In the contemporary world, the resource curse paradox remains a significant challenge in Asian countries, where abundant natural resources have not consistently translated into sustainable economic growth. To overcome this paradox, strengthening human skills, advancing digital governance, and increasing digital investments are elements of the knowledge-based economy that unlock the productive potential of resource-rich industries in enhancing efficiency and promoting inclusive and resilient economic growth. Given this context, the study analyzes both the direct and indirect relationships between natural resources and economic growth, mediated by human capital and skills, digital investment, and e-governance in Asian economies. Utilizing data from 2014 to 2022, the study applies the generalized method of moments to explore these dynamics. Our empirical findings confirm the resource curse hypothesis, revealing that higher levels of natural resources inhibit economic growth. In contrast, digital investment, e-governance, and human capital and skills significantly drive economic growth across all models. Moreover, their moderating effects are positive, helping to mitigate the adverse impacts of the resource curse. The consistency of these coefficients has been validated through robustness checks. These findings suggest that policymakers should promote human skills, digital investments, and e-governance within resource-based industries to support economic revival and counter the negative effects of the resource curse.

As developed economies accelerate their shift from fossil fuels to sustainable energy systems, understanding how structural, technological, and institutional factors interact to shape carbon emissions becomes critical. This study investigates the heterogeneous impacts of green energy transition, energy intensity, digitalization, and governance on carbon emissions across 25 developed countries from 1999 to 2024. Employing a combination of fixed effects estimation, method of moments quantile regression (MMQR), and common correlated effects (CCE) estimation, we uncover nuanced, quantile-specific dynamics that traditional mean-based approaches overlook. Our findings reveal that green energy transition consistently mitigates emissions, with its strongest effects in low- and middle-emission contexts, while structural rigidities attenuate its influence in high-emission economies. Energy intensity demonstrates minimal influence in low-emission countries but plays a pivotal role in moderating emissions where industrial activity is intensive. Digitalization amplifies emission reductions, particularly in moderately emitting economies, while governance underpins these effects by enhancing policy enforcement and institutional capacity. Crucially, interactions between green energy transition, digitalization, and governance underscore the power of integrated strategies: emission reductions are maximized when renewable energy adoption is coupled with advanced digitalization and robust governance. These results offer actionable insights for designing context-sensitive, multidimensional climate policies that advance SDGs 7, 8, and 13.

Amidst urgent global calls for sustainable energy transition, China has made significant progress in developing the photovoltaic (PV) sector, but it is also accompanied by technical bottlenecks. Therefore, understanding the evolution and driving mechanisms of China's PV collaborative innovation network is of great significance, as it determines the acceleration of developing a cleaner energy system. By employing social network analysis and the temporal exponential random graph model, this study finds that the correlation strength of China's PV collaborative innovation networks has been enhanced from 2001 to 2023. The network has evolved from a dual-core-driven model to a multipolar collaborative model and has formed an association pattern centered around the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions, exhibiting a small-world phenomenon and core-periphery structure. The evolution process of China's PV collaborative innovation network shows significant endogenous structural dependencies. This is mainly reflected in the popularity of unidirectional relationships, the development of bidirectional reciprocal relationships, and the closure of triadic relationships. The network shows path dependence. Provincial economic capacity, electricity demand, human capital, and research and development investment exhibit significant actor relationship effects. In contrast, environmental pollution only shows a sender effect. Furthermore, economic zone and geographical distance are also key formation factors for China's PV collaborative innovation network.

This study investigates the spatial differentiation of credit risk related to global climate change in 30 Chinese provinces from 2010 to 2020. We utilize geodetector techniques to explore the effects of five critical factors on credit risk: technological progress, economic pressure, climate change, financial management, and the ecological environment. The findings indicate significant spatial variance in credit risk across China. Per capita income, unemployment rate, annual extreme temperatures, energy consumption per capita, financial accumulation, patent applications, and number of research and development personnel have strong explanatory power for credit risk, whereas technological progress increases significantly over time. Furthermore, this study finds that regions in China, where climate change affects credit risk, underwent spatial and temporal transfers from south to north from 2015 to 2020. In this process, climate factors affect credit risk directly. This study suggests that regional policies are subject to variance in climate change, credit risks, and economic conditions.

