The influence mechanism of resource sharing on tourism industry innovation

The development of the concept of "sharing" in the era of the digital economy has promoted significant changes in the innovation mechanism of the tourism industry. This article takes 31 provinces (municipalities and districts) in China as the research object, and collect four types of tourism statistics. Inspired by resource-based theory and dynamic capability theory, the article builds a research model of resource sharing for tourism industry innovation, proposes relevant hypotheses, discusses the influence mechanism of tourism industry innovation, and uses a fixed effects model and intermediary mechanism model for empirical test. The results show that the constructed model has good explanatory power and adaptability. Resource sharing can significantly drive tourism industry innovation, and the conclusion is robust. Tourism industry marketization, innovation ability, and competitiveness play an intermediary role in resource sharing to promote tourism industry innovation. The influence of resource sharing on tourism industry innovation has regional heterogeneity. The article aims to reveal the influence mechanism of resource sharing on tourism industry innovation under the digital economy, and the research conclusions can provide references for various provinces (municipalities and districts) to improve regional industrial innovation ability.

The relationship between PM10 and SO2 exposure and Covid-19 infection rates in Turkey using nomenclature of territorial units for statistics level 1 regions

The Covid-19 pandemic, which has been affecting the world since December 2019, has become one of the biggest problems of the 21st century. There are studies stating that the contagiousness of the Covid-19 pandemic, which is transmitted from person to person, increases more with environmental factors such as air pollution, and accordingly, there is an increase in the number of cases. In this study, a panel regression model to investigate the effect of air pollution concentrations such as PM10 and SO2 as environmental factors and population density on the monthly mean number of Covid-19 cases for 12 regions at the nomenclature of territorial units for statistics (NUTS) level 1 in Turkey between June 2020 and November 2020, and a linear regression model to investigate the effect at the regional level. we used. Based on the model results, we concluded that a small increase in air pollution indicators led to an increase in the number of Covid-19 cases in Turkey and its regions. It is very important to identify preventable environmental factors in order to prevent and minimize the effects of respiratory tract diseases and rapidly spreading pandemic diseases such as Covid-19. Accordingly, we can conclude that countries should take some measures, especially on air pollution, in order to develop public health and pandemic/disease management strategies and to reduce the risk of respiratory diseases.

Sub-regional variation in atmospheric and land variables regulates tea yield in the Dooars region of West Bengal, India

Climatic variables can have localized variations within a region and these localized climate patterns can have significant effect on production of climate-sensitive crops such as tea. Even though tea cultivation and industries significantly contribute to employment generation and foreign earnings of several South Asian nations including India, sub-regional differences in the effects of climatic and soil variables on tea yield have remained unexplored since past studies focused on a tea-producing region as a whole and did not account for local agro-climatic conditions. Here, using a garden-level panel dataset based on tea gardens of Dooars region, a prominent tea-producing region in India, we explored how sub-regional variations in climatic and land variables might differently affect tea yield within a tea-producing region. Our analysis showed that the Dooars region harboured significant spatial variability for different climatic (temperature, precipitation, surface solar radiation) and soil temperature variables. Using graph-based Louvain clustering of tea gardens, we identified four spatial sub-regions which varied in terms of topography, annual and seasonal distribution of climatic and land variables and tea yield. Our sub-region-specific panel regression analyses revealed differential effects of climatic and land variables on tea yield of different sub-regions. Finally, for different emission scenario, we also projected future (2025-2100) tea yield in each sub-region based on predictions of climatic variables from three GCMs (MIROC5, CCSM4 and CESM1(CAM5)). A large variation in future seasonal production changes was projected across sub-regions (-23.4-35.7% changes in premonsoon, -4.2-3.1% changes in monsoon and -10.9-10.7% changes in postmonsoon tea production, respectively).

