Volume 18, Issue 1

The objective of this work is to estimate the effects of political turnover on municipal health indicators. For this purpose, we used a regression discontinuity design applied to a pool of data on close municipal elections that occurred in 2008 and 2012. The outcome variables tested were municipal health indicators observed 1 and 3 years after the elections. We assume that the change of parties in the leadership of the municipal executive power alters the managers and public health policies and, therefore, affects the indicators of this sector. Our results indicate that health indicators linked to administrative issues appeared to benefit from political turnover in the period immediately after the elections (1 year). These results were corroborated by our additional estimates (robustness). We highlight that the number of health workers and coverage of primary care remained better 3 years after the elections in municipalities that suffered political turnover, indicating that this improvement resulting from the change in management may not be transient. Finally, through averaging tests, we found that in municipalities where the incumbent lost the election and there was a turnover, health managers were more qualified, and there was greater creation of basic health units.

The main objective of this article is to study the impact of trade liberalisation in Croatia as one of the first structural reforms being implemented, covering the period from 2000 to 2021. The panel data model was specified using a two-step system generalised method of moment estimator. The obtained results show that trade liberalisation measured through the specific indices encompassing a broader set of both tariff and non-tariff barriers, size of the trade sector, freedom of foreign exchange market, and capital controls positively impacted export performance. The results also showed a negative and statistically significant effect of remoteness on trade, confirming that geographical distance is an essential indicator of transportation costs. The coefficient for Schengen accession was positive and statistically significant, indicating that the Schengen agreement has boosted exports and that we can expect the same for Croatia, especially in the context of simultaneous accession to the Eurozone and the Schengen area on 1 January 2023.

This article uses a nonparametric production frontier approach to investigate the operating efficiency differences by the impacts of capital expenditure and business model in the global semiconductor industry. Handling the impact of capital expenditure as a fixed input by the directional distance estimator, this study compares the operating efficiencies in the global semiconductor industry between the integrated device manufacturers and the fabless and foundry firms over 1999–2018. The estimation results indicate that the operating efficiencies do vary in the semiconductor by the business model. The vertically integrated manufacturers dominate the semiconductor industry, and the capital-intensive manufacturers operate more efficiently than the asset-light fabless firms on average.

Drawing on nationwide data from the 2021 China General Social Survey, this study leverages ordered logit regression and propensity score matching to delve into the intricate relationship between commercial health insurance and subjective well-being among Chinese residents. The analysis unveils a robust, positive association between commercial health insurance and subjective well-being, with its impact most evident at intermediate levels of happiness. Notably, the study pinpoints a more pronounced effect among the middle-high income group. However, closer scrutiny of the marginal effects reveals a heightened need for commercial health insurance among low-income individuals, suggesting a potential avenue for policy intervention. In light of these findings, we recommend that the Chinese government actively foster the development of commercial health insurance, aligning it effectively with the existing system, to bolster the subjective well-being of the entire population.

Integrating big data analytics and machine learning algorithms has become increasingly important in the fast-changing landscape of stock market investment. The numerical findings showcase the tangible impact of our methodology on the accuracy and efficiency of stock market trend predictions. Identifying and selecting the most salient features (technical indicators) is critical in predicting the trend direction of exchange-traded funds (ETFs) in emerging markets, leveraging financial and economic indicators. Our methodology encompasses an array of statistical techniques strategically employed to identify critical technical indicators with significant implications for time series problems. We improve the efficacy of our model by performing systematic evaluations of statistical and machine learning methods across multiple sets of features or technical indicators, resulting in a more accurate trend prediction mechanism. Notably, our approach not only achieves a substantial reduction in the computational cost of the proposed neural network model by selecting only 5% of the total technical indicators for predicting ETF trends but also enhances the accuracy rate by approximately 2%.

As the main goal of economic development, inclusive growth (IG) is an important strategic measure to achieve common prosperity. Whether digital inclusive finance can make use of the advantages of scientific and technological innovation to promote IG is of great significance to promote the fairness, effectiveness, and inclusiveness of global development. Based on the panel data of 30 provinces in China from 2011 to 2021 (excluding Tibet, Hong Kong, Macao and Taiwan), this article first measures the IG index of China from three dimensions: sustainable economic growth, income distribution, and social equity. Subsequently, the article uses a series of mathematical statistical models to verify the transmission path and mechanism of the influence of digital inclusive finance on IG. The findings are as follows: (1) The level of IG in China shows a decreasing trend from east to middle to west, while the average annual growth rate of IG in the eastern region is obviously lower than that in the central and western regions; (2) digital inclusive finance has a significant promotion effect on IG, and digital inclusive finance in the central and western regions has a more obvious promotion effect on IG; (3) digital inclusive finance can achieve IG by increasing innovation activity and improving the level of human capital. Finally, based on the research conclusions, the article puts forward relevant policy suggestions, which provide reference value for formulating high-quality national development strategies and promoting high-quality economic development.

