Search Results (101)

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

The integration of financial technology expenditures and green total factor productivity (GTFP) constitutes a critical impetus for sustainable economic advancement. This study employs provincial panel data from China (2012–2020) and uses the SBM model with undesirable outputs, the PVAR model, moderation effect analysis, and threshold regression to investigate the underlying mechanisms and threshold effects of financial technology expenditure on GTFP. The results show that (1) financial technology expenditure has a significant promoting effect on the growth of GTFP, with a coefficient of 0.614 (p < 0.05), indicating the need for further increases in fiscal investment in science and technology; (2) the effect of financial technology expenditure on GTFP varies across the eastern, central, and western regions of China, with stronger effects observed in the eastern region, suggesting that the government should formulate differentiated financial technology expenditure policies on the basis of local conditions; and (3) that educational investment and industrial upgrading play strong moderating roles in the impact of financial technology expenditure on GTFP, with interaction term coefficients of 0.059 (p < 0.05) and 0.206 (p < 0.1), respectively. Threshold analysis further reveals that the positive effect strengthens significantly once educational investment surpasses a log value of 9.3674 and industrial upgrading exceeds a ratio of 0.0814. However, currently, China’s education investment and industrial structure upgrading are still insufficient, necessitating further increases in education investment and promoting the transformation and upgrading of the industrial structure. Full article

In the context of the new developmental philosophy, this study aimed to address the bottleneck of regional sustainable development; it constructs a three-system evaluation indicator system for Industrial Structure Optimization (ISO), Ecological Environment Management (EEM), and Socio-economic Development (SED), based on panel data from 20 cities in the Western Taiwan Straits Economic Zone between 2011 and 2023. To reveal how the synergistic development of the three subsystems in different domains can achieve sustainable development through their interactions and to analyze the dynamic patterns of the three subsystems, this study employed the panel vector autoregression (PVAR) model to examine the interactions between subsystems. Additionally, drawing on the framework of evolutionary economics, the study quantified the temporal evolution and spatial characteristics of the coupling coordination level among the three subsystems based on the results of the degree of coupling coordination model. The results indicate the following: (1) ISO shows a significant upward trend, EEM slightly declines, and SED experiences minor fluctuations before accelerating. (2) ISO, EEM, and SED exhibited self-reinforcing effects. (3) The degree of coupling, coordination, and coupling coordination all exhibit a trend of “fluctuating and increasing initially, followed by steady growth”. The spatial patterns of the degree of coupling, coordination, and coupling coordination have shifted from “decentralized” to “centralized”, with clear signs of synergistic development. (4) The difference in the degree of coupling coordination along the north–south direction remained the primary factor contributing to inter-regional disparities. Regions with the higher degrees of coupling coordination were concentrated in the southeastern coastal areas, while those with the lower degrees of coupling coordination appeared in the northeastern mountainous areas and southwestern coastal areas. (5) The spatial connection in the strength of the degree of coupling coordination has gradually increased, with notable intra-provincial connections and weakened inter-city connections across the province. The study’s results provided decision-making references for the construction of a sustainable development community. Full article

Understanding the spatiotemporal dynamics of resource–environment carrying capacity (RECC) is essential for balancing ecological protection and socioeconomic development in river basins. This study applied various methodologies, including Panel Vector Autoregression (PVAR), Geographically Temporally Weighted Regression (GTWR), and Random Forest, to analyze in the Yellow River Basin from 2011 to 2021. PVAR quantifies dynamic interactions among RECC subsystems (population, resources, environment, and economy), while Random Forest identifies nonlinear drivers, and GTWR captures spatiotemporal heterogeneity. Results show RECC performance has continually improved, while subsystem and regional differences have been observed. Downstream regions exhibit higher RECC due to advanced infrastructure, whereas upstream areas face ecological constraints. PVAR results reveal bidirectional relationship among population, resource and economy subsystems, with unidirectional environmental pressure from economic activities. In terms of influencing factors analysis, which are classified into three sections, including geography, socioeconomic, and technological innovation. The random forest model identified that the economic development level has higher importance. The GTWR results expanded the spatiotemporal heterogeneity analysis: socioeconomic factors show significant regional variation. These findings provide a transferable paradigm for complex human–environment system analysis, offering policy-responsive zoning strategies that balance SDG implementation with basin-specific ecological constraints. Full article