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Maternal health, well-being, and employment transitions: A longitudinal comparison of partnered and single mothers in Germany

Balancing parenthood and employment can be challenging and distressing, particularly for single mothers. At the same time, transitioning to employment can improve the financial situations of single mothers and provide them with access to social networks, which can have beneficial effects on their health and well-being. Currently, however, it is not well understood whether the overall impact of employment on single mothers is positive or negative, and to what extent it differs from the impact of employment on partnered mothers. Building on the literature on work-family conflict, we investigate the differential effects of employment transitions on the health and well-being of single mothers and partnered mothers. Using longitudinal data from the German Socio-Economic Panel (1992-2016), we apply panel regression techniques that address the potential endogeneity of maternal employment, as well as the dynamic nature of the relationship between employment transitions and maternal health and well-being. We find that employment has a positive impact on single mothers, and that single mothers benefit from employment significantly more than partnered mothers. Surprisingly, income does not appear to be an important driver of these results. Overall, our findings suggest that employment plays a key role in the well-being of single mothers.

Impact of anti-dumping on global embodied air emissions: a complex network perspective

After the rise of trade protectionism, anti-dumping has become a common means of political and trade games between countries. Global supply chains move production emissions between countries or regions through trade. In the context of carbon neutrality, anti-dumping measures representing the right to trade may become a tool for the game of emission rights between countries. Therefore, it is very important to study the environmental effects of anti-dumping to cope with global climate change and promote national development. Taking a sample of 189 countries and regions from the EORA input-output table with a study period of 2000-2016, we use the complex network, multi-regional input-output and panel regression models to verify the impact of anti-dumping on air emission transfer by constructing an anti-dumping network and an embodied air emission network. The results show that the initiator of anti-dumping can use anti-dumping to realize the cross-border transfer of ecological costs, reduce the burden of emission reduction and save more on emission quota. Developing countries lacking the right to speak in trade will increase the export volume of commodities after being subjected to a large number of anti-dumping sanctions, thus paying higher ecological costs and consuming more emission quotas. From a global perspective, additional emissions from product production can further contribute to global climate change.

Spatiotemporal non-stationarity in green space and stress relationships: Recent evidence from South Korea

A growing body of literature has linked exposure to "green space" (vegetation-rich areas) and other forms of nature to mental health. Exposure-outcome associations at regional or national scales can overlook local associations that define how geographically distinct populations may experience nature differently. Large-scale results might downplay the importance of lived experiences and heterogeneity of human-nature relationships at local scales. The current study examines three types of vegetative cover and identifies how they are associated with perceived stress in South Korea during and before the COVID-19 pandemic. We find forest cover is consistently negatively associated with perceived stress at nationwide scales. In contrast, grass cover and the normalized difference vegetation index (NDVI) show mixed associations with perceived stress at nationwide scales. Models accounting for spatial and temporal variability demonstrate that associations of forest cover, grass cover, and NDVI with perceived stress varies across the country and the study's four-year time horizon. Local governments may need divergent urban greening strategies for health promotion that respond to their specific sociodemographic and pre-existing environmental conditions.

Drug overdoses, geographic trajectories, and the influence of built environment and neighborhood characteristics

Much research has analyzed the spatial patterns of drug overdose events and identified features of the environment associated with heightened overdose levels. Generally absent from the literature are studies that analyze how unique trajectories of overdoses vary over time. We address this gap in the literature through an analysis of drug overdoses occurring in Passaic County, New Jersey from 2015 through 2019. A group-based trajectory analysis classifies block groups according to their overdose trends. A mixed-effects panel negative binomial regression model then examines the built environment and neighborhood characteristics associated with overall overdose levels. Results indicate that Passaic County block groups can be classified across three groups based upon their overdose levels over the study period: low and stable, low with moderate increase, and elevated and increasing. While the largest effects were observed for concentrated disadvantage in the regression analysis, most variables positively associated with overdose levels were built environment measures.

The energy transition in Europe-a solution for net zero carbon?

