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Green taxes are taxes collected to protect the environment by controlling the negative effects of certain activities and products on the environment. They are also an instrument of environmental policy and can therefore contribute to several sustainable development goals. According to the studies carried out, the green economy aims to ensure sustainable development. The main objective of this paper is to identify the existing relationships between green taxes and sustainable economic development through a dynamic panel analysis. A dynamic panel analysis was therefore carried out on the existing links between environmental taxes and charges at the European level and the indicators of the circular economy. The results of the two dynamic regressions for the two dependent variables, namely total green taxes and energy taxes, show a positive and significant correlation with the variation of GDP and with primary energy consumption, confirming the hypothesis that environmental taxes and energy taxes are closely linked to these two important indicators of sustainable development. Thus, as GDP changes, the taxes on energy production and the energy products used in both transport and stationary applications increase. As a result of the analysis, we can note that the increase in primary energy consumption and the consumption of raw materials leads to an increase in environmental and energy taxes. Energy taxes are a possible solution to reduce CO2 emissions in third world countries and may even stimulate climate action. In contrast, we found no significant correlation between green taxes and the following variables: Human Development Index, net greenhouse gas emissions, private investment and gross value added related to circular economy sectors, the consumption of raw materials, waste generated, waste treatment, the supply, transformation, and consumption of renewable energy, public expenditure on environmental protection, and climate-related economic losses. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions. [ABSTRACT FROM AUTHOR]

In order to promote ecological sustainability, the issue of sulphur dioxide emissions is of increasing interest to researchers. Majority of the current research, however, focuses on the relationship between sulphur dioxide (SO2) emissions, foreign direct investment (FDI), and trade, as well as the effects of trade on SO2 emissions, thus rarely takes it into account that the greater impact of the institutional environment and economic growth on SO2 emissions. Using the 2008–2017 provincial panel data, this paper uses a fixed effects model to empirically test the institutional environment and economic growth of sulphur dioxide (SO2) emissions. The results show that GDP growth and SO2 emissions had an inverted "U"-shaped relationship. The institutional environment and the higher level of government intervention in the region led to SO2 emissions decreasing significantly, and the institutional environment and the level of government intervention on economic growth and SO2 emissions form a negative regulatory role. In this paper, environmental governance research, specified by the regional environmental governance, and government environmental performance audit policy provide empirical evidence, thus promoting sustainable ecological and environmental development. [ABSTRACT FROM AUTHOR]

This paper examines the causal relationship between human capital and economic factors in BRICS countries using a panel vector autoregressive model and data from 1997 to 2020. The economic factors considered include foreign direct investment (FDI), imports, exports, and gross domestic product (GDP). The study conducts a comparative analysis of Brazil, India, China, Russia, and South Africa by adopting a vector autoregressive (VAR) model. The findings indicate a bidirectional causality between human capital and FDI in China, while a unidirectional causality from FDI to human capital is observed in Brazil. Moreover, a unidirectional causality exists from human capital to GDP in Brazil, Russia, India, and South Africa. Additionally, a unidirectional causality is found from human capital to imports and exports in South Africa. Overall, the results suggest the pivotal role of human capital in achieving sustainable economic development in BRICS countries. Policymakers should ensure sustained investment in human capital, focusing on economic growth, FDI, and international trade. [ABSTRACT FROM AUTHOR]

The start of the full-scale Russian-Ukrainian war caused the largest wave of migration in the 21st century. More than five million Ukrainian citizens left for EU countries within a few months of the start of the conflict. The purpose of this paper is to forecast the level of health care expenditure in Ukraine for 2023–2024, considering the scale of migration and the fall in the level of GDP. The authors propose three scenarios for the development of Ukraine's economy in 2023–2024, taking into account changes in the age structure of the population, migration, and the amount of health care expenditure: (1) Pessimistic, in which economic growth will resume only in 2024, with a GDP rise of 5.6%, provided that the war concludes at the end of 2022. Under this scenario, inflation will be about 21% in 2023–2024, a slight decrease compared with the previous year. Some 12% of the population of Ukraine will have emigrated, resulting in a corresponding 12% drop in health care expenditure in 2023–2024. (2) Basic (realistic), in which economic growth will be about 5% in 2023–2024, inflation will be under 10%, and migration will have accounted for 5% of the country's population. Under this scenario, there will be an increase in health care expenditure of more than 40% in 2023–2024. (3) Optimistic, according to which rapid economic growth is expected in 2023–2024, inflation will not exceed 7%, the majority of those who left Ukraine in the early months of the war will return, and health care expenditure will increase by more than 70% in 2023–2024. The methodology of forecasting public expenditure on health care has been based on a six-step cohort method. The results have indicated that the cost of updating the age structure of Ukraine's population every year will decrease due to the aging of the population, and the overall impact of demographic processes will be negative. The impact of mass migration due to the war creates a significant change in health care costs, requiring administrative bodies to monitor the situation promptly and make appropriate changes to the structure of budget expenditure. [ABSTRACT FROM AUTHOR]