To further promote rural revitalization strategies and achieve common prosperity, it is necessary to clarify the relationships among public expenditure for agriculture, agricultural green total factor productivity (AGTFP), and the urban–rural income gap (URIG). On the basis of panel data for 30 provincial regions in China from 2012 to 2022, this study constructs a panel vector autoregression (PVAR) model and explores their mutual interaction and influence from both dynamic and static perspectives through the Granger causality test, impulse response analysis, and variance decomposition methods. The research results show that public expenditure on agriculture, AGTFP, and URIG exhibit significant self-reinforcing trends. There is a significant two-way interaction effect between public expenditure on agriculture and URIG, indicating that these factors promote and complement each other. In addition, both improving AGTFP and increasing public expenditure on agriculture can help narrow URIG, but the positive impact of AGTFP exhibits greater magnitude and sustainability. In conclusion, from a long-term perspective, to develop the rural economy and promote rural revitalization, it is necessary not only to increase public expenditure on agriculture continuously, but also to focus on enhancing AGTFP. Full article

Agroforestry (AF) effectively enhances ecological restoration and soil–water conservation (SWC), yet the relationship among soil and water conservation agroforestry (SWCAF) in karst soil, water loss (SWL) and rural eco-revitalization (RER) remains unclear, which may hinder the ecological restoration process around the world. This study aims to reveal whether SWCAF in karst areas improves RER through SWC benefits, ecosystem service (ES) enhancement and rural ecological environment quality (REEQ) improvement. We take Guizhou Province, the core area of the South China Karst (SCK), as the study area and 2010–2020 as the study period. By using the equivalent factor method, the remote sensing ecological index (RSEI) model, bivariate spatial autocorrelation and the panel vector autoregressive (PVAR) model, the study reveals SWCAF’s ecological benefits and its interaction mechanism with RER. Key findings reveal the following: (1) SWCAF reduced the area of SWL by 14.93% by converting cropland into forests. (2) The AF ecosystem service value (AFESV) increased by CNY 9.181 billion, and the forest-related AFESV increases represented 184% of the total AFESV, while REEQ showed an overall positive trend in the western SWC area. (3) The AFESV has an obvious synergistic effect with REEQ (r = 0.60) and obvious positive synergy with SWL (r = 0.69), and its spatial correlation increases over time. (4) The PVAR model verified that there is a bidirectional Granger causal relationship between the AFESV and RER, showing dynamic positive and negative alternating influences. This research study reveals that SWCAF drives RER through the dual path of SWL control and value-added ecological services, among which the forest ecosystem plays a core role. In the future, it is necessary to optimize the diversity of AF structures to avoid ecological service trade-offs. This research study provides a scientific basis for decision making and the ecological management of SWC in karst soils globally. Full article

Ensuring the synergistic advancement of agricultural pollution reduction and carbon emission mitigation, along with sustainable development, is crucial for achieving the ‘dual carbon’ target and modernizing agriculture. To ensure sustainable agricultural development, this study employs a coupling coordination model to explore the synergistic effects of pollution reduction and carbon emission mitigation in Henan Province, considering the agricultural carbon emissions (ACEs), agricultural non-point source pollution (ANP), and the gross value of agricultural output (GVAO) of 18 cities in Henan from 2010 to 2022 as endogenous variables. A panel vector autoregression (PVAR) model is utilized to analyze the interactive responses between agricultural pollution reduction and carbon emission mitigation and agricultural economic development. The results indicate that the degree of synergy between ACE and ANP in Henan Province has shown a trend towards higher values and a diminishing polarization phenomenon between 2010 and 2022, with most regions having degrees of synergy at higher levels. Furthermore, the interactive response relationships between agricultural pollution reduction and carbon emission mitigation and agricultural economic development reveals that the GVAO in Henan Province has a significant positive impact on both ACE and ANP, and that agricultural pollution reduction and carbon emission mitigation are constrained by agricultural economic development, with no significant bidirectional causal relationship observed overall and a lack of positive interaction in the long term. Finally, ACE, ANP, and GVAO in Henan Province exhibit a strong self-reinforcing mechanism, particularly ACE and GVAO, which show a pronounced self-growth trend. Overall, Henan Province should fully utilize the synergistic effects of agricultural pollution reduction and carbon emission mitigation to achieve coordinated progress in agricultural pollution reduction and carbon emission mitigation, as well as green and sustainable development of the agricultural economy. Full article