Purpose This research aims to explore how circular economy practices (CEPs) address environmental challenges in manufacturing while providing a competitive edge for sustainable growth. It examines the role of green knowledge sharing, green creative climate and enhanced artificial intelligence information quality in fostering the successful adoption of CEP, offering strategies to improve collaboration and innovation in green practices. Design/methodology/approach This research employed a quantitative method by using a survey to gather data from 332 respondents representing Chinese manufacturing SMEs. We applied partial least square structural equation modeling for hypothesis testing, offering robust insights into the relationships among the variables and their implications for the manufacturing sector. Findings The results show that green knowledge sharing and green creative climate are favorably connected to CEP. Meanwhile, green creative climate is a key mediator between green knowledge sharing and CEP. In comparison, artificial intelligence information quality positively moderates among targeted relationships. The importance-performance map analysis highlighted the superior importance (28.70) of green knowledge sharing and the exceptional performance (67.638) of green creative climate toward CEP. Research limitations/implications The findings can aid in improving academic and professional understanding of managing and evaluating CEP at the project and firm levels in the manufacturing sector. Therefore, policymakers and managers may implement CEP by emphasizing green knowledge sharing, green creative climate, and artificial intelligence information quality. Originality/value This research contributes to the limited prevailing literature by enhancing the understanding of green knowledge sharing, green creative climate, artificial intelligence information quality and CEP. It sheds light on the potential role of green knowledge sharing and green creative climate, as they are performing the role of catalysts for enhancing information quality and fostering CEP in organizations.

Digitization has expanded the scale, scope, and velocity of international service trade; nevertheless, a notable disparity persists in the digitization journey between developed and developing nations. This discrepancy arises from the capital and technology-intensive essence of digital technologies. Consequently, this divide further accentuates the rift in international digital trade between advanced and developing economies. For effective policy interventions, estimating and analysing the prevailing gaps in the global trade network and convergence potential between leaders and ladders is imperative. Hence, this study aims to estimate disparities at both global and regional perspectives in the realm of digital trade. It uses the Dagum Gini coefficient, kernel density, Moran’s I and Markov chain to measure the international differences and dynamic evolution of digital deliverable services trade between 2005 and 2020. The findings indicate that, when examining regional disparities, the variance in digital delivery service trade between Asia and Africa exhibits a progressively widening pattern. Conversely, North America, Western Europe, East Asia, and South Asia display a heightened concentration level. The evolution of the digital deliverable services trade follows a path-dependent trajectory marked by spillover and Matthew effects.

This paper examines the impact of environmental jurisdiction strengthening on carbon emissions through an analysis of a quasi-natural experiment involving environmental court reform that commenced in 2007 by using data from 278 cities from 2003 to 2021 and a staggered DID model. The analysis revealed the following results: The establishment of environmental courts has resulted in a notable reduction in the carbon emissions intensity of the city. This occurrence is especially common in cities characterized by significant ecological challenges, substantial financial outlays, and comparatively low degrees of judicial contentment. Environmental courts have the potential to significantly enhance the enforcement of command-and-control environmental regulations. As a result, the impact on carbon emissions relative to GDP in urban areas is realized through three primary channels: the synergistic reduction of pollution and carbon emissions, enhancements in energy efficiency, and the encouragement of low-carbon green innovation.

Smart cities are an important driving force for the coordinated high-quality development of the environment and economy. Starting from factor agglomeration theory, financing constraints theory, and stakeholder theory, this study combines macro-level digital governance with micro-level corporate decision-making, and uses a difference-in-differences (DID) model to examine the impact of smart city construction (SCC) on corporate carbon emissions (CCE). The research results show that SCC effectively reduces the carbon emissions of enterprises. Mechanism analysis confirms that the carbon emission reduction (CER) path of SCC lies in the dual effects of resource allocation and target synergy, which can alleviate corporate financing constraints and improve ESG performance to achieve CER effect. Heterogeneity results indicate that the CER effect of SCC varies significantly depending on the stage, nature, location, and factor concentration of the corporate. This study enriches and expands the micro-climate governance research framework of SCC, providing practical support for enterprises to actively use digital governance to promote low-carbon transformation.

The rapid advancement of digital technologies has potential to transform the production models, operational efficiencies, and resource allocations of energy firms. Given this, we analyze how energy firms' digitalization influences green transformation using China's A-share listed companies data from 2008 to 2022. The results reveal a significant positive effect of digitalization on the green transformation of energy firms. Specifically, digitalization influences internal decision-making by reducing managerial myopia and enhancing executives' awareness of green practices. Digitalization optimizes the external environment by increasing media attention and alleviating financing constraints, thus boosting the green transformation. This study uncovers significant heterogeneity in the impact of digitalization across different types of energy firms. Notably, non-state-owned, growth-stage, and renewable energy firms show a particularly strong responsiveness to digital transformation. These findings offer relevant policy recommendations.