Today's economic systems are, on the one hand, exposed to various risks and uncertainties with their trends changing almost daily, while on the other hand, they represent an extremely complex system with a large number of sustainable influential parameters. The challenge is to model macroeconomic parameters and achieve sustainability, yet also satisfy conflict situations with an increased level of uncertainty. The aim of this paper is to create an appropriate functional model by examining the mutual influence of various macroeconomic factors. It assesses a total of four scenarios considering mutual influences of: FDI (foreign direct investments), GDP (gross domestic product), imports, exports, inflation rate, RER (real exchange rate) and employment rate as defined parameters. First, the DEA (Data envelopment analysis) model was applied to determine the initial efficiency of two countries, Bosnia and Herzegovina (BIH) and Serbia, for the period 2005–2020. Then, PCA (Principal Component Analysis) was applied in combination with DEA to obtain more precise values. In addition, IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define weight coefficients of macro-economic parameters. Finally, the CRADIS (compromise ranking of alternatives from distance to ideal solution) model was applied for the final ranking of part of decision-making units. This developed, integrated model can be classified as a mathematical method for economic analysis and gives extended opportunities for solving different problems. The results show that 2009, 2013 and 2016 were the most favorable years in terms of the conditions set when it comes to Bosnia and Herzegovina, and 2012, 2014 and 2016 when it comes to Serbia. These years have been singled out and can be a benchmark for further handling and modeling of macroeconomic elements. In addition, correlation was tested using statistical coefficients. [ABSTRACT FROM AUTHOR]

Concepts of ethical behavior and corporate social responsibility have emerged in recent years due to organizations' increasing ethical problems. Business ethics, social responsibility, and corporate governance are significant drivers for organizational performance, growth, and sustainable development (SD). In this paper, we propose an original tool, at a macroeconomic level, for the integration of concepts such as business ethics (BE), corporate social responsibility (CSR), and corporate governance (CG). The paper also seeks to establish the relationships among corporate responsibility dimensions (CR), sustainability, and economic performance. This research used the Sustainable Development Report 2020, Candriam's ESG Country Report, World Bank Doing Business 2020 Report, World Bank national accounts data, and Eurostat as data sources. As part of the research, we selected the European Union states (27) plus the United Kingdom. The main results reveal the positive direct and indirect influences of corporate responsibility on economic performance, ultimately leading to sustainable development. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the metrics the United Nations has set and called the Sustainable Development Goals (SDGs) and their association with the gross domestic product (GDP) in emerging economies. SDGs have been identified to measure healthy development, whereas GDP has historically been used to measure economic health and has been prioritized above many other indicators. This research deploys the feasible generalized least squares (FGLS) and the seemingly unrelated regressions (SUR) on panel data consisting of the five BRIC countries spanning 2000 through 2017 to estimate a regression model that shows the association of SDGs with GDP. The paper concludes that targeting GDP may not lead to achieving overall SDGs. [ABSTRACT FROM AUTHOR]

This article examines the relationship between household food waste and Gross Domestic Product (GDP) in various European regions, aiming to determine how economic prosperity influences the levels of household food waste. Using comparative analysis of secondary and tertiary data, a synthetic indicator (IpFW) was developed to assess the interaction between GDP per capita and household food waste per capita. Linear correlation analysis was also applied for better interpretation of the data. Despite expectations, higher GDP is not consistently correlated with lower household food waste, challenging economic prosperity and environmental stewardship assumptions. This research highlights the complexity of the interaction between economic factors and household food waste management, revealing a lack of significant correlation even at the regional level. The findings indicate a need to re-evaluate current policies and highlight that improving food supply chains and influencing consumer behavior can promote more sustainable consumption patterns, which is in line with the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Nighttime light data offer a unique view of the Earth's surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area. [ABSTRACT FROM AUTHOR]

This paper discusses the current state of thought amongst the Sustainability Indicator (SI) community, what has been achieved and where we are succeeding and failing. Recent years have witnessed the rise of “alternative facts” and “fake news” and this paper discusses how SIs fit into this maelstrom, especially as they are themselves designed to encapsulate complexity into condensed signals and it has long been known that SIs can be selectively used to support polarized sides of a debate. This paper draws from chapters in a new edited volume, the “Routledge Handbook of Sustainability Indicators and Indices”, edited by the authors. The book has 34 chapters written by a total of 59 SI experts from a wide range of backgrounds, and attempts to provide a picture of the past and present, strengths and weaknesses of SI development today. This paper is an “analysis of those analyses”—a mindful reflection on reflection, and an assessment of the malign and benign forces at work in 2018 within the SI arena. Finally, we seek to identify where SIs may be going over the coming, unpredictable years. [ABSTRACT FROM AUTHOR]

Research on sustainable tourism involves developing an appropriate framework to highlight the interdependences of economic, social and environmental systems. The interdependence is based on the entropy of the system while respecting the principle of holism and diversity of rural tourism sustainability. In this context, sustainability in general and rural tourism in particular can be considered a complex system of development, which in some ways can be studied by statistical and econometric methods that allow the analysis of the interdependences between the variables of rural tourism at county level and at the level of rural communities. Conducting such studies involves identifying the rural communities where rural tourism has reached significant levels. Based on this consideration, this paper aims to identify the development regions and counties of Romania where the trends of development of rural tourism are significantly above the average recorded at country level, as a first step towards particular studies of sustainability in rural communities. [ABSTRACT FROM AUTHOR]

