. The results show stage-specific effects of DEAII, including the phenomenon of “pollution reduction but carbon increase”. Mechanism analysis indicates that improvements in green energy technology efficiency (GETE) and optimization of urban spatial structure are the main channels for achieving synergy. Heterogeneity analysis reveals that although government attention to environmental protection strengthens pollution control, it has limited effects on short-term carbon reduction. Moreover, the carbon reduction benefits of green energy transition pilots exhibit a time lag, and the “digital intelligence divide” generates negative spatial spillovers. These findings provide new evidence for the dilemma of “environmental protection without low-carbon benefits” and suggest policy directions for enhancing the coordinated governance of pollution and carbon reduction. Full article

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and western regions. Empirical analysis is conducted using panel data from 22 provinces and municipalities across eastern and western China spanning 2014–2024, employing both a spatial difference-in-differences model and a dual machine learning model. Findings reveal that both AI technology spillovers and sustainable innovation significantly enhance the efficiency of industrial leapfrogging across regions. Their synergistic effects are pronounced, generating positive spatial spillovers. Institutional environments exert a significant influence on leapfrog industrial development. By regulating AI technology environments and sustainable innovation environments, institutional frameworks enhance leapfrogging efficiency, though this mediation exhibits a dual-threshold effect: most western provinces have yet to cross the first threshold. Industrial and economic heterogeneity weaken the efficiency of AI technology spillovers and sustainable innovation in facilitating industrial leapfrogging between eastern and western regions. This research provides robust empirical support for addressing industrial development imbalances and enhancing industrial resilience between eastern and western regions. Full article

The rapid development of new-generation information technologies, such as cloud computing, artificial intelligence, big data, and blockchain, is profoundly reshaping production and lifestyles, with regional development patterns. This study employs text analysis to extract the policy adoption timeline of cloud computing from official documents and constructs a quasi-natural experiment framework. First, spatial autocorrelation and hotspot analysis reveal significant spatial dependence in the urban green total factor productivity (GTFP). Accordingly, using panel data of 284 Chinese cities from 2000 to 2023, we apply a spatial difference-in-differences (SDID) model to empirically examine the impact of cloud computing on the urban GTFP. The results show that, first, the adoption of cloud computing significantly enhances the local GTFP, but simultaneously suppresses neighboring cities’ GTFP through the siphon effect, thereby generating negative spatial spillover effects. These findings remain robust across parallel trend tests, placebo tests, and multiple robustness tests. Second, mechanism analysis indicates that improved resource allocation efficiency and strengthened green innovation are the two core channels through which cloud computing promotes GTFP. Third, heterogeneity analysis reveals that cloud computing exhibits stronger siphon effects in smaller cities, generates significant positive spatial spillover effects in coastal regions, and effectively fosters GTFP growth within urban agglomerations, while exerting limited influence on non-agglomerated areas. Moreover, industrial agglomeration further amplifies the positive impact of cloud computing on GTFP. Additionally, from the perspective of regional policies, this study finds that promoting the integrated development of urban agglomerations, reducing administrative monopoly, facilitating free factor mobility, and advancing urban international economic activities are effective pathways to mitigate the siphon effect of cloud computing on the urban GTFP. Based on these findings, this study offers targeted policy recommendations to leverage cloud computing for advancing green and high-quality urban development. Full article

Decision-making algorithms based on big data, artificial intelligence and other technologies are increasingly being applied to urban emergency decision-making, and urban smart emergency response is gradually appearing to be transformed from traditional empirical decision-making to human–machine collaborative decision-making. This paper explores the motivations for cooperative decision-making between leaders (human) and followers (machines) in urban emergency management in the presence of science and technology input spillovers. It focuses on the impact of human–machine cooperative decision-making on urban emergency response capacity, science and technology inputs and total urban emergency response benefits and discusses how to maximize the total benefits of urban emergency response under different levels of spillovers. In this paper, a three-stage dynamic game model is constructed: leaders and followers decide whether to establish a cooperative decision in the first stage; decide the level of science and technology inputs in the second stage; and compete for sequential decisions in the third stage. It was found that, firstly, unlike the case of static games, in sequential games, leaders and followers develop a willingness to cooperate in decision-making only when the spillover coefficients are in the lower range. Second, cooperative human–machine decision-making may diminish the importance of human experience in urban emergency management. Finally, the effectiveness of collaborative human–machine decision-making in urban emergencies deserves further research. The research in this paper provides recommendations for smart urban emergency management. Full article