Climate change has generated intense concerns from public authorities and international institutions with regard to shaping the behavior of companies, consumers, investors, and other stakeholders so as to manage this challenge as efficiently as possible. In order to address the climate emergency in the post-pandemic era, recovery plans need to trigger decarbonization, and a green transition, including specific investments and providing a more adaptive structure of the sources of energy in different regions, able to meet the need for a systemic shift towards a more sustainable economy that works for both people and the planet. The main measurable effect of energy production and consumption is by far represented by carbon emissions. In the present paper, we aim to identify the statistical significance of several factors influencing the carbon dioxide emission per capita in the European countries-level of economic development, level of globalization, trade openness, and the intensity of energy transition measured by the share of renewable energy in total energy consumption. The results show an increased interest of the experts in energy consumption model shift through green energy increased share, with relatively high differences among the 42 European countries analyzed. The analysis was conducted for the period 1990-2018 and policy differences depending on variables (GDP/capita, globalization index, trade openness, and renewable energy share in total energy consumption) were identified. The results showed that the carbon dioxide emission per capita evaluation designed model is representative of the European countries. The fact that the targets set by European non-EU member states for reducing CO2 emissions are lower than for the EU is influencing the dynamics of the energy transition, with implications for the size and destination of funds to finance the development of renewable energy.

The association between health expenditure, institutions, and economic growth in MENA countries

Background: Expenditure on health is vital in the development of a country. Furthermore, the current COVID-19 pandemic emphasises the importance of health investments in maintaining a healthier economy worldwide. A substantial amount of empirical research on the relationship between health expenditure and economic growth yields conflicting results. The study intends to investigate the relationship between health spending and economic growth and institutions’ role in causing health spending to promote growth.

Methods: The study uses longitudinal data to examine the relationship between health spending and economic growth in seven MENA countries from 2000 to 2017. The study uses the Phillips Perron (PP) Fisher chi-square stationarity test, indicating that the data series is not stationary. Following this, we used the Pedroni test for cointegration, and the results show long-run relationships between the variables. Next, Granger causality determines the direction of causality. Finally, panel data methods of panel ordinary least squares (Panel OLS), fully modified OLS (FMOLS), and dynamic OLS (DLOS) supplement the findings.

Results: The Pedroni cointegration test (P value<0.0001) indicates that the variables have a long-run cointegrating relationship. On the other hand, the Granger causality test finds no causal relationships between health spending and economic growth. Furthermore, the panel data models show that expenditure on health does not directly contribute to higher economic growth in MENA countries.

Conclusion: The findings of this study indicate that health spending does not lead to increased economic growth; this could be due to poor institutional quality. However, for health spending to positively impact economic growth, these investments in health care must be supplemented by other factors, particularly institutions.

Assessing the influence of urban greenness and green stormwater infrastructure on hydrology from satellite remote sensing

Green stormwater infrastructure (GSI), which includes features like rain gardens, constructed wetlands, or urban tree canopy, is now widely recognized as a means to reduce urban runoff impacts and meet municipal water quality permit requirements. Many co-benefits of GSI are related to increased vegetative cover, which can be measured with satellite imagery via spectral indices such as the Normalized Difference Vegetation Index (NDVI). In urban landscapes, there remain critical gaps in understanding how urban greenness and GSI influence hydrology. Here, we quantify these relationships to assess the feasibility of tracking the effectiveness of urban greening for improving downstream hydrologic conditions. We combined hydrologic data from the United States Geological Survey (USGS) gauges with an NDVI time series (1985-2019) derived from Landsat satellite imagery, and synthesis of GSI implementation data from a set of 372 urbanized watersheds across the United States. We used a multivariate panel modeling approach to account for spatial and time varying factors (rainfall, temperature, urban cover expansion) in an effort to isolate the relationships of interest. After accounting for expansion of urban boundaries, only 32 watersheds (9%) showed significant greenness trends, a majority of which were reductions. Urban greenness had significant influences on downstream flow responses, so that on average, a 10% greenness increase showed a corresponding reduction of total flow (-3.8%), flow variance (-7.7%), peak flows (-4.7%), high flows (-7.6%), flashiness (-2.2%), and high flow frequency (-1.5%); and a corresponding increase in baseflow (4.3%). For a subset of these watersheds for which GSI data were available (n = 48), the level of GSI implementation showed a significant, but weak influence on urban greenness with a 20% increase in BMP density corresponding to a greenness increase of 0.9%. The study results may support valuation and verification of GSI co-benefits in urbanized landscapes at the watershed scale.