Taking advantage of Romania’s compulsory and unique presumptive tax system (turnover tax) for the smallest of the small- and medium-sized enterprises (SMEs), which unequivocally allows grouping the companies into two subsamples according to their tax status (the smallest of the SMEs, i.e. micro-companies for tax purposes, subject to alternative turnover tax vs larger SMEs subject to regular profit tax), the papers compute and compare three versions of firm-specific effective tax rates and brings first-time empirical evidence on the effects of turnover taxation on corporate tax burden. The results show not only that in Romania, the turnover tax is now the prevailing tax system, massively displacing the profit tax, but also it is more burdensome in terms of taxes actually paid relative to the firm size. Moreover, turnover taxation means that taxes are due irrespective of the profitability of a company, making the share of companies who paid taxes despite having losses much higher for micro-companies than for larger SMEs, which translates into higher public finance receipts, at the cost of lower tax equity. Since the corporate tax burden triggered by turnover taxation remains under the radar of even the most qualified policy reports, the results offer relevant lessons for other jurisdictions and have valuable policy implications.

This study attempts to quantify various macroeconomic variables’ influences on environmental quality. Being significantly negative in character, the impacts of economic growth (EG), energy consumption (EC) levels, financial development (FD) tools, changes in institutional qualities (IQUs), and globalization (GLO) on environmental degradation are regarded worthy of research. The dynamic panel model reports robust results using quantile regression and generalized moment method approaches. Renewable EC, GLO, and IQU significantly reduce the negative impacts on the natural environment across all quantiles. In contrast, electricity consumption has a significant deleterious impact across all quantiles, except at the 95% level, where the impact is not detrimental. EG and FD produce significantly positive injurious effects on the environment across all quantiles. Our findings reveal that callous indifference to global environmental degradation should not be tolerated for economic and ethical reasons, and it falls on the OECD countries to show leadership and take the initiative. OECD countries can mitigate environmental degradation by sharing knowledge and experience of balancing growth to promote sustainable development and responsible consumption globally.

We investigate how different approaches to the implementation of regulations affect compliance by evaluating the implementation of food hygiene standards in four English authorities. We draw upon regulatory theory in general together with specific literature on food hygiene standards to advance a new “compliance return” concept, which enables compliance outcomes and resource inputs to be considered jointly. We operationalize “compliance return” as part of a low-cost methodology for evaluating the relative effectiveness of different approaches to implementing food hygiene standards. We find that different approaches to implementation have significant impacts on food policy outcomes. Specifically, implementation ranged from a greater emphasis on deterrence to a greater emphasis on cooperation, with cooperation resulting in greater compliance and deterrence requiring less resource input. When compliance level is considered alongside resource input, to assess the overall “compliance return,” a stronger case emerges for a deterrence approach. The food policy implications are two-fold: (i) implementation affects policy outcomes, and (ii) the measure of “compliance return” developed and implemented in this article offers a low-cost approach to support the ex-post evaluation of implementation choices. The Compliance Return approach may be applicable more widely, especially where there is discretion regarding how regulations are to be implemented.

Digitalization is a crucial driver of enhanced energy system efficiency, facilitating the energy transition and offering a gateway for technology companies to enter the sector. This study, employing stochastic frontier analysis as the chosen methodology, investigates the efficiency challenges faced by companies venturing into the energy industry with a digital technology focus. Our empirical analysis reveals a positive correlation between higher total assets and increased research and development expenditures, signifying the vital importance of talent acquisition and securing R&D funding. Additionally, it is noteworthy that smaller companies experienced a more pronounced negative impact of COVID-19 on their efficiency. These findings contribute to refining digitalization strategies in the energy industry, emphasizing the role of efficiency from a corporate perspective.