The shared socioeconomic pathways (SSPs) were designed to project future socioeconomic developments as they might unfold in the absence of explicit additional policies and measures to limit climate forcing or to enhance adaptive capacity. Based on the sixth national population census and the third economic census data of China in 2010, this paper projects the population and economic conditions of the Yangtze River basin from 2010 to 2100 under the SSPs. The results showed that: (1) the population growth rate in most areas of the Yangtze River basin will decrease from 2021 to 2100. The population of the eastern Sichuan Province will decrease obviously, while it will increase obviously in Shanghai during this period. The population of the Yangtze River basin will decline from 2010 to 2100 under the SSPs except for SSP3; (2) The GDP (Gross Domestic Product) in most regions will increase by more than CNY 30 billion (Chinese Yuan) compared with 2010 and the total GDP will continue to rise after 2020; (3) The population of the three major urban agglomerations will decrease from 2020 to 2100. However, the GDP of the three major urban agglomerations will increase year by year, among which the YRDUA (Yangtze River Delta Urban Agglomeration) has obvious economic advantages. The GDP growth rate will maintain above 6% in 2020 under different SSPs, and then the growth rate will slow down or stall, even with negative growth in SSP1 and SSP4; (4) The GDP Per of the Yangtze River basin shows growth under different SSPs and it will maintain a growth rate of 6–9% until 2020. While the average annual growth rate of the SSP5 will be about 2.56% at the end of the 21st century, and it will remain at about 1% under other scenarios. This paper provides a scientific basis for the study of future population and socioeconomic changes and climate predictions for quantifying disaster risks. [ABSTRACT FROM AUTHOR]

With the rapid advancement of global technology, high-tech industries have become key drivers for the economic growth of many nations and regions. This study delves into the spatiotemporal dynamics and determinants influencing China's high-tech sector from 2007 to 2021. The key findings include the following: (1) Nationally, the high-tech sector has been a cornerstone for China's GDP growth over the preceding 15 years. The expansion rate of the high-tech domain consistently outpaces the broader economy. In particular, since 2015, the percentage of high-tech industries' GDP has surged to approximately 42%. (2) At the provincial level, the spatial representation of the high-tech sector's GDP predominantly leans towards the east and the south, revealing pronounced spatial autocorrelation. Nevertheless, the demarcations between east and west and between north and south are progressively diminishing. (3) Regarding influential determinants, R&D internal expenditure, operating revenue, and industry agglomeration have been instrumental in spearheading innovation and bolstering growth within the high-tech realm. These insights are invaluable for comprehending the evolutional nuances of China's high-tech industry and devising pertinent policy measures. [ABSTRACT FROM AUTHOR]

Economic growth is an important metric for the sustainable development of any region or country. Central and Eastern Europe members of the European Union are important players of the single market, which implements regional policies to mitigate socio-economic differences between its newer and established members. The present study examines the factors that shape the phenomenon of economic growth across 62 NUTS 2 regions from 11 countries in Central and Eastern Europe during the period 2011–2020. The study investigates determinants related to education level, involvement of young people in the labor market, household net income, high-speed internet facilities and overall hours spent at work during a year. Three panel data models estimated with first-differenced generalized method of moments showed that regional economic growth was significantly influenced mainly by income, the rate of young employees and educational attainment level. Relevant insights and policy implications for regions in CEE countries are addressed. [ABSTRACT FROM AUTHOR]

In this study, the factors affecting economic growth in developed countries within the context of their sustainability transition are explored. By analyzing both traditional and novel factors, we aim to expand the scientific knowledge of the drivers behind sustainable economic development. To achieve this purpose, some factors that have demonstrated the potential to positively impact economic growth while simultaneously promoting environmental sustainability are included. Research results demonstrate that a 1% increase in energy consumption is associated with a 0.314% increase in real GDP, indicating a positive relationship between energy usage and economic growth. Additionally, the consumption of renewable energy boosts a positive impact on sustainable economic growth: When it grows by 1%, the real GDP increases by 0.12%. The empirical findings further reveal that scientific progress and economic freedom are significant drivers of economic growth, as a 1% increase in both factors leads to an increase in economic output by 0.349% and 0.323%, respectively. By conducting a comprehensive analysis, we provide valuable insights into the complex interplay between economic growth and sustainability in developed countries. Based on these findings, the study offers specific policy recommendations, which include the diversification of the energy mix, the promotion of education and scientific advancement, and the digitalization of public services. [ABSTRACT FROM AUTHOR]

There are many concepts and methods trying to accommodate the growth of cities without impairing sustainability. However, most are too complex, cannot measure green performance over time and fail to deliver actionable advice to decision-makers. The Green City Concept (GCC) is one of the latest of these concepts. This paper introduces a Green City Conceptual Framework (IHS-GCCF) and a harmonized method to measure Global Green City Performance over time (GGCPI). The IHS-GCCF highlights the importance of energy as a steward of resource efficiency and green performance. IHS-GCCF and GGCPI can be used to measure green performance, to explain possible factors influencing the calculated green performances over time, set targets and track achievements. They are adaptable to individual city needs. By applying the method to fifty cities worldwide through desk research, the paper shows that a green city is also sustainable and liveable. It also shows that GDP influences Green City Performances positively, population size influences it negatively and sanitation and air quality sectors influence green city performance the most. The paper addresses a range of gaps in the green city field. It consolidates prior research into one actionable conceptual framework and method. [ABSTRACT FROM AUTHOR]

The inter-regional freight volume is a crucial factor for transportation infrastructure planning and investment decision-making. However, existing studies on freight volume estimation have mainly focused on the total freight volume within a specific region, without taking freight flow into consideration. In this research, a gravity model was employed to estimate the inter-regional trade coefficient matrix based on the input–output tables of the 31 provinces in China in 2017. The inter-regional freight volume was then determined by converting the value flow into freight flow. To determine the model parameters, we used information from 2017 and subsequently validated the results using dates from 2012 to 2020. We also studied the impact of industrial structure change on freight volume by simulating dates from the aforementioned model in 2017. The results indicated that the model can effectively simulate inter-regional freight volume while taking into account the influence of industrial restructuring. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China's multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China's GDP moved 128.80 km to the southwest. These results indicate China's achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy. [ABSTRACT FROM AUTHOR]

