Is economic growth enough to propel rehabilitation expenditures? An empirical analysis of country panel data and policy implications.

PURPOSE: Rehabilitation is a set of services designed to increase functioning and improve wellbeing across the life course. Despite being a core part of Universal Health Coverage, rehabilitation services often receive limited public expenditure, especially in lower income countries. This leads to limited service availability and high out of pocket payments for populations in need of care. The purpose of this research was to assess the association between macroeconomic conditions and rehabilitation expenditures across low-, middle-, and high-income countries and to understand its implications for overall rehabilitation expenditure trajectory across countries. MATERIALS AND METHODS: We utilized a panel data set from the World Health Organization's Global Health Expenditure Database comprising the total rehabilitation expenditure for 88 countries from 2016 to 2018. Basic macroeconomic and population data served as control variables. Multiple regression models were implemented to measure the relationship between macroeconomic conditions and rehabilitation expenditures. We used four different model specifications to check the robustness of our estimates: pooled data models (or naïve model) without control, pooled data models with controls (or expanded naïve model), fixed effect models with all controls, and lag models with all controls. Log-log specifications using fixed effects and lag-dependent variable models were deemed the most appropriate and controlled for time-invariant differences. RESULTS: Our regression models indicate that, with a 1% increase in economic growth, rehabilitation expenditure would be associated with a 0.9% and 1.3% increase in expenditure. Given low baseline levels of existing rehabilitation expenditure, we anticipate that predicted increases in rehabilitation expenditure due to economic growth may be insufficient to meet the growing demand for rehabilitation services. Existing expenditures may also be vulnerable during periods of economic recession. CONCLUSION: This is the first known estimation of the association between rehabilitation expenditure and macroeconomic conditions. Our findings demonstrate that rehabilitation is sensitive to macroeconomic fluctuations and the path dependency of past expenditures. This would suggest the importance of increased financial prioritization of rehabilitation services and improved institutional strengthening to expand access to rehabilitation services for populations.

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.

Do demographic factors affect the environment? Empirical evidence from the Middle East and North African countries.

Every country intends to enhance national production by achieving sustainable development. The purpose of this study is to examine whether there exists any long-run association among environmental deterioration measured by territorial emissions in CO2, demographic factors (total population, population density, and urban population) and some other variables, namely, energy use, per capita income, energy intensity, and industrial value added for the 16 countries from the Middle East and North African (MENA) over 1990-2018. We implemented the generalized method of moments (GMM), fully modified ordinary least square (FMOLS), robust least square estimators, and panel Granger causality techniques for estimation. The empirical estimates reveal that there exists a long run cointegration among the series. Results also exhibit that energy use, per capita income, energy intensity, industrial value added, population density, total population, and urban population have positive effects on CO2 emissions. Furthermore, in each panel, there is bi-directional causality between population density and CO2 emissions, total population and CO2 emissions, and urban population and CO2 emissions. These findings suggest that the policymakers need not exclusively to focus on the transformation of rural labor from an agricultural-based model to urban regions with powerful, dominant industry and services sectors but also related to the changing of rural establishments into urban spaces is required. These changes in demographics involve changes in the demand for additional transportation services, food, shelter, clothing, and other necessities.

Quantifying the interconnectedness between poverty, health access, and rabies mortality.

The global 2030 goal set by the World Organization for Animal Health (WOAH), the World Health Organization (WHO), and the Food and Agriculture Organization (FAO), to eliminate dog-mediated human rabies deaths, has undeniably been a catalyst for many countries to re-assess existing dog rabies control programmes. Additionally, the 2030 agenda for Sustainable Development includes a blueprint for global targets which will benefit both people and secure the health of the planet. Rabies is acknowledged as a disease of poverty, but the connections between economic development and rabies control and elimination are poorly quantified yet, critical evidence for planning and prioritisation. We have developed multiple generalised linear models, to model the relationship between health care access, poverty, and death rate as a result of rabies, with separate indicators that can be used at country-level; total Gross Domestic Product (GDP), and current health expenditure as a percentage of the total gross domestic product (% GDP) as an indicator of economic growth; and a metric of poverty assessing the extent and intensity of deprivation experienced at the individual level (Multidimensional Poverty Index, MPI). Notably there was no detectable relationship between GDP or current health expenditure (% GDP) and death rate from rabies. However, MPI showed statistically significant relationships with per capita rabies deaths and the probability of receiving lifesaving post exposure prophylaxis. We highlight that those most at risk of not being treated, and dying due to rabies, live in communities experiencing health care inequalities, readily measured through poverty indicators. These data demonstrate that economic growth alone, may not be enough to meet the 2030 goal. Indeed, other strategies such as targeting vulnerable populations and responsible pet ownership are also needed in addition to economic investment.