Sustainable Development Goals (SDGs) and the challenges in attaining these goals are always an eye-catching research theme for academics, politicians, and stakeholders around the world. Subjective well-being (SWB) is one of the central SDGs but is under-researched so far. Addressing this concern is crucial to maintaining societal welfare and promoting the realization of a sustainable future. Hence, this study explores the determinants of SWB with a particular focus on technological advancement and environmental damage. The paper examines SWB determinants in six South Asian nations from 2005 to 2022 using the cross-sectional augmented auto-regressive distributed lag model. The conclusions demonstrated that technology advancement improved SWB in the long term. Meanwhile, environmental deterioration has no appreciable short-term effects and harms SWB in the long term. This study adds to the field by examining how technological advancement and environmental deterioration explain SWB in South Asian economies. The study recommends policies that aim at improving innovations and reducing CO2 emissions.

The Sustainable Development Goals, championed by the United Nations, have elevated the importance of renewable energy development (RED) within China's energy landscape. As China strives to align its energy goals with these global objectives, it becomes imperative to delve into the intricate spatial dynamics of renewable energy. This study employs a modified gravity model and harness the power of social network analysis to scrutinize the landscape of RED in 30 Chinese provinces from 2012 to 2021. The findings reveal a captivating narrative of transformation. Over the years, we have witnessed shifts in the general trajectory of RED across China and nuanced variations within each province. The spatial network underpins RED's growth as a multifaceted tapestry, showing stability and adaptability. It paints a vivid picture of interconnected provinces with telltale signs of spatial spillover effects. Amid this intricate web emerges a core–edge pattern, signifying the ascent of core regions while the peripheries undergo a contraction. Nevertheless, beneath the surface, the demographics of network components play a significant role, contributing to the rich tapestry of RED variations across provinces. This spatial network thrives on the disparities in industrial policy, economic development, system adjacency, transportation infrastructure, and the diverse political systems across the Chinese provinces. In light of these compelling findings, our call to action is clear. Policymakers would reinforce their commitment to RED, encouraging regional collaboration and harmonizing policies to accelerate China's journey towards sustainable energy goals.

This study investigates the relationships between the resource curse, energy consumption, and the moderating role of digital governance within the context of South Asian countries. By employing a robust analytical framework, the study delves into how digital governance can potentially mitigate the adverse effects of the resource curse while influencing energy consumption patterns. It employs Method of Moment Quantile Regression (MMQR) on south asian countries panel from 2003 to 2022. The findings report detrimental impact of natural resources rent on economic growth, confirming resource curse hypothesis in low and high growth countries. Digital governance has a positive and significant impact at middle and higher quantiles. The joint influence of natural resources rent, and digital governance is associated with lower economic growth. It imply that digital governance doest not decreases the negative effect of natural resources rent on economic growth. Energy consumption contributes to higher economic growth from lower to higher quantiles. The interaction of digital governance and energy consumption has significant and positive impact on higher quantiles, suggesting that digital governance enhances energy consumption efficiency. These findings suggest valuable policy suggestions.

The extraction and utilization of mineral resources often lead to environmental pollution and resource depletion, highlighting the urgent need to improve green utilization efficiency. At the same time, Fintech, a fusion of financial services and technological innovation, transforms the traditional financial landscape and significantly affects natural resource markets. Given this, we examines the impact of Fintech on the mineral resources green utilization efficiency (Mrgue) across 30 provincial-level administrative regions in China from 2012 to 2021. The results show that Fintech significantly boosts Mrgue, particularly in the eastern regions of China. However, these effects are less pronounced in the central and western regions. The marginal impact of Fintech on Mrgue is notably positive and strengthens at higher quantiles. A significant improvement in Mrgue is observed only after Fintech surpasses a specific threshold. Lastly, Fintech promotes Mrgue by driving green technological innovation and optimizing the energy consumption structure. These findings offer insights for policymakers to understand Fintech's role better and leverage it to advance Mrgue.

The CAREC region, particularly Central Asian economies, traditionally rely on mineral resources. However, there is an increasing awareness of the significant potential and growing interest in financial technology, and supportive business regulations. Hence, this study investigates the pivotal role of fintech, natural resources, and economic freedom in shaping the economic growth trajectory of the CAREC region between 2000 and 2020. So far, no publicly available fintech index exists for this region. This study constructed a comprehensive index considering digitalization and financial development to analyse fintech readiness. It applies fixed and random effect models to examine the relationships between these variables. To account for non-linearity stemming from structural shifts and global financial shocks, it employs method of moment quantile regression (MMQR). The findings from the fixed effect models substantiate the positive impact of fintech, economic freedom, and natural resources on economic growth, albeit with varying degrees of marginal influence. A one-percentage-point improvement in fintech, economic freedom, and natural resources yields economic growth boosts by 0.102%, 0.903%, and 0.176%, respectively. Parallel results emerge from the random effect models with differing magnitudes and parameter significance levels. The MMQR outcomes consistently highlight the potency of fintech and economic freedom, with their impact being more pronounced at higher and lower quantiles of economic growth, respectively. This suggests that countries in the CAREC region experiencing higher growth levels stand to gain more from Fintech adoption, while those with lower growth levels can reap greater benefits from enhanced economic freedom. This study sheds light on the multifaceted interplay of these factors, offering valuable insights for policymakers and stakeholders in the CAREC region.

