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.

Evaluation of tourism competitiveness and mechanisms of spatial differentiation in Xinjiang, China.

Evaluation of tourism competitiveness is useful for measuring the level of regional tourism development. It is of great importance to understand the advantages and disadvantages of tourism development correctly and formulate corresponding development strategies. To investigate tourism competitiveness, this paper established an evaluation index system, including tourism development competitiveness, tourism resource competitiveness, and tourism-support competitiveness, for 14 prefectures and cities in Xinjiang in China. The characteristics and laws of spatial differentiation were analyzed. Factor analysis was applied to examine the spatial differentiation of regional tourism competitiveness. The results showed an obvious spatial differentiation in tourism competitiveness among the 14 prefectures and cities. In terms of development competitiveness, Yili and Urumqi constituted the spatial center, followed by Changji, Altay, and Ba Prefecture. As the provincial capital, Urumqi has political, economic, cultural, transportation, and geographic advantages, but its competitiveness is not prominent in terms of monopoly and efficiency. In terms of resource competitiveness, Yili is the core attraction, while Urumqi, Kashgar, Altay, and Ba Prefecture are dominant attractions. With respect to supporting competitiveness, Bo Prefecture has high value, followed by Urumqi City and Aksu. Hetian and Ke Prefecture have the lowest values. The comprehensive competitiveness of tourism is centered on Yili. Urumqi and Bo Prefecture are subcenters, and Changji, Altay, Ba Prefecture, Aksu, and Kashgar are characterized as multi-polar competition areas. Using the KMO and Bartlett's sphericity tests, the cumulative contribution variance of the eigenvalues of the eight factors extracted by the maximum variance rotation method was found to be 92.714%. Socio-economic conditions, tourism resources, infrastructure construction, regional cultural influence, ecological environment carrying capacity, tertiary industry development, tourism service level, and living security system are the main driving factors affecting the spatial differentiation of tourism competitiveness in Xinjiang. Analyzing the spatial evolution characteristics and the driving factors of the regional tourism competitiveness in Xinjiang, this paper seeks to promote the optimal allocation of tourism production factors in the macro regional system, and provide theoretical guidance and an empirical basis for the comprehensive and harmonic development of regional tourism.

Study on temporal and spatial differentiation of biocapacity in Shenyang from a multi-scale perspective.

Biocapacity of a region exhibits spatial differences owing to the limitations of regional scale and natural conditions. Based on the multi-scale perspective, we comprehensively studied and analyzed the temporal and spatial differences of the biocapacity of a region in an attempt to establish the groundwork for optimizing urban development and its utilization framework. By adopting the ecological footprint model along with multi-scale difference evaluation method, the municipal and county scales are incorporated into a unified analysis framework in this paper, thereby facilitating the exploration of the temporal and spatial differences in the biocapacity of Shenyang-a city in China-from 2005 to 2019. The results demonstrated that: 1) At the municipal scale, the biocapacity per capita fluctuated between 1.35 hm2/person and 2.22 hm2/person. It revealed an "up-down-up" trend, which appeared consistent with the Kuznets cycle; at the county scale, the biocapacity depicted spatial differences, while those of downtown and surrounding districts/counties developed a two-level ascending hierarchical structure. 2) The time series of footprint size and depth first ascended and then declined, and can be classified into four types: closed type, inverted U-type, S-type, and M-type. Among them, S-type and M-type have the phenomenon of over-utilizing the stock capital. 3) For a long time, the regional difference of biocapacity has mostly dwelt on two scales with an evident scale effect, and the biocapacity of Liaozhong District was the worst.

Assessment and influencing factors analysis of economic system vulnerability of the Belt and Road Initiative countries.