Veterinary vaccines are essential tools for controlling infectious and zoonotic diseases, safeguarding animal welfare, and ensuring global food security. However, conventional vaccines are hindered by cold-chain dependence, thermal instability, and logistical challenges, particularly in low- and middle-income countries (LMICs). This review explores next-generation veterinary vaccines, emphasizing innovations in thermostability and delivery platforms to overcome these barriers. Recent advances in vaccine drying technologies, such as lyophilization and spray drying, have improved antigen stability and storage resilience, facilitating effective immunization in remote settings. Additionally, novel delivery systems, including nanoparticle-based formulations, microneedles, and mucosal routes (intranasal, aerosol, and oral), enhance vaccine efficacy, targeting immune responses at mucosal surfaces while minimizing invasiveness and cost. These approaches reduce reliance on cold-chain logistics, improve vaccine uptake, and enable large-scale deployment in field conditions. The integration of thermostable formulations with innovative delivery technologies offers scalable solutions to immunize livestock and aquatic species against major pathogens. Moreover, these strategies contribute significantly to One Health objectives by mitigating zoonotic spillovers, reducing antibiotic reliance, and supporting sustainable development through improved animal productivity. The emerging role of artificial intelligence (AI) in vaccine design—facilitating epitope prediction, formulation optimization, and rapid diagnostics—further accelerates vaccine innovation, particularly in resource-constrained environments. Collectively, the convergence of thermostability, advanced delivery systems, and AI-driven tools represents a transformative shift in veterinary vaccinology, with profound implications for public health, food systems, and global pandemic preparedness. Full article

In recent years, the rapid progress of artificial intelligence (AI) technologies has significantly influenced urban green energy efficiency. Leveraging panel data from 271 cities in China spanning the period of 2010–2022, this paper conducts an empirical analysis of the impact of AI on urban green energy efficiency from multiple perspectives, including green finance, industrial chain resilience, and the intensity of environmental regulation. The key findings are as follows: ① AI has a substantial positive effect on urban green energy efficiency, a conclusion that is consistently confirmed through multiple robustness tests; ② Heterogeneity analysis shows that the influence of AI varies markedly across different regions, city sizes, and whether cities are central, coastal, or transportation hubs, yet it maintains an overall positive correlation. However, its impact is relatively weaker in the northeastern region and in megacities; ③ Mechanism tests reveal that AI enhances urban green energy efficiency by improving green finance, strengthening industrial chain resilience, and intensifying environmental regulation; ④ Spatial spillover analysis indicates that AI exerts a positive spatial spillover effect on local urban green energy efficiency. Based on these findings, this paper offers targeted policy recommendations to enhance urban green energy efficiency and advance sustainable development. Full article

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

This research investigates the spillover effects between assets of the Fourth Industrial Revolution (4IR), focusing on the role of climate policy uncertainty in shaping these interactions. Using a time-varying parameter vector autoregressive (TVP-VAR) approach and a joint connectedness method, the analysis incorporates five global indices representing key 4IR domains: the internet, cybersecurity, artificial intelligence and robotics, fintech, and blockchain. The findings reveal significant interdependencies among 4IR assets and evaluate the effect of risk factors, including climate policy uncertainty, as a critical driver of the determinants of returns. The results indicate the growing impact of climate-related risks on the structure of connectedness between 4IR assets, highlighting their implications for portfolio diversification and risk management. These insights are vital for investors and policymakers navigating the intersection of technological innovation and environmental challenges in a rapidly changing global economy. Full article

The tourism industry’s explosive growth has triggered severe carbon emission issues, making enhancing tourism carbon efficiency (TCE) a pressing concern for achieving sustainable tourism development. The widespread application of artificial intelligence (AI) in tourism presents new opportunities. This study applies the Environmental Kuznets Curve (EKC) theory to examine the pathways and mechanisms of AI’s impact on TCE, with a focus on China. The findings reveal that AI significantly enhances TCE, where improvements in tourism labor productivity, the rationalization of the tourism industry structure, and advancements in tourism technology are the key channel mechanisms. Heterogeneity tests indicate that AI substantially boosts TCE in eastern developed regions and areas with deficient tourism resource endowments. Furthermore, AI exhibits significant spatial spillover effects, enhancing both local and neighboring regions’ TCE. These insights provide crucial policy implications for utilizing AI to promote China’s sustainable tourism industry. Full article