We study the time-varying effects of Tobin’s q and cash flow on investment dynamics in the USA using a vector autoregression model with drifting parameters and stochastic volatilities estimated via Bayesian methods. We find a significant variation over time of the response of investment to shocks in both variables. The time-varying sensitivity of investment to a shock in Tobin’s q (cash flow) decreased (increased) since the early 1960s through the early 1980s, increased (decreased) since the early 1980s through the early 2000s, and it has decreased (increased) importantly again since then. Thus, the time-varying response of investment to a shock to Tobin’s q has been almost the mirror image to the time-varying response of investment to a shock to cash flow. This implies that Tobin’s q and cash flow represent both complementary and alternative sources of information needed to understand short-run investment behavior.

Science, technology, and innovation (STI) are fundamental elements for achieving development and sustainable growth. Developing STI is also currently the main challenge faced by countries, especially those with emerging economies, making it essential that these countries implement comprehensive policies to generate a knowledge-based society. Institutions and policy-makers are responsible for defining the rules by which a society is governed. In particular, it is widely held that institutions should place special emphasis on establishing property rights, legal systems, customs, and the political systems based on which central planners should govern. From this perspective, this study integrates qualitative and quantitative analysis through a preference model with three types of STI policy-makers and projections to better clarify the requirements for new STI institutions in Colombia. It does so by using the time series of STI spending generated by the Administrative Department of STI (Colciencias) from 1968 to 2018. The simulation results show that among the chosen agents, the STI policy preferences of the scientist-manager yield economic outcomes closest to the optimal level. In contrast, the yield of the politician’s preferences is suboptimal and even negative at certain points. These results have implications for public policy, as poor policy choices may lead to undesirable results in terms of STI policy application, implementation, and execution. These findings are important for strengthening STI policy. They also indicate the important role of the government in transforming Colombia from being a receiver of technology to being a generator of knowledge, technology, and innovation in line with the requirements of the country’s population and its natural resource endowments.

Controlling credit risk is crucial for maintaining financial stability, and the role of geographic factors in this regard is a significant concern for scholars and policymakers. Drawing on the concept of information asymmetry, we developed a theoretical model to analyze how geographic factors influence credit risk. Our theoretical proposition suggests that the spatial organization of banks affects the efficiency of collecting and processing soft information, ultimately impacting the credit risk. To test this proposition, we collected microdata from Chinese commercial banks spanning the period from 2011 to 2022. Employing a mediating effect model, we empirically examined the relationship between spatial organizational structure and credit risk. Our results indicate that the distance between bank operations and functional distance impedes the collection and processing of soft information, thereby exacerbating credit risk in banks. The study focuses on examining how the spatial organizational structure of Chinese commercial banks affects credit risk. By analyzing geographic factors and information asymmetry, the study aims to understand how the organization of banks influences the collection and processing of soft information, which in turn impacts the credit risk. Furthermore, our analysis of the sample reveals that the mediating role of soft information varies between state-owned banks and joint-stock banks due to their distinct customer profiles. On the basis of these findings, we propose several policy recommendations, including a focus on enhancing the collection and processing of soft information, promoting the growth of locally based small and medium-sized banks, and reducing information barriers within bank hierarchies.

Utilizing panel data from 30 large- and medium-sized cities in China from 2003 to 2018, this study employs the bootstrap method to investigate the heterogeneous impact of air pollution on housing prices and its underlying mechanisms. The research findings indicate that an increase in the concentration of various air pollutants has a restraining effect on housing prices, with the primary components of haze exerting a greater influence. Air pollution indirectly affects housing prices by influencing urban economic development and the development of the tertiary industry, with population density and the development of the secondary industry not serving as mediators in this relationship. Finally, policy recommendations are provided, including enhancing mandatory standards for energy consumption and waste gas emissions by enterprises, establishing a robust environmental monitoring system, and promoting industrial transformation and upgrading, aiming to provide insights for the stable development of the real estate industry.

The new technologies generated by the digital age are changing how individuals and societies communicate, learn, work, and manage. Although digital citizenship is defined as the behavioral norms regarding the use of technology in the most general sense, it also includes ethical and appropriate behaviors and being informed about this issue while using technology. However, there is a generational gap in digital information and literacy. In this context, the research analyzes digital citizenship according to gender, age, number of children, job position, and education level. In this analysis, which was structured as exploratory research to examine the digital citizenship status of participants, the descriptive model was preferred over quantitative research models. According to the analysis results, it can be said that the participants’ digital citizenship behavior is in the range of “I am undecided” (at a moderate level). Citizenship behavior with the lowest score was “political activism on the internet.” The groups with a significantly higher level of digital citizenship are women, those between the ages of 22 and 42 working as managers, and those at the graduate level.