Economic vulnerability is an important indicator to measure regional coordination, health and stability. Despite the importance of vulnerabilities, this is the first study that presents 26 indicators selected from the dimensions of the domestic economic system, external economic system and financial system in the Belt and Road Initiative (BRI) countries. A quantitative analysis is conducted to analyze the characteristics of spatial heterogeneity of vulnerability of the economic subsystems and the comprehensive economic system of the BRI countries and the main influencing factors of the comprehensive economic system vulnerability (CESV) are identified based on obstacle degree model. The results show that the CESV of the East Asia, South Asia and ASEAN countries are lower than that of the Middle Eastern Europe, Central Asia and West Asia countries. The CESV of the BRI countries are generally in the middle level and the average vulnerability index of highly vulnerable countries is twice as much as that of lowly vulnerable countries. In addition, in terms of the vulnerability of the three subsystems, the spatial distribution of vulnerability of the domestic economic system (DESV) and financial system (FSV) is basically consistent with the spatial distribution pattern of CESV, both of which are low in East Asia and South Asia and high in West Asia and Central Asia. While, the vulnerability of external economic system (EESV) shows a different spatial pattern, with vulnerability of West Asia, Central Asia and ASEAN higher than that of East Asia and South Asia. The main obstacle factors influencing the CESV of BRI countries include GDP growth rate, saving ratio, ratio of bank capital to assets, service industry level, industrialization level and loan rate. Therefore, the key way to maintain the stability and mitigate the vulnerability of the economic system of BRI countries is to focus on the macroeconomic development and operation, stimulate the economy and market vitality, promote the development of industries, especially the service and secondary industries, and optimize the economic structure, banking system and financial system.

The relationship between urologic cancer outcomes and national Human Development Index: trend in recent years.

OBJECTIVES: To describe the influence of the socioeconomic development on worldwide age-standardized incidence and mortality rates, as well as mortality-to-incidence ratio (MIR) and 5-year net survival of urologic cancer patients in recent years. METHODS: The Human Development Index (HDI) values were obtained from the United Nations Development Programme, data on age-standardized incidence/mortality rates of prostate, bladder and kidney cancer were retrieved from the GLOBOCAN database, 5-year net survival was provided by the CONCORD-3 program. We then evaluated the association between incidence/MIR/survival and HDI, with a focus on geographic variability as well as temporal patterns during the last 6 years. RESULTS: Urologic cancer incidence rates were positively correlated with HDIs, and MIRs were negatively correlated with HDIs. Prostate cancer survival also correlated positively with HDIs, solidly confirming the interrelation among cancer indicators and socioeconomic factors. Most countries experienced incidence decline over the most recent 6 years, and a substantial reduction in MIR was observed. Survival rates of prostate cancer have simultaneously improved. CONCLUSION: Development has a prominent influence on urologic cancer outcomes. HDI values are significantly correlated with cancer incidence, MIR and survival rates. HDI values have risen along with increased incidence and improved outcomes of urologic caner in recent years.

Universities' Scientific and Technological Transformation in China: Its Efficiency and Influencing Factors in the Yangtze River Economic Belt.

Universities are important sources of knowledge and key members of the regional innovation system. The key problem in Chinese universities is the low efficiency of the scientific and technological (S&T) transformation, which limits the promotion of regional innovation and economic development. This article proposes the three-stage efficiency analytical framework, which regards it as a complex and interactive process. Avoiding the problem of considering the input and output of university S&T transformation as a "black box" and neglecting the links among different transformation stages. The super efficiency network SBM model is applied to the heterogeneous region of the Yangtze River Economic Belt. Empirical research proves that university S&T transformation has not been effectively improved and the scientific resources invested in universities have not been efficiently utilized in recent years. Generally, Despite the correlation between regional economy and transformation efficiency, the exclusive increase in resources is not enough. Regional openness and the quality of research talents are key factors for the application of technological innovation and technology marketization. Universities should not only pursue the number of research outputs but pay more attention to high-quality knowledge production to overcome difficulties in research achievements transformation.

Prepare developed democracies for long-run economic slowdowns.

Developed democracies proliferated over the past two centuries during an unprecedented era of economic growth, which may be ending. Macroeconomic forecasts predict slowing growth throughout the twenty-first century for structural reasons such as ageing populations, shifts from goods to services, slowing innovation, and debt. Long-run effects of COVID-19 and climate change could further slow growth. Some sustainability scientists assert that slower growth, stagnation or de-growth is an environmental imperative, especially in developed countries. Whether slow growth is inevitable or planned, we argue that developed democracies should prepare for additional fiscal and social stress, some of which is already apparent. We call for a 'guided civic revival', including government and civic efforts aimed at reducing inequality, socially integrating diverse populations and building shared identities, increasing economic opportunity for youth, improving return on investment in taxation and public spending, strengthening formal democratic institutions and investing to improve non-economic drivers of subjective well-being.