How does Covid-19 affect global equity markets?

This study applies OLS, panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019 (Covid-19) outbreak on the global equity markets during the early stage of the pandemic. We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1% significance level. Furthermore, the pandemic has a more significant impact on the European countries than on the East Asian economies. The results have three main implications. Firstly, policy makers should react fast to mitigate the impact of a crisis. Secondly, investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly. Furthermore, the Covid-19 outbreak results in a shift of power from the west to the east.

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.

Differential diffusion of pharmaceutical innovations in a mixed market middle - income economy

Background

Policymakers are faced with the challenge of balancing patient’s access for effective and affordable medicines to sustain the rising healthcare costs. In a mixed healthcare market such as Malaysia, coverage decisions of new medicines are different: public funded health system has a formulary listing process whereas for private sector, which is a market-based economy, depends on patient’s willingness to pay and insurance coverage. There is little overlap between public and private healthcare service delivery with access to new innovative medicines, as differentiated by sources of funding. The objectives of this study were to examine the diffusion of New Chemical Entities (NCEs) into the public and private healthcare market between 2010 and 2014, and determine the factors explaining the diffusion.

Methods

We matched medicines from the product registration database by medicine formulation to medicines in IQVIA National Pharmaceutical Audit database for each year. The price per Defined Daily Dose (DDD), market concentration and generic utilization share variables were calculated. A panel fixed effect model was performed to measure diffusion of NCEs for each year and test possible determinants of diffusion of NCEs for overall market and sector specifics.

Results

The utilization of NCEs was larger in the private sector compared to the public sector but the speed of diffusion over time was higher in the public sector. Price per DDD was negatively associated with diffusion of NCEs, while generic utilization share was significantly regressive in the public sector. Market concentration was negatively associated with utilization of NCEs, however result tends to be mixed according to sector and Anatomical Therapeutic Chemical (ATC) category.

Conclusions

Understanding key aspects of sectoral variation in diffusion of NCEs are crucial to reduce the differences of access to new medicines within a country and ensure resources are used on cost effective treatments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06786-6.

Association between the total fertility rate and under-five child sex ratio in India: a panel study among districts of the major states

This study examined the relationship between the total fertility rate and under-five child sex ratio to understand the role of fertility in the phenomenon of missing girls in India. Using data from the last four decennial censuses for the fifteen major states of India and their districts, covering more than 90% of the population of India, the study showed that there was a major decline in the female to male child sex ratio from 1981 to 2011 in most of the major Indian states and their districts. The panel regression model showed that the total fertility rate was significantly associated with the under-five child sex ratio at the district level for the 30-year period from 1981 to 2011 in India, even after controlling for other factors and any other unobserved heterogeneity. This indicates that areas of India with the highest fertility had the higher female to male child sex ratio, while low-fertility districts had a more male-biased sex ratio.

Evidence that social-economic factors play an important role in drug overdose deaths

BACKGROUND

Drug overdose deaths in the United States increased from approximately 16,000 per year in 2001 to 41,000 per year in 2014. Although every US state witnessed an increase, the increases were much larger in some states than others. There was also variation as a function of race and ethnicity. Non-Hispanic Whites accounted for more than 80% of the deaths, and in some states their rates were about ten times greater per capita than Hispanic and Non-White rates. State and temporal differences provide an opportunity to evaluate explanations of what is driving drug overdose deaths. In this report, we evaluate the degree to which state level variation in opioid prescription rates and social-economic conditions explain state level variation in overdose death rates.

METHODS AND DATA

We used publicly available data from the Center for Disease Control (CDC), Bureau of Labor Statistics (BLS), Drug Enforcement Agency (DEA) and the Opportunity Insights project.

