What drives China's healthcare expenditure? A theoretical and empirical study of determinants and trends.

As economic development advances, there is an increasing focus on improving health conditions, making healthcare expenditure a critical issue worldwide. In China, healthcare spending has shown a marked upward trend, highlighting the importance of understanding its underlying determinants to guide effective policy-making. This study introduces the application of an SV-TVP-FAVAR model to examine the drivers of healthcare expenditure in China from 2007 to 2022. The analysis reveals that economic factors, demographic composition, and policy interventions significantly influence healthcare spending dynamics. Economic growth is strongly linked to increased healthcare expenditure, with economic factors having a particularly pronounced impact during periods of prosperity. Although an aging population drives greater demand for healthcare, the growth rate of healthcare spending has not kept pace with demographic aging, especially following China's economic slowdown. Policy variables present a dual-edged impact: while increased fiscal outlays contribute to budget deficits, limiting the fiscal space for healthcare investment, government emphasis on scientific and technological progress tends to enhance healthcare spending, indicating a synergistic relationship between these areas. Furthermore, the study identifies a prolonged impact of the COVID-19 pandemic on healthcare expenditure, which continues to interact with other driving factors over an extended period. The empirical findings from this research provide crucial evidence to support the development of informed healthcare policies.

Spatio-temporal coordinated development pattern evolution and driving factors of regional population and green economy: Evidence from Shandong Province in China.

The study of spatio-temporal evolution characteristics and factors affecting the coordinated development of population and green economy (CD_PGE) in Shandong province, China, has significant decision-making implications for promoting high-quality and sustainable regional development. Based on 2001 to 2020 panel data for each city and economic zone in Shandong province, this paper constructs an evaluation model for the CD_PGE systems. Using growth elasticity models, geographic concentration models, kernel density estimation models, spatial autocorrelation, analysis of population and regional green economy development in Shandong Province from the perspective of spatial agglomeration coupling, spatial and temporal coupling coordination patterns, and evolutionary characteristics. In addition, we use the fixed effect models and panel quantile models to empirically test the effects of coordinated demographic and green economy development. The results show that: (1) In terms of demo-graphic and economic development characteristics, Shandong's demographic and green economy development trends are good, but there are still many challenges. (2) According to the time series evolution and spatial distribution characteristics, the degree of CD_PGE in Shandong Province is on the rise, and the level of spatial distribution is distinct. (3) From the spatio-temporal dynamical grid evolution of the degree of CD_PGE, the CD_PGE is characterized by significant spatial clustering, but with large regional differences. (4) From an impact factor perspective, both market mechanisms and government intervention have a significant impact on the degree of CD_PGE, but the direction and extent of the impact vary.

From global to national: The role of urban agglomerations in China's new development paradigm.

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes.

Differences in tourism economic development and its influencing factors among three major city clusters along the middle reaches of the Yangtze River.

An in-depth study of the mechanisms governing the generation, evolution, and regulation of differences in tourism economics holds significant value for the rational utilization of tourism resources and the promotion of synergistic tourism economic development. This study utilizes mathematical statistical analysis and GIS spatial analysis to construct a single indicator measure and a comprehensive indicator measure to analyze tourism-related data in the research area from 2004 to 2019. The main factors influencing the spatial and temporal differences in the tourism economy are analyzed using two methods, namely, multiple linear regression and geodetector. The temporal evolution, overall differences and differences within each city group fluctuate downwards, while the differences between groups fluctuate upwards. Domestic tourism economic differences contribute to over 90% of the overall tourism economic differences. Spatial divergence, the proportion of the tourism economy accounted for by spatial differences is obvious, the comprehensive level of the tourism economy can be divided into five levels. The dominant factors in the formation of the pattern of spatial and temporal differences in the tourism economy are the conditions of tourism resources based on class-A tourist attractions and the level of tourism industry and services based on star hotels and travel agencies. This study addresses the regional imbalance of tourism economic development in city clusters and with the intent of promoting balanced and high-quality development of regional tourism economies.

The impact of technological innovation on the green digital economy and development strategies.

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

Ending extreme poverty has a negligible impact on global greenhouse gas emissions.

Growing consumption is both necessary to end extreme poverty1and one of the main drivers of greenhouse gas emissions2, creating a potential tension between alleviating poverty and limiting global warming. Most poverty reduction has historically occurred because of economic growth3-6, which means that reducing poverty entails increasing not only the consumption of people living in poverty but also the consumption of people with a higher income. Here we estimate the emissions associated with the economic growth needed to alleviate extreme poverty using the international poverty line of US $2.15 per day (ref. 7). Even with historical energy- and carbon-intensity patterns, the global emissions increase associated with alleviating extreme poverty is modest, at 2.37 gigatonnes of carbon dioxide equivalent per year or 4.9% of 2019 global emissions. Lower inequality, higher energy efficiency and decarbonization of energy can ease this tension further: assuming the best historical performance, the emissions for poverty alleviation in 2050 will be reduced by 90%. More ambitious poverty lines require more economic growth in more countries, which leads to notably higher emissions. The challenge to align the development and climate objectives of the world is not in reconciling extreme poverty alleviation with climate objectives but in providing sustainable middle-income standards of living.