This article aims to uncover the asymmetric labor-market consequences of the long-run civil war in Afghanistan by employing a non-linear autoregressive distributed lags (NARDL) model and an asymmetric causality technique over the period from 2004Q3 to 2020Q4. The findings from the NARDL model reveal that the positive asymmetric shocks from the cost of war, GDP growth, final government expenditure, foreign direct investment, and the rule of law significantly decrease the unemployment rate, while their negative asymmetric shocks increase the unemployment rate in the short and long runs. Innovatively, the composite financial inclusion index has been incorporated into the model, which provides interesting results. It demonstrates that enhancing the outreach of financial services plays an important role in reducing the unemployment rate during wartime in Afghanistan, while its exclusion is found to increase the unemployment rate both in the short and long runs. Moreover, the results of the asymmetric causality test reveal that an asymmetric causality runs from both the positive and negative components of the cost of war, the composite financial inclusion index, GDP growth, foreign direct investment, inflation rate, population growth, and the rule of law to the unemployment rate, while no evidence is found to support a causality nexus between the unemployment rate, final government expenditure, and the secondary school enrollment rate. The results entail several policy implications that are discussed. [ABSTRACT FROM AUTHOR]

Economic expansion leads to higher CODe2 emissions, which puts pressure on environmental degradation. More than 30% of carbon emissions are contributed by the top0polluting countries in the world through their energy consumption. Therefore, the current study examines the association between CO2 emissions, energy consumption, GDP and industrial production, along with other control variables at the aggregated and disaggregated levels for the top emitter countries for the 1990–2019 period. The short- and long-term results indicate that CO2 emissions are positively and significantly linked with energy consumption, except carbon emissions from the gas model, by employing the PARDL model using pooled mean group (PMG) analysis. Thus, gas consumption is less polluting to the environment than other sources of energy; therefore, countries need to reduce the consumption of coal and oil, which will lead to a decrease in CO2 emissions. This refers to the composition effect, which focuses on the use of clean energy instead of dirty energy in the production and consumption processes. The shift from oil or coal to gas in the production process will help to reduce the oil demand, which ultimately controls its consumption and prices, which may help to control the prices of various other goods and services. [ABSTRACT FROM AUTHOR]

Moldova possesses the largest area of farmland as a share of its total land surface, an advantage which should encourage economic development strategies oriented towards the agriculture sector. Government subsidies and agriculture loans have been used as tools for developing the Moldavian agriculture. However, considering the challenges generated by both climate change (the drought from year 2012 that affected 80% of farmland) and a difficult political situation (restrictions imposed by the Russian Federation on the Republic of Moldova's agri-food imports and exports between 2013 and 2014), the country's agricultural system ranks very low when it comes to agricultural production efficiency. The present paper analyses the performances of the agricultural sector and its impact on the Moldavian economy over a nine-year period (between 2008 and 2016), by using a custom-developed analytical framework based on a dataset containing 21 relevant indicators. The analytical framework generates various perspectives that can be used to elaborate an economic sustainable development strategy of the Moldavian agriculture sector. The development of the analytical framework is based on the dynamics of agriculture subsidies, agricultural loans, the agricultural sector's gross domestic product (GDP) and gross value added (GVA), as well as the dynamics of agricultural production and production value, also considering the main crops belonging to the Moldavian agriculture sector. The results are presented as sets of mathematical regression models that quantify the relationships found between the relevant agricultural parameters and their impact on the economics of the agricultural sector. It has been identified that the agriculture sector has a considerable impact on the Moldavian economy, a fact revealed by the significant model between the agriculture GVA and total GVA and GDP. A significant, negative correlation model was identified between agriculture subsidies and agriculture loans, although a small percentage of Moldavian agriculture farms were subsidized. Strong correlation models were also identified between wheat and maize production and total agriculture production, emphasizing the importance of these two crops for the Moldavian agricultural economy. Grape and maize production values also generated a correlation model, emphasizing the market interconnection between these crops It can be concluded that the increase in value of governmental agriculture subsidies, as well as expanding their addressability in order to maximize the access possibility for a higher number of agriculture farms, are essential for the Moldavian agriculture sector's future development, since considering the limiting value of and accessibility to subsidies, a direct correlation model was identified between governmental agriculture subsidies and agriculture GVA. [ABSTRACT FROM AUTHOR]

The relationship between sustainable growth and public investment, considered one of the key factors, is a topic of interest in the context of globally adopted sustainable development strategies and current budgetary constraints, especially in the case of tight budgets in developing countries, which constrain public investment more than current expenditure, for political or other reasons. Although there are endogenous growth patterns that incorporate public spending as a factor that promotes growth, the findings in the empirical literature provide contradictory results. The study is an empirical investigation into the effects of public investment on sustainable economic growth in emerging EU and Central European countries. For the period 1995–2019, the research shows that, in most of the countries included in the sample, the long-term impact of a public capital shock on GDP is estimated to be negative. The analysis of the effect of public investment on sustainable economic growth was performed by applying the VAR model and impulse response functions, the results being confirmed by estimating the accumulated multiplier to obtain the GDP response to a shock equal to a standard deviation of public capital. [ABSTRACT FROM AUTHOR]