How do resource dependence and technological progress affect carbon emissions reduction effect of industrial structure transformation? Empirical research based on the rebound effect in China.

Under the guidance of carbon peak and carbon neutral targets, the industrial structure transformation is vital for carbon emissions reduction in China. However, there is a rebound effect of carbon emissions during the industrial structure transformation. Resource dependence and technological progress have significant impacts on industrial structure transformation and its carbon reduction effect. This paper explores how industrial structure transformation under resource dependence causes the rebound effect from a technological progress perspective. The key results indicate that (1) resource dependence distorts the carbon emissions reduction effect of industrial structure transformation; (2) with the development of technology, the industrial structure upgrading under resource dependence could cause an increase on carbon emissions at the beginning, but the increase would be weakened subsequently, displaying a two-stage feature; (3) the industrial structure rationalization under resource dependence reduces carbon emissions at first, but the reduction would be weakened as the technology develops, then industrial structure's rationalization shows an insignificant impact on carbon emissions, and finally reduces carbon emissions again, presenting a four-stage characteristic; (4) environmental protection technology can correct the distortion effect of resource dependence on the industrial structure rationalization and amplify the industrial structure rationalization's reduction effects on carbon emissions; (5) with the development of energy-saving technology, industrial structure rationalization has a paradoxical impact on carbon emissions, the industrial structure rationalization first reduces, then increases, and finally reduces carbon emissions, indicating an inverted "N" relationship. Finally, policy recommendations for carbon emissions reduction are proposed from the perspective of industrial structure transformation and technological progress.

Decent Work Country Programme 2019 to 2021

Suriname is located in the South American continent with a population of approximately 573,0003 and geographic size of slightly under 164,000km. It is a democratic country which became independent from the Netherlands in 1975. Its Gross National Income (GNI) per capita is USD 5,1504 . In terms of human development, in 2017 it was classified as an upper middle income country and ranks 100 out of 189 countries on the Human Development Index (HDI). The population density is approximately 3.6 persons per square kilometre, making it the lowest in the Latin America and Caribbean region. Most of the population lives in the capital city of Paramaribo and surrounding areas, located on the country's northern coast. However, there are populations which live in the interior rural regions of Suriname. The relatively high cost of transportation and communication challenges pose barriers to the provision of services.

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.

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

Artificial Intelligence Assists the Construction of Quantitative Model for the High-Quality Development of Modern Enterprises.

Artificial intelligence companies are different from traditional labor-intensive and capital-intensive companies in that their core competitiveness lies in technology, knowledge, and manpower. Enterprises show the characteristics of a high proportion of intangible assets, strong profitability, and rapid growth. At the same time, there are also the characteristics of high risk and high uncertainty. In addition to the existing value brought by existing profitability, corporate value should also consider the potential value brought by potential profitability. Enterprise value is affected by many factors such as profitability, growth ability, innovation ability, and external environment. Traditional valuation techniques are often utilised to value artificial intelligence businesses in the present market. Traditional valuation methods ignore the dynamics and uncertainties of artificial intelligence enterprise value evaluation, make static and single predictions of future earnings, ignore the value of enterprise management flexibility, and are unable to assess the intrinsic value of artificial intelligence businesses. Based on the projection pursuit method, this paper constructs a modern high-quality development enterprise high-quality development evaluation model, uses real-code accelerated genetic algorithm to optimize the projection objective function, and calculates the best projection direction vector and projection value. The collected sample data can be imported into the evaluation model to calculate the comprehensive evaluation value of the high-quality development of modern high-quality development enterprises and the weights of various indicators included. By comparing the size of the comprehensive evaluation value, each sample can be calculated Evaluation of the level of high-quality development. The results show that the high-quality development level of China's overall economy is on the rise, but the level of development is still low, and there is a large gap between the development level of the eastern region and the central and western regions. Using the systematic generalized moment estimation method, empirically, we analyse the impact of artificial intelligence on the high-quality economic development. The results show that artificial intelligence at the national level and in the central and western regions will significantly promote high-quality economic development, while artificial intelligence in the eastern region has a significant inhibitory effect on high-quality economic development.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

Economic convergence in a globalized world: The role of business cycle synchronization.