Small- and medium-sized enterprises (SMEs) in China have been hit hard by the coronavirus (COVID-19) outbreak, which has jeopardized their going out of business altogether. As a result, this research will shed light on the long-term impacts of COVID-19 lockdown on small businesses worldwide. The information was gathered through a survey questionnaire that 313 people completed. Analyzing the model was accomplished through the use of SEM in this investigation. Management and staff at SMEs worldwide provided the study's data sources. Research shows that COVID-19 has a significantly bad influence on profitability, operational, economic, and access to finance. In the study's findings, outside funding aids have played an important role in SMEs' skill to persist and succeed through technological novelty than in their real output. SME businesses, administrations, and policymakers need to understand the implications of this study's results.

Electricity price distortion (DIS) can significantly affect industrial green transformation (IGT), influencing the pace and direction of sustainable economic growth. Understanding this link is essential for crafting effective green energy policies. Initially, this study evaluates the direct and heterogeneous effects of DIS on IGT and then investigates the indirect influence channels using a sample of 30 Chinese provincial-level administrative regions from 2006 to 2019. Spatial analysis techniques (standard deviation ellipse and geographically and temporally weighted regression methods) are applied to explore the spatial and temporal dependence and non-stationary association between DIS and IGT. The outcomes suggest that DIS significantly reduces IGT in the eastern region through R&D input intensity and energy mix, while insignificant in the central and western regions. The adverse effect of DIS is more substantial at higher quantiles of IGT. The individual spatial heterogeneity characteristics reveal that the gravity centre of IGT is located in the southeast of the geometric centre of China, displays a southwest-northeast-southeast directional migration, and distributed at the junction of Henan and Hubei. Manifestly, the ellipse and azimuth of IGT vary significantly between 0.862°-32.854°. The IGT level steadily progresses from discrete to concentrated, reflected by the ellipse's long and short semi-axes. These regions are mainly concentrated in the eastern and northwestern areas, with the most significant inhibitory effects in Fujian, Anhui, Shaanxi, Zhejiang, and Yunnan. These findings offer valuable policy implications.

A complete understanding of the interplay between environmental regulations and fiscal decentralization for the realization of health outcomes is crucial for policy formulation and decision-making. The aim of the study is to investigate both policy variables’ separate and combined impacts using data on four BRICS economies from 2000 to 2020. The study has employed the novel method of moments quantile regression to quantify the effect. The findings of the study show (i) a significant impact of regulations on health outcomes in higher quantiles. Environmental regulations have a strong positive impact on all three-health proxies, that is, health expenditures, life expectancy, and the number of infant deaths. Total revenue and expenditure decentralization affect health outcomes positively, while tax revenue decentralization negatively impacts them, with the effect being stronger in the lower quantiles; (ii) the combined impact of decentralization and environmental regulations turned out to be negative and significant in our study; and (iii) all variables have unidirectional causality. However, with tax revenue decentralization, health expenditures, life expectancy, and infant deaths have bidirectional causality. This finding has a strong policy implication for the policymakers. Although both policies positively impact health indicators, their interaction leads to deteriorating health outcomes. From a policy point of view, it is suggested to strike a balance between regulations and fiscal decentralization to realize the full potential of this policy mix to get better health outcomes. This study adds to previous research by incorporating the interconnected impact of environmental regulations and fiscal decentralization on health outcomes in BRICS economies.

Due to the worsening issues related to resources and the environment, industrial green transformation (IGT) has emerged as an unavoidable solution in the present stage. Likewise, renewable energy technology innovation (RETI) is indispensable in driving the industrial development path towards green transformation. Initially, this study measures the current status of RETI and IGT and then applies fixed effects models and dynamic panel threshold models to analyze the underlying mechanism between RETI and IGT utilizing China’s regional data from 2006 to 2019. The results support a significant positive association between RETI and IGT. It also shows that China’s IGT is low with significant rough development characteristics, while RETI yields progressive trends. Besides, RETI significantly contributes to IGT in eastern and central areas of China, while western areas of China produce insignificant effects. Manifestly, RETI possesses a nonlinear effect on IGT, which implies that the positive effect of RETI on IGT gradually strengthens when R D investment intensity and industrial development level crosses a specific threshold value. Similar results are endorsed using alternative instruments, proxies, and estimators, offering reliable outcomes and recommendations.