Digital technology drives global industrial transformation. The synchronized development of organizational digital transformation and innovation systems is pivotal in corporate strategy and governmental governance. The dynamic interaction mechanisms among digital economy, government innovation policy, and regional innovation capacity remain insufficiently explored. This study employs panel data from 15 prefecture-level cities within the Yangtze River Delta urban agglomeration, spanning the years 2012 to 2020, and uses the panel vector autoregression (PVAR) model to investigate the interrelationships among the digital economy, government innovation preferences (the government’s supportive attitude and policy inclination towards innovative activities in the fields of science and technology as well as economic development), and regional innovation capacity. This research emphasizes the impact of the digital economy on regional innovation capacity and the influence of government innovation preferences on regional innovation capacity. The findings indicate that both the digital economy and government innovation preferences significantly enhance technological and product innovation, with this effect being particularly pronounced in the initial stages but diminishing over time. The three dimensions of the digital economy exert varying effects on technological and product innovation. Specifically, digital application has the most substantial impact on technological innovation, whereas infrastructure has a more pronounced effect on product innovation. Overall, the influence of government innovation preferences on technological and product innovation is less significant than that of the digital economy. The intensity of government innovation preferences has a greater impact than does the structure of government innovation preferences; however, in the long term, the structure of government innovation preferences can exert a more stable and sustainable influence. This study offers policy implications for constructing an innovation ecosystem driven by the synergy between government and market forces, particularly in optimizing data governance systems and planning sustainable transformation pathways, which hold practical value. Full article

Green technological innovation, government policies, and the low-carbon transformation of the manufacturing industry are critical for promoting high-quality development in the Yangtze River Economic Belt. This study, based on input–output and total factor productivity theories, selects relevant variables and utilizes a PVAR model to analyze data from 11 provinces in the region from 2011 to 2023. The empirical results indicate that (1) green technological innovation and the low-carbon transformation of the manufacturing industry exhibit significant bidirectional causality, with the low-carbon transformation exerting a stronger positive impact on green innovation, underscoring the “demand–pull” effect. (2) Government policies provide initial impetus for both green innovation and low-carbon transformation but show signs of self-restriction and diminishing returns over time, reflecting a typical “policy lag–decay” pattern. (3) Variance decomposition highlights the dominant role of green technological innovation in driving long-term low-carbon transformation, while the direct impact of government policies remains limited, indicating that policy effectiveness is largely mediated through technological channels. These findings emphasize the importance of enhancing regional green innovation capacity, establishing dynamic policy feedback mechanisms, and fostering sustained technological advancement as key pathways to deepening the low-carbon transformation. Full article

This study examines the impact of Hofstede’s six cultural dimensions and institutional quality on financial development in the periods preceding and following the global financial crisis. The study analyzes data from 33 countries spanning 2001 to 2021 using a combination of OLS, two-stage GMM, and PVAR models and concludes that inflation and economic growth negatively, and exchange rate and institutional quality positively significantly enhance financial development. Countries characterized by low masculinity and uncertainty avoidance scores, alongside high individualism and indulgence scores, tend to exhibit greater financial development. The results also indicate that cultural factors ought to be regarded as dynamic modifiers of financial development. National culture and institutional quality have a consistent influence on financial development pre- as well as post-crisis periods. Policymakers must recognize the significance of both formal and informal institutions in fostering an environment that promotes financial development and growth. A strategic integration of diverse cultural identities and values will confer a competitive advantage to nations. The effective management of cultural diversity and openness is crucial for attracting new investment, fostering innovation, comprehending the needs and skills of the workforce, and promoting financial development. Full article

This study explores how adjustments in risk management can influence the future financial performance of 20 countries in the MENA (Middle East and North Africa) region. While the existing literature has explored risk factors in emerging economies, this research provides novel empirical evidence on how risk management practices influence long-term financial stability and growth, a dimension underexplored in the MENA context. Using a Panel Vector Autoregression (PVAR) model, we analyze data from 2005 to 2021 to quantify the dynamic relationship between risk mitigation strategies and key financial outcomes, accounting for regional volatility and cross-country heterogeneity. This methodology allows for the examination of the impact of risk management on future financial outcomes, considering both current uncertainties and strategic approaches to mitigating risks. The results reveal that robust forward-looking risk management practices significantly impact the future financial performance and resilience of the countries in the MENA region. Our findings highlight that a well-designed risk management strategy is crucial for averting financial crises and supporting long-term economic growth and sustainability of nations. This study contributes to the understanding of how strategic risk management can drive future economic and financial stability in the MENA region, providing unique insights into the role of forward-thinking risk practices in shaping national success. Full article