Increasing economic integration and global synchronization can be key for countries aiming to catch up in GDP per capita terms. Little attention has hitherto been placed in synchronization as determinant of convergence. In this paper we estimate the effect of economic globalization and synchronization on income convergence for a sample of 89 developed and developing countries in the period 1970-2015. We use a dynamic factor model and panel data techniques to undertake the objectives of the paper. We show that synchronized countries (those correlated with the factor) exhibit a higher response on GDP per capita growth with variations on the global business cycle. This implies that synchronization improves growth for that group in global expansionary phases, but also implies risks during global recessions. On the contrary, the effect on growth of an economic globalization index is less relevant for synchronized countries than for asynchronized countries. The latter result implies that asynchronized countries can benefit more increasing their levels of economic globalization.

Efficiency of tourism development in China's major cities under the constraint of PM2.5.

Climate / weather factors are important factors for tourists to choose tourist destinations. With the public's attention to the influence of haze, air quality will have a profound impact on the development of tourism in tourist destinations. Based on the Epsilon-based Measure (EBM) super-efficiency model and Global Malmquist-Luenberger index analysis method, this paper aims to study the tourism development efficiency of 58 major cities in China from 2001 to 2016 and analyse the total factor productivity in the development of urban tourism and the changing driving factors in consideration of the undesirable output of haze characterised by PM2.5 emission concentration. The study findings show that the overall efficiency of tourism development of 58 cities is not high in 2001-2016, but the tourism development efficiency of all cities is increasing year by year. Under the constraint of haze, the efficiency of urban tourism development is not directly proportional to the degree of urban development. The overall redundancy rate of each input index is slightly high, and the redundancy of PM2.5 emission concentration has a considerable effect on the efficiency of urban tourism development. The overall change trend in total factor productivity in the development of urban tourism is improved, mainly due to the improvement of technological progress factors. On this basis, the corresponding policy implications are concluded according to high-efficiency and high-quality development of tourism in 58 major cities.

Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms.

The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China's high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ's economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ's regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China's high-tech industrial parks.

RD internationalization, domestic technology alliance, and innovation in emerging market.

Although R&D internationalization plays an important role in enterprises' globalization, few studies explore the mechanism of R&D internationalization and emerging market companies' innovation, or the relationship between R&D internationalization, domestic technology alliances and absorptive capacity. How does the R&D internationalization of emerging market enterprises affect the innovation of those enterprises? Under fierce market competition, do absorption capacity and domestic technology alliances have a significant impact on enterprise innovation? From the perspective of the knowledge-based view, this paper studies 185 enterprises undergoing R&D internationalization in China from 2012 to 2017, using high-dimensional Poisson fixed effects model, we use instrumental (HDFE IV) estimation to explain the impact of R&D internationalization on the innovation of the parent company and the mechanism behind it. The study finds that R&D internationalization positively promotes the parent company's innovation, and domestic technology alliances and absorptive capacity play a partial mediator role in R&D internationalization. In the face of fierce market competition, domestic technical alliances play a significant role in promoting enterprise innovation, while absorptive capacity plays a negative role in promoting enterprise innovation with the moderating effect of market competition.

Beta decoupling relationship between CO2 emissions by GDP, energy consumption, electricity production, value-added industries, and population in China.

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances.

Relationship between FDI, fiscal expenditure and green total-factor productivity in China: From the perspective of spatial spillover.