In the framework of foreign direct investments (FDI) and green finance (GF), environmental regulations (ERs) and green technologies have influenced China’s green economic recovery between 2000 and 2020. The idea is to conduct an empirical investigation of the main drivers behind China’s green economic revival. Second-generation panel cointegration techniques were adopted for the analysis. The relationship among ERs, green technological investment, GF, and green economic recovery is supported by the long-run cointegration results. In this context, some of the most important measures include ER, GF, and increased investment in environmentally friendly technologies. The estimated results demonstrate a positive contribution towards the green economic recovery from GF, the development of green technologies, and ERs. Evidence suggests that green economic recovery is inversely associated with FDI. The establishment of an environmentally sustainable economy can be rendered by policy implications to continue advocating for the green shift in financial institutions along with enforcing the appropriate regulations and laws.

The coupled and coordinated development between economic growth and the utilization of resources and the environment in manufacturing enterprises holds great significance in achieving high-quality, efficient, and low-energy consumption goals, and promoting the green and sustainable development of the manufacturing industry. This paper proposes an evaluation indicator system for a manufacturing enterprise under the context of carbon peak and carbon neutrality goals and establishes an evaluation model and comprehensive evaluation index. By organizing the relevant data on the economic development and ecological environment of this manufacturing enterprise from 2016 to 2022 and calculating the weighting coefficients, coupling model, and coupling degree model, the coupling development level of the manufacturing enterprise was evaluated. Empirical results demonstrate that the coupled development between economic growth and the ecological environment in the enterprise is in a stable rising state, transitioning from a moderately imbalanced state to a barely coordinated level. The enterprise should continue to research coupling points, explore coupling control directions, formulate targeted policies, and strive to reach a level of high-quality and coordinated development.

The aim of this study is to assess the impact of various institutional variables on the long-term unemployment rate (LAPU) in the Colombian urban labor market. Vector autoregressive models are estimated using microdata from the Great Integrated Household Survey (GEIH), which has national coverage. Monthly data were analyzed for the 13 major capital cities and their metropolitan areas, as well as for the group of 11 intermediate capital cities, resulting in a total of 24 main urban labor markets in Colombia. The dataset includes unionized individuals, individuals with verbal and written contracts, non-labor income, unemployed individuals with subsidies, and individuals receiving severance payments. The results indicate that the growth in the number of unionized employees and non-labor income contributes to increasing the persistence in the duration of unemployment in Colombia. A key finding is that a positive growth in the ratio – gap between individuals with written contracts versus verbal contracts reduces LAPU. This provides evidence of how reducing information asymmetries in the Colombian labor market can improve labor market outcomes and contribute to medium- and long-term social welfare in Colombian urban centers.

Turkey has been suffering from a persistent deficit in its current account for decades. The connection between the oil price, the real exchange rate, the terms of trade, industrial production, the foreign direct investments, and the current account balance was investigated by using the structural vector autoregression (SVAR) model. The exchange rate and industrial production accounted for the largest explanatory shares in the balance, according to variance decomposition, although their impacts diminished with time. The balance was also strongly influenced by the terms of trade. In the first few months, the oil price had a weak influence on the balance. However, its impact gradually grew over the course of the following periods. The impulse response functions showed that the exchange rate, industrial production, and terms of trade had a positive and significant effect on the current account balance in the short-term. Furthermore, the SVAR model was applied to the time frame before the COVID-19 pandemic. The results indicated that although the basic findings for the exchange rate and industrial production remained unchanged, their explanatory significance for the current account balance decreased. As a consequence, the country must formulate fiscal and monetary strategies that are advantageous in mitigating the impact of these variables on the balance.

This study evaluates the impact of green finance (GF) on agricultural nonpoint source pollution (ANPSP) control and emission reduction in 30 Chinese provinces from 2005 to 2021. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of GF development and ANPSP. It employs a mediation effect model to empirically assess the pollution control efficacy of GF and to elucidate the mechanisms underlying its influence. The findings indicate that GF development significantly curtails ANPSP emissions. It achieves this through government environmental regulation (ER) and land transfer mechanisms. Heterogeneity test results show that GF has a stronger impact on ANPSP in regions with lower economic development level and GF reform policies. Therefore, the study suggests strengthening the GF infrastructure in rural areas, aligning GF policies with ER, promoting large-scale land operations, and implementing tailored strategies for regions with different levels of economic development and GF reform policies.