The question of how to balance rapid economic growth with ecosystem pressures has become a key issue in recent years. Using the Tapio decoupling model, the spatial autocorrelation model, and the LMDI decomposition model, we analyzed the spatiotemporal variation in gross ecosystem product (GEP) in Hubei Province, studied the relationship between GEP and economic growth, and analyzed the driving factors of GEP variation. The results show that, during the period 2010–2019, the decoupling coefficient between GEP and economic growth in Hubei Province gradually decreased, while the decoupling relationship changed from weak decoupling to strong decoupling; this change is reflected not only in the decoupling index values of various cities but also in the number of changing cities, so this negative change should attract the attention of policy-makers. In addition, there is a significant local spatial autocorrelation in Hubei Province, mainly distributed in the northwest and southwest of the province, and the trend is becoming increasingly obvious. As the decoupling trend is negative, it is necessary to pay attention to local autocorrelation changes, especially in highly correlated cities, and take action to prevent the further exacerbation of such decoupling to maintain healthy economic and social development. Regarding the driving factors of GEP changes in Hubei Province, cities with strong decoupling and those with weak decoupling have certain differences, and different types of decoupling cities need to adopt different strategies to alleviate pressure on the ecological environment. Cities with a weak decoupling need to address the problem of pollutant emissions associated with industrial upgrading and the positive impact of scientific and technological innovation on the ecological environment. Cities with strong decoupling should not only address pollutant discharge but also improve the area of ecological land. From the perspective of urban development, the high-quality development trend of Wuhan, Yichang, Xiantao, Qianjiang, Xianning, and other cities shows a continuous trend of improvement. Ezhou, Jingzhou, Shennongjia, and other cities need to guard against the loss of ecosystems caused by economic growth. [ABSTRACT FROM AUTHOR]

Although the Gulf Cooperation Council (GCC) countries are in an arid region with limited water resources, the per capita water and electricity consumptions are high, at 560 L/capita/day and 7000–18,000 kWh/year, respectively. Although macroscale parameters (e.g., GDP and population) have been assumed to be correlated with water and electricity demand, this study aims to verify whether this assumption still holds true. As opposed to the previous literature, this study reveals that, although water production and electricity generation had been correlated with GDP and population for years, they have been decoupled from these macroscale parameters since 2015. Such decoupling can be explained by the three phases of economic development. In the initial stage, GDP and population growth promoted rapid increases in water and electricity demands, which came down in the second stage as the consumers became satisfied with water and electricity supplies. In the third stage, the water and electricity demands were decoupled from GDP and population due to demand-management policies for environmental protection and cost saving, combined with consumers' efforts, such as water-saving faucets and energy efficiency in homes, which indicates that microscale parameters have become more influential on water and electricity demands than macroscale parameters. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to study the influence of various indicators related to the knowledge economy on country competitiveness in the European Union (EU). Based on the Pearson coefficient and panel-data regression models, we analyzed the Global Competitiveness Index (GCI) in relation to research and development (R&D) expenditure (as a % of gross domestic product (GDP)), percentage of population with tertiary education, lifelong learning, GDP per capita, and debt to equity. The findings highlighted the crucial role of both innovation and education as determinants of EU competitiveness and economic convergence. The development of EU policies regarding the lifelong learning possibilities of the European workforce and the focus on research and development activities can significantly contribute to the competiveness of EU member states. [ABSTRACT FROM AUTHOR]

Transport is very important for the economy and the society because good transport infrastructure helps to improve the mobility of citizens and the traffic of goods, thus ensuring economic sustainability. Background: The European Union (hereinafter—EU) plans to maintain support for the development of transport infrastructure in individual member states. Lithuania's role as a transit country in the EU's transport network is of great importance; therefore, efficient transport services and appropriate infrastructure can not only support the domestic market and economic and social growth of the country, but also promote positive economic, social, and political processes in other countries and ensure EU's strategic defence movement channels. Methods: The study was conducted using econometric methods, including correlation analysis and a regression model, to assess the selected parameters of the transport sector of Lithuania as a transit country and their impact on the real gross domestic product (hereinafter—RGDP) of the selected Baltic states (Latvia and Estonia). Results: The study identified a combination of key factors in the Lithuanian transport sector that affect differences in the level of real GDP per capita. Conclusions: The findings revealed differences in the context of relation between RGDP per capita in the three Baltic states and indicators of the Lithuanian transport sector. [ABSTRACT FROM AUTHOR]

Approximately 90% of the sediment yield of the Yellow River is derived from the Loess Plateau. In this paper, the Loess Plateau was used as the research object. To investigate the influence of economic and social development on reducing sediment load of the Yellow River, a mathematical method was employed with hydrological and sediment data from three hydrological stations (Toudaoguai and Sanmenxia at the Yellow River, and Ganguyi at the Yan River) as well as per capita GDP data from the Yan River basin. The results showed that the reduction in runoff in the reaches between the Toudaoguai and Sanmenxia stations accounted for 39.3% of the decrease in the sediment load of the Yellow River, and the other 60.7% of the decrease may have resulted from economic and social development. Using the Yan River basin as an example, there was an inverse relationship between per capita GDP and sediment delivery during the period from 1984 to 2018. Grey relational analysis revealed a relatively high relation between the sediment load of the Yan River and the number of rural laborers transferred from the area, the afforestation area, and the tertiary industry value of Yan'an city. Thus, economic development and social transformation are highly related to sediment delivery in the basin, which may result in a decrease in sediment delivery to some extent. [ABSTRACT FROM AUTHOR]