Deeply investigating the relationship between foreign direct investment (FDI), fiscal expenditure and green total-factor productivity (GTFP) is beneficial to formulating effective policies to promote the high-quality development in China. Based on theoretical mechanism analysis, with panel data of China's mainland 30 provinces during 2003-2017, this paper utilizes spatial econometric model to empirically explore the effects of FDI, fiscal expenditure and their interaction item on the growth of GTFP in China. The results show that FDI significantly promote the growth of the local and its neighboring GTFP, and both fiscal expenditure and the interaction between FDI and fiscal expenditure exert significantly negative effects on the growth of GTFP in the local and its neighboring regions. A series of robustness checks and the endogeneity test can ensure the reliability of these results. In addition, great heterogeneity can be found across China's different regions in the relationship between FDI, fiscal expenditure and GTFP. The conclusions suggest that it is necessary to give fully play to the synergy between FDI and fiscal expenditure and formulate regionally targeted policies to improve GTFP and promote high-quality development in China.

The effects of carbon emissions, rainfall, temperature, inflation, population, and unemployment on economic growth in Saudi Arabia: An ARDL investigation.

OBJECTIVE: While macroeconomic and environmental events affect the overall economic performance of nations, there has not been much research on the effects of important macroeconomic and environmental variables and how these can influence progress. Saudi Arabia's economy relies heavily on its vast reserves of petroleum, natural gas, iron ore, gold, and copper, but its economic growth trajectory has been uneven since the 1990s. This study examines the effects of carbon emissions, rainfall, temperature, inflation, population, and unemployment on economic growth in Saudi Arabia. METHODS: Annual time series dataset covering the period 1990-2019 has been extracted from the World Bank and General Authority of Meteorology and Environmental Protection, Saudi Arabia. The Autoregressive Distributed Lag (ARDL) approach to cointegration has served to investigate the long-run relationships among the variables. Several time-series diagnostic tests have been conducted on the long-term ARDL model to check its robustness. RESULTS: Saudi Arabia can still achieve higher economic growth without effectively addressing its unemployment problem as both the variables are found to be highly significantly but positively cointegrated in the long-run ARDL model. While the variable of carbon emissions demonstrated a negative effect on the nation's economic growth, the variables of rainfall and temperate were to some extent cointegrated into the nation's economic growth in negative and positive ways, respectively. Like most other nations the short-run effects of inflation and population on economic growth do vary, but their long-term effects on the same are found to be positive. CONCLUSIONS: Saudi Arabia can achieve both higher economic growth and lower carbon emissions simultaneously even without effectively addressing the unemployment problem. The nation should utilize modern scientific technologies to annual rainfall losses and to reduce annual temperature in some parts of the country in order to achieve higher economic growth.

User demands analysis of Eco-city based on the Kano model-An application to China case study.

Eco-cities have witnessed rapid growth in these years worldwide. As the Eco-cities entering operation stage gradually, more and more researchers have found that users (who are living or working in the Eco-cities) satisfaction is one of the most important factors to determine the success or failure of Eco-cities. Therefore, it is very important to investigate the user demands to attract more citizens willing to live or work in the Eco-cities, which will make the development of Eco-cities more sustainable and solid. The recent researches on user demands investigation and analysis in the Eco-cities mainly focused on understanding the user need itself, yet lack of research on the relationship between the user demand and user satisfaction. This paper initially introduced the Kano model analysis method to the research field of user demands in Eco-city, to explore the relationship between the user demand and user satisfaction. After proposing user demands library in Eco-city (including Land use, Ecological environment, Green building, Energy utilization, etc.), the user demands classification and importance analysis methods of Eco-city were proposed based on Kano model. The questionnaire survey for users of two Eco-cities in China as case study was conducted, consisted of user demand items questionnaire based on the Kano model and a questionnaire on the importance of the user demand items. By utilizing the integration of quantitative analysis methods based on the Kano model and Analytic Hierarchy Process (AHP) method, the final ranking of user demands importance was obtained. Comparing with the existing literatures in terms of user demands research for Eco-city, the user demands analysis method based on Kano model of this paper, is able to reveal the influence degree of user satisfaction towards the facilities and services provided in the Eco-city. The user demands analysis method can be used for other researchers worldwide to investigate and quantitively analyze user demands according to their local development situation and preference of Eco-city. The user demands analysis results obtained through this method, can benefit different stages of Eco-city.