The development of tourism governance (TG) is influenced by the tourism economy (TE), and the development of TE is guaranteed by tourism governance. This study investigates the development levels of the tourism economy and tourism governance, as well as their interactive coordination in 31 Chinese provinces (including municipalities and autonomous regions) from 2012 to 2021. First, the vertical and horizontal differentiation method was employed to measure tourism economy and tourism governance development levels. Second, the Panel Vector Autoregression (PVAR) model was adopted to examine the Granger causality and the interactive effects between the tourism economy and tourism governance. Third, the coupled coordination model, kernel density estimation, and Markov chain model were combined to explore the degree of coordinated development and the spatio-temporal evolutionary trend of TE-TG. The findings reveal the following: (1) The development level of the tourism economy exhibits a fluctuating upward trend, with its spatial distribution pattern demonstrating a distinct coastal-to-inland decreasing gradient. Meanwhile, tourism governance shows a steady improvement trajectory marked by significant regional disparities. (2) A long-term equilibrium relationship has been established between the tourism economy and tourism governance, with bidirectional Granger causality observed between the two systems. (3) The coupled coordination between the tourism economy and tourism governance has progressively increased. However, the development level of tourism governance still lags behind that of the tourism economy. The eastern and central regions demonstrate significantly higher TE-TG coordination levels compared to the western and northeastern regions. Full article

This study focused on the dynamic relationships among industrial development, energy consumption, economic growth, and carbon emissions in China, with the goal of achieving long-term ecological sustainability. Using the Panel Vector Autoregressive (PVAR) model and Generalized Method of Moments (GMM) estimation, panel data from 30 Chinese provinces between 2017 and 2021 were analyzed. The impulse response analysis and variance decomposition demonstrated that industrial and economic subsystems significantly influenced carbon emissions, while the energy subsystem had a moderating effect. These results highlight a shift in China’s energy consumption structure, with industrial and economic activities driving carbon emissions, while energy consumption patterns slowed the increase in emissions. These findings have critical implications for understanding the interactions among industry, economy, energy, and carbon emissions. Full article

This study investigates the coupling and coordination between resilience and efficiency in promoting the sustainable development of tourism economies, using the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employs an integrated approach combining the improved CRITIC-Entropy method, super-efficiency SBM model, and coupling coordination degree model to measure the coupling coordination degree between tourism economic resilience and efficiency, examining their spatiotemporal evolution. Further, a PVAR model is used to explore the bidirectional dynamic relationship between resilience and efficiency. The findings indicate that the coupling coordination between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration has evolved from a “coordination transition stage (2011–2012)” to a “coordination development stage (2013–2020)”, showing a trend towards positive coordination. Spatial analysis reveals significant regional differences, with Beijing and Tianjin having higher coupling coordination levels than Hebei Province, demonstrating the radiating effect of core cities, while the overall level within Hebei’s cities still needs improvement. The study confirms a positive interaction between tourism economic resilience and efficiency, with both exhibiting self-enhancing mechanisms. This research highlights the importance of balancing resilience and efficiency for sustainable tourism economic development. It offers valuable insights for policymakers and regional planners to enhance the adaptability and competitiveness of tourism economies in response to external shocks, contributing to the long-term sustainability of the industry. Full article

Achieving the goals of carbon peak and carbon neutrality is crucial for the balance of global economic development with carbon emissions reduction and ecological environment protection, which are essential for the sustainability of human development. Digital inclusive finance (DIF), as an emerging force capable of promoting economic growth and technological innovation, plays a significant role in curbing carbon emissions. By using the panel data of 30 provinces in China from 2011 to 2021 and employing the panel vector autoregression (PVAR) model, this study empirically investigates the impact of DIF on total carbon emissions (TCE) and carbon emission intensity (CEI) from the perspective of technological innovation. The results show that DIF significantly reduces TCE and CEI and can further decrease TCE and CEI by promoting the level of technological innovation. The results of the impulse response function (IRF) reveal that technological innovation has a more significant and volatile impact on CEI compared to its effect on TCE. Moreover, heterogeneity analysis suggests that the impact of DIF on the reduction in carbon emissions is characterized by regional heterogeneity, with the impact of DIF on TCE in the central regions being the most pronounced, significantly influenced by the spillover effects from the eastern regions. Further research finds that the western regions exhibit a more significant impact of technological innovation levels on CEI compared to the eastern regions, with a discernible trend towards the convergence of inter-provincial disparities in CEI in the process of development. Full article