Regional trade network relationships are not only a medium for transmitting shocks to value chains, but also an important vehicle for reconfiguring value chains. This article applies social network analysis, combines regional trade agreements, and describes the current status of regional trade network development in North America, the European Union, and “the Belt and Road,” as well as the individual characteristics of China’s service industry in each regional trade network by using the indicators of trade relations to construct the network density, connectedness, and centrality through clustering analysis. The research found that the complexity of regional trade network relationships has increased over the years. Additionally, the developing trend of equalization is accelerating but still exhibits a “Core-Periphery” structure. Additionally, subregional groups within the regional trade network are increasingly obvious. Further study on the impact of industry regional centrality on value chain resilience revealed that the increase of degree centrality and the betweenness centrality of China’s service industry can conspicuously strengthen value chain resilience in regional trade network relationships. Moreover, balanced regional trade network relationships can reinforce this effect. In contrast, the degree of the polarity of regional trade network relationships exacerbates the weakening motivation of centrality for value chain resilience.

Marriage-related migration by women from less developed to high-income nations has been increasing rapidly in Asia, and the proportion of children born to these foreign-born women is increasing as well. Using a unique dataset based on the Korean Youth Risk Behavior Survey, we investigate the relationship between social support and suicidal ideation among children with foreign-born mothers. We find that both maternal advice and teachers’ advice are significantly associated with a reduction in suicidal ideation among these children, with a further decrease of 3.2 and 4.9% points, respectively, compared to children with native Korean mothers. These findings imply that more government attention should be focused on educating and training foreign-born mothers and schoolteachers to improve their capacity to advise these children.

This article aims to model Keynesian involuntary unemployment within the framework of Walrasian general equilibrium, incorporating an additional hypothesis: the possibility of credit restrictions by banks or financial intermediaries. Keynes’s theory of involuntary unemployment is based on the rejection of the “second classical postulate,” which leads to the idea that entrepreneurs alone decide the level of employment. This is what some Keynesians call the asymmetry of the wage relationship. The result is a restriction of Walras’ law, which no longer includes the labor market. However, the problem is that entrepreneurs themselves may find themselves constrained by financial intermediaries. If there is a second type of asymmetry, i.e., between banks/financial intermediaries and entrepreneurs, Walras’ law will be doubly restricted since it will also exclude the credit market. I set out to identify the consequences of the properties of involuntary unemployment in a general equilibrium model.

The dynamic nature of security risks confronting the global aviation industry necessitates a deeper understanding of the factors influencing job-crafting behaviors among airport security officers. Grounded in the Job Demands-Resources model, this study aims to investigate the impact of perceived organizational support, work engagement, and social courage (SC) on job crafting among airport security officers. Data were collected from 379 airport security officers at Antalya Airport, Turkey’s busiest airport, through face-to-face surveys conducted between January and December 2022. The findings indicate that organizational support positively influences job crafting, with work engagement mediating this relationship. Furthermore, SC moderates both the relationship between work engagement and job crafting and the strength of the mediating effect of work engagement on the relationship between organizational support and job crafting. This study addresses gaps in the literature by providing a detailed framework that illustrates how organizational support, work engagement, and SC impact job crafting among airport security officers in a non-Western context. Promoting organizational support and fostering employees’ SC can enhance job-crafting behaviors, leading to a more adaptable and resilient workforce capable of addressing the evolving security challenges in the aviation industry.

Industrial structure adjustment is a process that involves reconfiguring input factors to enhance productivity and efficiency. A crucial aspect of this adjustment is the transformation of the labor force composition, which subsequently affects the employment structure. Consequently, variations in productivity levels lead to the cross-departmental migration of workers, resulting in changes to the income disparities among employees. This article examines the impact of China’s industrial structure adjustment and labor mobility on the urban–rural income gap between 1990 and 2019. Employing an empirical approach, the study investigates the influence of industrial structure adjustment and the direction of labor mobility on the urban–rural income gap. The findings indicate that the quality of industrial structure adjustment contributes to the widening of the urban–rural income gap, whereas labor mobility helps to narrow it. In regions experiencing a net influx of labor, superior industrial structure adjustment amplifies the urban–rural income gap, whereas labor migration diminishes it. Conversely, in regions witnessing a net outflow of labor, superior industrial structure adjustment reduces the income gap, while labor mobility widens the urban–rural income disparity. Notably, in more developed areas of China, the quality of industrial structure adjustment has widened the income gap, whereas labor mobility has narrowed the urban–rural income gap. However, in less developed regions, labor mobility exacerbates the urban–rural income gap.