RESULTS

Legally prescribed opioids, social capital and work force participation accounted for 53-69% of the between-state variation in overdose deaths in Non-Hispanic Whites. Prescriptions and the two social economic measures accounted for about the same amounts of unique variation, but shared variation among the three independent variables was the strongest predictor of overdose deaths. Panel regression results of the year-to-year changes in overdose deaths were similar. However, the pattern of correlations for Hispanics and Non-Whites was quite different. Neither opioid prescriptions nor social capital were significant predictors of overdose deaths in the between-state and between-year Hispanic and Non-White regression analyses.

CONCLUSIONS

Common variation in opioid prescriptions rates, social capital, and work force participation proved the strongest predictor of drug overdose deaths in Non-Hispanic Whites. We discuss reasons why the same did not hold for the Hispanic/Non-White population.

The implications of high-speed railways on air passenger flows in China

The High-speed Railway (HSR) network in China is the largest in the world, competing intensively with airlines for inter-city travel. Panel data from 2007 to 2013 for 138 routes with HSR-air competition were used to identify the ex-post impacts of the entry of HSR services, the duration of operating HSR services since entry, and the specific impacts of HSR transportation variables such as travel time, frequency, and ticket fares on air passenger flows in China. The findings show that the entry of new HSR services in general leads to a 27% reduction in air travel demand. After two years of operating HSR services, however, the negative impact of HSR services on air passenger flows tends to further increase. The variations of the frequency in the temporal dimension and the travel time in the spatial dimension significantly affect air passenger flows. Neither in the temporal nor spatial dimensions are HSR fares strongly related to air passenger flows in China, due to the government regulation of HSR ticket prices during the period of analysis. The impacts of different transportation variables found in this paper are valuable to consider by operational HSR companies in terms of scheduling and planning of new routes to increase their competitiveness relative to airlines.

The impact of the dimensions of environmental performance on firm performance in travel and tourism industry

This study investigates the impact of the aggregate and individual dimensions of environmental performance (EP) on financial performance (FP), based on a dataset covering the travel and tourism industry (airlines, casinos, hotels, and restaurants) across different economic regions over the period 2003-2014. The results reveal that EP positively affects the FP in the hotel industry when aggregate EP is used. When individual dimensions of EP are considered, resource reduction is found to positively (negatively) affect the performance in the hotel (airline) industry, while product innovation positively affects the performance in the restaurant industry. Hence, the trade-off effect seems to be dominant in the airline industry, and the 'heterogeneous resources and reputation-building' hypothesis is evident in both the hotel and restaurant industries. In addition, in general, the findings support the positive moderating effect of slack resources on the relationship between the individual dimensions of EP and FP in the travel and tourism industry, and, hence, are supportive of the slack resources hypothesis. These effects, however, vary depending on the travel and tourism industry under investigation.

The economy, tourism growth and corporate performance in the Taiwanese hotel industry

This study investigates the impact of economy and tourism growth on the corporate performance of tourist hotels in Taiwan. The indicators of corporate performance under consideration are occupancy rate (OPR), return on assets (ROA), return on equity (ROE), stock return, and the overall financial performance measured by a comprehensive score (a combined measure of asset management, profitability, short-term solvency or liquidity and long-term solvency based on factor analysis). The effects of changes in the state of economy (real GDP growth rate, Δ GDP ) and tourism growth (growth rate of total foreign tourist arrivals, Δ TA ) on the corporate performance of tourist hotels are then examined via panel regression tests. Test results show that both Δ GDP and Δ TA are significant explanatory factors of OPR, but only Δ TA can strongly explain ROA and ROE. However, neither Δ GDP nor Δ TA have a significant influence on hotel stock performance. Further, the economic factor ( Δ GDP ) is slightly more crucial than the industry factor ( Δ TA ) in describing the overall financial performance in the Taiwanese hotel industry. Empirical findings offer valuable information for government tourism policymakers and tourist hotel owners and managers.