Study on the effect of digital economy on high-quality economic development in China.

At present, the digital economy, which takes information technology and data as the key elements, is booming and has become an important force in promoting the economic growth of various countries. In order to explore the current dynamic trend of China's digital economy development and the impact of the digital economy on the high-quality economic development, this paper measures the digital economic development index of 30 cities in China from the three dimensions of digital infrastructure, digital industry, and digital integration, uses panel data of 30 cities in China from 2015 to 2019 to construct an econometric model for empirical analysis, and verifies the mediating effect of technological progress between the digital economy and high-quality economic development. The results show that (1) The development level of China's digital economy is increasing year by year, that the growth of digital infrastructure is obvious, and that the development of the digital industry is relatively slow. (2) Digital infrastructure, digital industry and digital integration all have significant positive effects on regional total factor productivity, and the influence coefficients are 0.2452, 0.0773 and 0.3458 respectively. (3) Regarding the transmission mechanism from the digital economy to the high-quality economic development, the study finds that the mediating effect of technological progress is 0.1527, of which the mediating effect of technological progress in the eastern, northeast, central and western regions is 1.70%, 9.25%, 28.89% and 21.22% respectively. (4) From the perspective of spatial distribution, the development level of the digital economy in the eastern region is much higher than that in other non-eastern regions, and the development of digital economy in the eastern region has a higher marginal contribution rate to the improvement of the total factor productivity. This study can provide a theoretical basis and practical support for the government to formulate policies for the development of the digital economy.

Temporal and spatial evolution and obstacle diagnosis of resource and environment carrying capacity in the Loess Plateau.

Natural resources are scarce in the Loess Plateau, and the ecological environment is fragile. Sustainable development requires special attention to resource and environmental carrying capacity (RECC). This study selected 24 representative cities in five natural areas of the Loess Plateau; used the entropy-weight-based TOPSIS method to evaluate and analyze the RECC of each city and region from 2013 to 2018; established a diagnosis model to identify the obstacle factors restricting the improvement of RECC; and constructed the theoretical framework of the RECC system mechanism. The results show that the RECC of the Loess Plateau is increasing in general but is relatively small. The environmental and social subsystems have the highest and lowest carrying capacities, respectively. There is an evident contradiction between economic development and the environment. Population density, investment in technological innovation, per capita sown area, and per capita water resources are the main obstacles affecting the improvement of RECC in the Loess Plateau. Such evaluations and diagnoses can support ecological civilization and sustainable development.

Relationships between urbanization and CO2 emissions in China: An empirical analysis of population migration.

China's announcement of its goal of carbon neutrality has increased the practical significance of research on carbon dioxide (CO2) emissions that result from urbanization. With a comprehensive consideration of population migration in China, this study examines the impact of urbanization on CO2 emissions based on provincial panel data from 2000 to 2012. Two indicators (resident population and household registration population) are used to measure urbanization rate. The results reveal that the impact of urbanization on CO2 emissions in China is closely correlated with the structure of urban resident population and interregional population migration. The estimation results are still robust by using generalized method of moments (GMM) estimator and two-stage least squares (2SLS) estimator. The proportion of temporary residents is introduced as a proxy variable for population migration. The panel threshold model regression results show that the proportion of temporary residents has a marginal effect on the relationship between urbanization and CO2 emissions. In regions with a higher proportion of temporary residents, the positive effects of resident population urbanization on CO2 emissions tend to be weaker. These findings are consistent with the theories of ecological modernization and urban environmental transition. This paper makes suggestions on China's urbanization development model and countermeasures are proposed to minimize the CO2 emissions caused by urbanization.

An evaluation of coupling coordination between sports industry and health service industry in China.