This article is concerned with emergency material relief in response to major emergencies, concentrating on the difficulties in locating emergency logistics facilities and deploying emergency supplies. Using discrete scenarios, we describe the uncertainty of the demand for emergency supplies at the catastrophe site and the uncertainty of the cost and timing of the shipment of such supplies. Meanwhile, we consider two key objectives, i.e. emergency relief cost and time, and build a multi-objective emergency logistics centre siting model, including deterministic and robust optimization models. In the construction of the siting model, due to the time urgency of emergency logistics, we adopt a bi-objective function, including transportation and transportation time, and consider the construction and inventory costs of the emergency logistics centre. We also introduced a generalized hybrid frog-hopping algorithm to encode facilities that provide emergency material relief services. To verify the effectiveness of the models and algorithms, we designed a multi-scenario simulation experiment, and the results show that the two models and algorithms we propose have good feasibility and effectiveness. The robust optimization model performs well in handling various uncertainties.

This study examined the stock market volatility of the member states of the South Asian Association for Regional Cooperation (SAARC) and geopolitical risk (GPR). The analysis period covered January 2014 to March 2024, and the time–frequency wavelet method was used to process the data. The time-varying parameter vector autoregression and spatial autoregressive models helped determine the dynamic connectedness of volatility in the analyzed states. The findings revealed similar stock market connections in Bangladesh and India. In addition, a comparative analysis of stocks in India and Pakistan led to the identification of common elements. The connection between geopolitical concerns and Sri Lankan stocks was the strongest and increased in intensity after 2019. GPR and Nepal’s stock market maintained a continuous but low-intensity relationship. The dynamic connectedness between member states’ stock markets was limited during the review period. The study results could encourage SAARC governments to bridge their political differences to ensure that South Asia becomes a strong partner in the global economy. Equally, our results can benefit investors, financial institutions, regulatory authorities, and governments.

The financial setbacks of the sub-Saharan African regions have necessitated this study. The study assesses the role of trade openness and foreign direct investment (FDI) in driving tax revenue growth in sub-Saharan Africa. The major parameter representing the dependent variable is tax revenue growth while the independent factors include trade openness, exports, imports, and GDP as a moderating element. The research runs from 1990 to 2022 and employs the vector error correction model (VECM), with a unit root test that yields order one, while the Johansen co-integration test shows a long-run connection for all variables. The VECM results suggest that the long-run disequilibrium is corrected at a positive rate of 8.5%. It is also noteworthy that a percentage change in trade openness would result in a 13.7% decrease in tax income, but fluctuations in all other parameters except GDP will enhance tax revenue growth in sub-Saharan Africa. The test for the impact of all criteria on tax revenue growth yields minor results; however, tax revenue growth initiatives in Sub-Saharan Africa have a negative short- term effect on FDI but a favourable long-term impact. In addition, trade openness has a negative effect on GDP and exports in the short run, while imports have a negative impact on foreign investment. The policy implications include that bilateral trade policies will need to be reviewed, with an emphasis on exports, economic growth, and tax collection schemes. In addition, the governments in sub-Saharan African regions are encouraged to enact tax policies that would engender the inflow of overseas investment.

Whether government expenditure in elderly-care institutions can improve the efficiency of care in elderly-care institutions is not only related to the realisation of “Care for the Elderly,” but also one of the concerns of policymakers and implementers. In order to explore the impact of government expenditure on the efficiency of care in elderly-care institutions, DEA-Tobit two-stage model is used with a sample of 50 elderly-care institutions in Wuhan. Firstly, the efficiency of institutional elderly-care service is evaluated using the DEA method. Secondly, the Tobit regression model is employed to examine the effect of government expenditure on the efficiency of institutional elderly-care service. The research finds that government direct investment has no significant effect on the efficiency of care in elderly-care institutions, while fiscal subsidies can effectively improve it. Under the condition of controlling other variables, for every 10,000 yuan increase in fiscal subsidies, the value of comprehensive technical efficiency and pure technical efficiency of elderly-care institutions increase by 0.244 and 0.181, respectively. Therefore, to improve the efficiency of care in elderly-care institutions, fiscal subsidies should be chosen more often to purchase care in elderly-care institutions, and through it to guide the social forces to enter the field of nursing services.