This study analyzes seven bioeconomy sectors with the aim of establishing the leading contributing sectors to gross domestic product (GDP), and also determines the future relationship between bioeconomy and the national economy in Japan. We use data from World Input–Output Database (WIOD), International Renewable Energy Agency (IRENA), and the World Bank Group for this analysis. First, we use principal component analysis (PCA) techniques to identify the bioeconomy sectors that contribute significantly to the national economy. We find through the PCA that all the bioeconomy sectors that we analyzed contribute almost uniformly and significantly to the national economy. We also find forestry and wood sectors to be the most significant contributing bioeconomy sectors. We use the autoregressive distributed lag (ARDL) bounds test to prove the existence of short-run and long-run relationships between bioeconomy and gross domestic product (GDP). We finally use the vector error correction Granger causality model to establish a bicausality between bioeconomy and GDP in the long-run, but not in the short-run. [ABSTRACT FROM AUTHOR]

Xinjiang's land transport network is mainly comprised of highways and railways. Using statistical data from 1985 to 2015, this study applies the Lotka-Volterra model to investigate the competitive interactions among highway transport, railway transport, and the regional economy of Xinjiang. We can draw the following conclusions: First, highway mileage, highway passenger transportation, and railway freight transportation have played a significant role in promoting the development of the national economy in Xinjiang, while the latter has weakened highway passenger transportation and promoted the growth of the added value of transportation industry (AVTI), railway mileage, and railway passenger transportation. Second, highway passenger transportation enhances the development of railway passenger transportation, while railway freight transportation has played the same role in the development of highway freight transportation. Third, the increase in AVTI will increase total population, but both may have a negative impact on the national economy in the short term. Fourth, there is no significant relationship between the economy and transportation investment. [ABSTRACT FROM AUTHOR]

As the world's highest railway, and the longest highland railway, the Qinghai-Tibet Railway (QTR) has been paid considerable attention by researchers. However, most attention has been paid to the ecological and environmental issues affecting it, and sustainable ecological, social, and economic development-related studies of the QTR are rare. In this study, by analyzing the passenger traffic, freight traffic, passenger-kilometers, and freight-kilometers of the QTR for the period 1982-2013 and the transport structure of the Tibetan Plateau (TP) for 1990-2013, the evolutionary process of the transport system in the TP following the construction of the QTR has been revealed. Subsequently, by comparing Gross Domestic Product (GDP), population, industrial structure, and urbanization level at the county and 1 km scales between surrounding and non-surrounding areas of the QTR, the differences in socioeconomic performance before and after its construction were detected. The results show that (1) in the TP, the highway-dominated transport system will break up and an integrated and sustainable transport system will form; (2) at the county scale, the annual growth rates of GDP of counties surrounding the QTR were greater than those of non-surrounding counties for the period 2000-2010. At the 1 km scale, following the opening of the completed line, the GDP of surrounding areas had a greater growth rate than before; (3) analysis at the county and 1 km scales indicated that population was not aggregated into the surrounding areas of the QTR in the period 2000-2010; (4) in terms of industrial structure, the proportion of primary industry decreased continuously, while the proportion of secondary and tertiary industries increased overall in the period 1984-2012. The QTR had no obvious impact on changes in the urbanization level of its surrounding areas. [ABSTRACT FROM AUTHOR]

Frequent fire accidents pose a serious threat to human life and property. The spatio-temporal features of China's urban fires, and their drivers should be investigated. Based on the Spatio-temporal Dynamic panel data Model (SDM), and using fire data gathered from 337 Chinese cities in 2000 to 2009, the influence of spatio-temporal factors on the frequency of urban fires was analyzed. The results show that (1) the overall fire incidence of China increased annually before 2002 and reduced significantly after 2003, and then high fire incidence increased in western China; (2) Spatio-temporal factors play a significant role in the frequency of Chinese urban fires; specifically, the fire assimilation effect, fire inertia effect and fire caution effect. The ratio of fire incidence of China has reduced significantly, and the focus of fire incidence moved towards the western region of China. GDP and humidity have a significant effect on urban fire situation change in China, and these effects may be referred to as "fire assimilation effects", "fire inertia effects" and "fire caution effects". [ABSTRACT FROM AUTHOR]

China has experienced a huge socioeconomic advancement over the past few decades, resulting in great change in land use and land cover. To date, negligible attention has been given to examining the socioeconomic changes in the context of land-use change, especially from a futuristic standpoint. However, motivated by China's latest carbon neutrality target, this study analyzes the prospective changes in socioeconomic status, and carbon dioxide emission in the context of future land-use change, focusing on three future periods: 2026–2030 (carbon dioxide peak phase), 2056–2060 (carbon-neutral phase), and 2080–2099 (long-term period). In this regard, recently published land-use products under seven Shared Socioeconomic Pathways-based scenarios (SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5) as part of the CMIP6, as well as the projected GDP and population under five socioeconomic scenarios are used. To estimate socioeconomic change over prominent land-use types (urban), we combined five socioeconomic scenarios with seven corresponding SSPs-based land-use change scenarios (SSP1 with SSP1-1.9 and SSP1-2.6; SSP2 with SSP2-4.5; SSP3 with SSP3-7.0; SSP4 with SSP4-3.4 and SSP4-6.0; and SSP5 with SSP5-8.5 scenarios). Our results reveal that rapid urban land expansion in the future is the most dominant aspect in China. In the carbon neutrality phase (2056–2060), urban land is expected to expand ~80% more than that of the reference period (1995–2014). In the spatial aspect, the expansion of urban land is mainly prominent in the eastern and central parts of China. For socioeconomic changes, the most prominent increase in the urban population is estimated at 630.8% under SSP5-8.5 for the 2056–2060 period compared to the reference period. Regarding GDP for the urban area, industrial GDP will be higher than service GDP in the carbon emission peak phase (2026–2030), but it is projected to be overtaken by service GDP for the carbon-neutral target (2056–2060) and long-term periods (2080–2099). Further, the CO2 emission in China was found to increase with intensified urban land for the historical period (1995–2019). In the future, the largest increase in CO2 emission from the urban area is anticipated under SSP5-8.5 in the carbon-neutral target (2056–2060) phase, while CO2 emission will largely decline after (2056–2060) under SSP1-1.9, SSP1-2.6, and SSP4-3.4. Importantly, population change is expected to be the most predominant factor in future urban land expansion in China. These findings highlight the importance of well-governed urban-land development as a key measure to achieve China's carbon neutrality goal. [ABSTRACT FROM AUTHOR]