Depending on the strategy of "Healthy China", more and more people pay attention to health issues. The integration and development of sports industry and health service industry is an inevitable outcome of industrial transformation and upgrading and healthy life in the new era. Through constructing the evaluation index system of the coupling and coordination development degree between sports industry and health service industry, using entropy evaluation method and coupling and coordination degree model, this paper explore the comprehensive level and coupling and coordination development status of sports industry and health service industry in thirty-one provinces, municipality cities and autonomous regions of China from 2013 to 2017. The results of this paper show that the comprehensive China's sports industry and health service industry both present an incremental development trend year by year, and are characterized by the distribution of "high in the east and low in the west" in space. The government's policy support provides superior industrial supporting conditions for the development of sports industry. However, it is not conducive to the promotion of industrial economic benefits. In the health service industry, the rapid development of health insurance is beneficial to the integration of industrial resources and the perfection of industrial chain. Whereas as the core content of health service industry, health service has greater space for development; the coupling and coordination degree between the two industries rises from mild maladjustment to basic coordination, which is characterized by the distribution of "high in the east and low in the west" in space; among provinces, with Beijing, the Yangtze River Delta and Guangdong as the three development center points, it shows the spatial evolutionary process from "dispersion-type plaques" to "gathering type scattered surfaces".

Research on the evolution and driving forces of the manufacturing industry during the "13th five-year plan" period in Jiangsu province of China based on natural language processing.

The development of China's manufacturing industry has received global attention. However, research on the distribution pattern, changes, and driving forces of the manufacturing industry has been limited by the accessibility of data. This study proposes a method for classifying based on natural language processing. A case study was conducted employing this method, hotspot detection and driving force analysis, wherein the driving forces industrial development during the "13th Five-Year plan" period in Jiangsu province were determined. The main conclusions of the empirical case study are as follows. 1) Through the acquisition of Amap's point-of-interest (POI, a special point location that commonly used in modern automotive navigation systems.) data, an industry type classification algorithm based on the natural language processing of POI names is proposed, with Jiangsu Province serving as an example. The empirical test shows that the accuracy was 95%, and the kappa coefficient was 0.872. 2) The seven types of manufacturing industries including the pulp and paper (PP) industry, metallurgical chemical (MC) industry, pharmaceutical manufacturing (PM) industry, machinery and electronics (ME) industry, wood furniture (WF) industry, textile clothing (TC) industry, and agricultural and food product processing (AF) industry are drawn through a 1 km× 1km projection grid. The evolution map of the spatial pattern and the density field hotspots are also drawn. 3) After analyzing the driving forces of the changes in the number of manufacturing industries mentioned above, we found that manufacturing base, distance from town, population, GDP per capita, distance from the railway station were the significant driving factors of changes in the manufacturing industries mentioned above. The results of this research can help guide the development of manufacturing industries, maximize the advantages of regional factors and conditions, and provide insight into how the spatial layout of the manufacturing industry could be optimized.

Spatiotemporal pattern and coordination relationship between urban residential land price and land use intensity in 31 provinces and cities in China.

The trend towards efficient and intensive use of land resources is an inevitable outcome of current social development. The rational matching of urban land prices and land use intensity has become an important factor under accelerating urbanization, and promotes the healthy development of the social economy. Using data on residential land price and on land use intensity for 31 provinces and cities in China, we employ the E-G cointegration test and quadrant map classification to determine the coordination relationship between land price and land use intensity. We then employ HR coordination to calculate the coordination degree of land price and land use intensity, and classify the coordination type accordingly. Our results are as follows. (1) The spatio-temporal distribution of urban land price shows high variability with multiple maxima, and follows a decreasing trend from the southeast coastal area to the northwest inland area and the northeast. (2) The overall land use intensity is at or above the middle level, and shows large spatial differences between provinces, but the agglomeration between provinces is increasing. (3) From the perspective of the relationship between urban land price and land use intensity at the inter-provincial scale, we find that the land price and land use intensity are well coordinated, and the number of provinces has been dynamically changing during different development periods. There is an east-west difference in the spatial distribution of land price and land use intensity coordination level. Different provinces and cities with the same coordination stage show differences in their land price and land use intensity level.

Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model.

This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.