This article examines the relationship between media tone and earnings management before the earnings announcement. Using data from China, I discover a positive relationship between media tone before the earnings announcement and earnings management. For companies whose managers sell their ownership in the next time and have prior media exposure, the beneficial relationship is more pronounced. The relationship between abnormal media tone before results declaration and earnings management appears to be weakened by external oversight. According to the findings, managers can use media slant in addition to earnings management as a way to increase stock price following an earnings announcement. I also find that managers may use two public channels to influence media tone before the earnings announcement: the distribution of preliminary accounting data and earnings pre-announcement. Our study expands on the media’s involvement in earnings management and offers proof of the concurrent use of multiple management techniques to artificially raise stock prices.

One of the most outstanding accomplishments of the economic science over the last decades is the development of a sound and coherent theory of economic growth. Research in growth theory has demonstrated that significant and systematic increases in well-being are attainable whenever the right formula is implemented. When combined with efficiency, the ingredients of this formula – innovation, the diffusion of ideas, and human capital accumulation – can drive the economy toward a virtuous path of sustained growth. Notwithstanding, this is an overly optimistic view of growth that does not account for the many obstacles that the creation of wealth may encounter. The current essay surveys cutting-edge research on growth theory to conclude in favor of a paradigm shift: the main concern is no longer just with how to correctly combine production inputs, but with how their efficient use is eventually hampered by a large collection of worldwide risks and threats. Global risks come in many shapes (they can be classified as economic, environmental, geopolitical, societal, and technological) but, in any case, they call for a reexamination of growth theory.

Efficiency and productivity analysis have been critical in healthcare and economics literature. Despite the tremendous innovation in methodology and data availability, a comprehensive literature review on this topic has not been conducted recently. This article provides a three-part literature review of healthcare efficiency and productivity studies. It begins by reviewing the two primary empirical methods used in healthcare efficiency studies, emphasising the treatment of inefficiency persistence. Second, previous contributions to healthcare productivity research are discussed with a focus on methodology and findings. In the third section, various measures of outputs, inputs, and prices in health literature are explored to determine the extent of consensus in the literature. On the methodological front, the literature review shows that while the Data Envelopment Analysis and the Stochastic Frontier Analysis have been used extensively in healthcare productivity and efficiency studies, their application in the context of longitudinal data is limited. Further, no study currently undertakes to measure the TFP changes and its components that use both primal and dual approaches. There is also a considerable variation in the use of inputs, outputs, and price variables, suggesting that the use of variables in healthcare productivity and efficiency literature rests on the balance between data availability and the research scope.

This article is the first of a series that offers a new paradigm for economics, the “multilevel paradigm,” using generalized Darwinism as its theoretical framework. Generalized Darwinism refers to all processes that combine the ingredients of variation, selection, and replication – not just genetic evolution – making it relevant to the cultural evolution of economic systems that are embedded in political, social, and environmental systems. We contrast the multilevel paradigm with the neoclassical paradigm and other schools of economic thought. The multilevel paradigm, like the neoclassical paradigm, provides an integrative framework for micro- and macro-economics. It also incorporates the meso level, comprising groups of various sizes, linking micro and macro. Other schools of economic thought are not fully integrative in this sense, constituting instead a form of diffuse pluralism. In the integrative framework of the multilevel paradigm, many important ideas that are currently on the periphery of economics are brought toward the core.

This contemporary-based essay provides a case for a life course approach to financial literacy education that offers a pathway for individuals to enter their later years with improved levels of financial security and savings. Financial security in older age is seen as an important factor in promoting social connectedness with implications for (a) health and well-being in later life and (b) reducing social isolation and loneliness among older people. Financial literacy is presented within the context of an ongoing life skill requiring early habit building with the need for episodic updating in light of new information and changing life circumstances. Embedding financial literacy education within a life course framework is seen as offering the opportunity to gain economic empowerment leading to long-term financial stability and independence. The notion of economic empowerment is considered as an equal right for both men and women and warrants attention by educators and policymakers to address gender-related disparities in economic security. The increasing numbers of people expected to live longer lives will create an urgent need for early planning actions to achieve financial security in older age.