Understanding commuters' behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location's industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy. [ABSTRACT FROM AUTHOR]

This study analyzes the factors influencing the bilateral trade between Mongolia and China using the trade gravity model, principle component analysis (PCA), unit root test, bound test, and the estimation of coefficients in a panel data set from 1996–2019. A total of 9 variables including exports, gross domestic product (GDP), population, geographical distance, cultural distance, trade agreements, tariffs, trade facilitation index of China and Mongolia-China trade cost were considered for all models. The results indicate that the cultural distance between Mongolia and China and the population of Mongolia are stationary at level. The coefficient of GDP (income) of both countries is positive and statistically significant with exports. Moreover, trade facilitation has significant positive impact on exports of both countries. These findings reveal that efforts in improving excellence of border administration, arrangements would make a positive contribution in trade of goods. Another major influencing factor is tariffs, which was negatively significant for exports, suggesting that if China imposes 1% tariffs on Mongolian exports, it will result in 24% decrease of Mongolian exports. The results of regression coefficients show that there is long run association between variables. This indicates that China adopted a more restrictive trade policy on the flow of goods from Mongolia with an increase population of China. The study suggests a free trade agreement and relaxation of export/import procedures for Mongolia in order to increase the GDP of Mongolia. [ABSTRACT FROM AUTHOR]

By 2050, the world population is expected to reach 9.7 billion, almost 90% of which will live in urban areas. With such a fast growth in population and urbanization, it is anticipated that the annual waste generation will increase by 70% in comparison with current levels, and will reach 3.40 billion tons in 2050. A key question regarding the sustainability of the planet is the effect of city size on waste production. Are larger cities more efficient at generating waste than smaller cities? Do larger cities show economies of scale over waste? This article examines the allometric relationship between the amount of municipal waste (total and per capita) and the populations, city area, density, and wealth of city residents. The scope of the research concerned 930 Polish cities. Using the allometric equation, the waste scaling factors were calculated for selected parameters, and the Hellwig method was used to optimize their selection for cities with more than 50,000 inhabitants. The calculations show that the parameter population (1.059) and then the city area (0.934) are important elements influencing the scaling of the amount of municipal waste in cities of all sizes, but none came close to the value of the animal metabolism model (0.75). In response to the question of whether larger cities show benefits from economies of scale, it should be stated that, for the model of city size in Poland, such a regularity does not exist. [ABSTRACT FROM AUTHOR]

The main aim of this work was to identify and present the situation and changes in the level of expenditure on healthcare in the European Union in the years 2013–2017. This involved an analysis of the available literature on healthcare financing, data from the EUROSTAT database. For this work, the comparative method was used, dynamics indicators were used, the Gini concentration coefficient was calculated, and the degree of concentration was presented using the Lorenz curve. Pearson's linear correlation coefficients were also used. A descriptive, tabular and graphic method was used to present the test results that were obtained. A high concentration of expenditure on healthcare was found in the EU countries with the largest population and that are the most economically developed. These results also relate to these expenses on a per capita basis. The main factor differentiating the level of healthcare financing was the level of economic development. This regularity was confirmed in the statement of expenditure per capita, the relationship between this expenditure and the value of GDP and the results obtained when calculating the correlation between expenditure and economic development. In addition to the economic situation, an important factor determining the amount of expenditure on healthcare was the percentage of the country's population that are older people. [ABSTRACT FROM AUTHOR]

The research's fundamental investigation elaborates on interactions between tertiary educational factors and Namibia's sustainable economic development. Sequential mixed-research-method guides the investigation towards its results: A quantitative statistical data analysis enables the selection of interrelated educational and economic factors and monitors its development within Namibia's last three decades. Subsequent qualitative interviews accumulate respondents' subjective assessments that enable answering the fundamental interaction. Globally evident connections between a nation's tertiary education system and its economic development are partially confirmed within Namibia. The domestic government recognizes the importance of education that represents a driving force for its sustainable economic development. Along with governmental NDP's (National Development Program) and its long-term Vision 2030, Namibia is on the right track in transforming itself into a Knowledge-Based and Sustainable Economy. This transformation process increases human capital, growing GDP, and enhances domestic's living standards. Namibia's multiculturalism and its unequal resource distribution provoke difficulties for certain ethnicities accessing educational institutions. Namibia's tertiary education system's other challenges are missing infrastructures, lacking curricula' quality, and absent international expertise. The authors' findings suggest that, due to Namibia's late independence, there is a substantial need to catch up in creating a Namibian identity. Socioeconomic actions would enhance domestic's self-esteem and would enable the development of sustainable economic sectors. Raising the Namibian tertiary education system's educational quality and enhancing its access could lead to diversification of economic sectors, accelerating its internationalization process. Besides that, Namibia has to face numerous challenges, including corruption, unemployment, and multidimensional poverty, that interact with its tertiary education system. [ABSTRACT FROM AUTHOR]

This study analyzes the effects of foreign direct investment (FDI), economic growth, industrial structure, renewable and nuclear energy, and urbanization on Korean greenhouse gas (GHG) emissions from 1981 to 2014. The cointegration relationship of the variables is examined using autoregressive distributed lag (ARDL) bounds test. The test confirmed the long-run equilibrium among the variables. After that, the short-run and long-run coefficients are estimated by an ARDL error-correction model. The result shows that in the long run, economic growth and urbanization are the main contributors to the increase of GHG emissions, while manufacturing industry share, renewable energy and nuclear energy contributed to the reduction of GHG emissions. The inflow of FDI has led to the increase of greenhouse gases, but the coefficients is negligible. In the short run, economic growth has caused an increase in GHG emissions, while renewable and nuclear energy have contributed to the reduction in GHG emissions. FDI and urbanization did not play a role in increasing of GHG emissions in the short term. [ABSTRACT FROM AUTHOR]

With the growth of an economy, the banking industry expands and the competitiveness becomes intense with the increased number of banks in the economy. The objective of this research was to discover the influence of industry-specific and macroeconomic determinants of non-performing loans (NPLs) in the entire banking system of Bangladesh. We performed an analysis for the period from 1979 to 2018 by an autoregressive distributed lag (ARDL) model and checked the robustness of the results in the vector error correction (VEC) model. The outcomes of this research suggest that both industry-specific and macroeconomic factors influence NPLs significantly. Among the industry-specific determinants, bank loan growth, net operating profit, and deposit rates negatively impact NPLs with statistical significance while bank liquidity and lending rates have a significant positive affiliation with NPLs. Gross domestic product (GDP) growth and unemployment, among the macroeconomic variables, have a negative connection with NPLs. Whereas, domestic credit and exchange rates have a significant positive association with NPLs. The contribution of this research is that the outcomes found by means of econometric models can be used for predicting and measuring NPLs in upcoming years, not only for Bangladesh but also for developing and emerging economies. Individual banks, as well as the banking sector, by and large, can get a guideline from this research. [ABSTRACT FROM AUTHOR]

This study analyzes the evolution and trends of the share of remittances in gross domestic product (GDP) and the influence of migration on remittances in Romania. The analysis on data from Eurostat over 2008–2017 has three components: a statistical analysis, an estimation of evolution of indicators, and an estimation of impact of migration on remittances, using polynomial-time regression and difference equation models, respectively. The results showed that GDP and GDP/capita had a permanent increase, meaning an improvement in the standard of living in Romania, while the other indicators had an evolution with a period of sharp decline triggered by the global crisis, followed by a slow growth. We may conclude that the remittances represented and still represent a relatively stable financial resource for Romania as for the other emerging countries in Europe, affecting in a positive way the standard of living of the citizens, although their value has a tendency to decrease. At the same time, the negative effects of remittances, dependence on money received from migrants and the exodus of "brains" and skilled workers, must be considered, implying the necessity of government policies for a better use of remittances, i.e., mainly for investments and less for consumption. [ABSTRACT FROM AUTHOR]

Results of the CDI and QODA evaluation developed by OECD showed that Korea’s aid presented low efficiency compared to other aid countries. However, these methods represent a qualitative assessment of the effectiveness of each country’s aid and are not applicable to the evaluation of actual aid projects and the identification of causes of the inefficiency. Therefore, it is needed to grasp the reality of Korea’s aid and to identify the cause of aid inefficiency to set up a better ODA policy. The purpose of this study is to improve the effectiveness of Korea’s Official Development Assistance (ODA) provided to developing countries. To do this, we analyzed the efficiency of ODA provided to 33 recipient countries by Korea through data envelopment analysis method. The effects of three factors, illiteracy, integrity, and GDP, on efficiency were also investigated by utilizing a Tobit regression analysis. As a result of the DEA efficiency analysis, it was found that the average efficiency was about 65.74%. By region, the average efficiency scores of Asia, Africa, Central and South America, and Middle East and Eastern Europe were about 47.8%, 78.9%, 70.4%, and 62.7%, respectively. This indicates that Asian countries are inefficient compared to countries from other regions. It was also found that GDP, integrity, and illiteracy have positive effects on efficiency. The methodology proposed in this study can be used for other studies to create an aid policy that produces efficient results. [ABSTRACT FROM AUTHOR]

Developments in technology have enabled envisioning the derivation of materials and products from renewable biomass as an alternative to finite fossil-based resource consumption. Therefore, bioeconomy is regarded as an opportunity for sustainable economic growth. Countries are formulating strategies in accordance with their goals to attain a bioeconomy. Proper measurement, monitoring, and reporting of the outcomes of these strategies are crucial for long-term success. This study aims to critically evaluate the national methods used for the measurement, monitoring, and reporting of bioeconomy contribution to the total economy. For this purpose, research and surveys have been conducted on selected countries (Argentina, Germany, Malaysia, the Netherlands, South Africa, and the United States). The results reveal that the bioeconomy targets set up in the strategies often reflect the country’s priorities and comparative advantages. However, comprehensive approaches to measure and monitor bioeconomy progress are frequently lacking. Most countries only measure the contribution to gross domestic product (GDP), turnover, and employment of the sectors included in their bioeconomy definition, which may provide an incomplete picture. In addition, this study identifies the mismatch between the targets and measurement methods, as the environmental and social impacts of bioeconomy are often foreseen, but not measured. It is concluded that existing global efforts towards sustainable bioeconomy monitoring can be strengthened and leveraged to measure progress towards sustainable goals. [ABSTRACT FROM AUTHOR]