Ethnic entrepreneurship is the phenomenon that individuals from certain ethnic backgrounds participate in entrepreneurial activities. Individuals from those ethnic backgrounds are affected by various factors such as entrepreneurial tendencies, cultural values, social networks, and economic opportunities. Although such factors have the potential to explain ethnic entrepreneurship, it is important to continually update research and definitions in this field as ethnic entrepreneurship is an ever-growing topic. The purpose of this article is to be a part of the studies on ethnic entrepreneurship and to contribute to the field by identifying the conditions that influence the entrepreneurial process of Meskhetian Turks, who immigrated to the USA under special laws and became successful in establishing their own businesses. In order to achieve this purpose, in-depth interviews were conducted with entrepreneurs, and the findings are discussed in the context of the interactional model. According to the findings, social, capital, and ethnic networks play a significant role in business establishment and development, knowledge acquisition for business, and labor supply. In addition, due to the differences in the migration experiences of entrepreneurs, it is seen that both ethnic and non-ethnic networks affect entrepreneurship in the dimension of resource mobility. This study confirms the need to evaluate the benefits of ethnic entrepreneurship as a dynamic field.

Previous research has investigated some of the relationships among brand parity (BP), brand image (BI), brand satisfaction (BS), and brand loyalty (BL). However, there is not a model investigating all of them in a single conceptual model. Based on information processing theory, the first aim of the article is to investigate the important, direct effect relationships among these variables. Moreover, scant information is available regarding the moderating role of BP. Consequently, the second purpose of the study is to test the moderating effect of BP regarding the effect of BI on BL and BS on BL. The data were collected from customers of chain stores in Antalya. The results supported the positive, direct effect of BI on BS and BL, plus BS on BL. There were no direct and moderating effects of BP. Based on the microeconomic behavior of consumers and because of the bounded rationality of consumers, as BP rises, consumers’ decisions are not affected regarding the effect of BI on BL and BS on BL. This is an important insight that brands should consider in terms of points of parity associations that affect consumers’ decision-making process and create a brand-loyal customer base.

This study examines the impact of Green Human Resources Management (GHRM) in the textile and fashion industry on corporate sustainability (CS). In addition, the indirect effects of organizations’ environmental performance (EP) and employee commitment to CS are also examined. The research was carried out with the participation of the employees ( n = 129) of Turkish organizations operating in the fashion and textile sector that adopt a sustainable business management approach. Structural equation modeling via smart PLS was used for data analysis. The findings showed that GHRM positively affects CS, and EP mediates this relationship. However, employee commitment did not have a mediating effect in this relationship. These results provide CS experts and managers with evidence-based insights into designing GHRM practices and sustainability strategies.

High-quality development of sports competition performance industry in the Chengdu-Chongqing region is based on the improvement of the sports event system and the strengthening of market entities, and it promotes economic and social development by extending the industrial chain and enhancing the value chain. To effectively evaluate its development effect and promote high-quality development, based on the analysis of the opportunities and challenges faced by the sports competition performance industry in the Chengdu-Chongqing region, this article uses the literature research method and survey interview method to construct a performance evaluation index system from the perspective of performance evaluation theory. It also analyzes the strategic thinking of high-quality development from three aspects: guiding ideology, main principles, and development goals, and explores the strategic path for high-quality development from five aspects: integrated development of points and surfaces, priority development between regions, conditional step-by-step development within the region, selective key development of small cities, and layered development with emphasis on different stages.

Effective coordination of urbanization dynamics and influencing factors is crucial for achieving balanced development. This study analyzes urbanization, economic, and environmental data of the Yangtze River Economic Belt from 2005 to 2019 using the Coupling Degree model and Entropy Method with the Analytic Hierarchy Process, focusing on the coordination between urbanization development, economic development, and environmental protection (EP). It highlights the significance of government interventions and the necessary level of government engagement in environmental matters for harmonized development across regions. Findings show that higher urbanization and economic levels align with robust environmental safeguards facilitated by active government interventions. Conversely, lower levels may lead to reduced EP, influenced by government actions. The study enhances understanding of the interplay between urbanization, economic growth, and environmental conservation, underlining the government’s role in coordination. Different development stages reveal the importance of government environmental interventions for policy formulation. Introducing the relatively unexplored variable of government policy, the study establishes a comprehensive framework for interventions, enriching coordination analysis and insights.

Environmental protection is closely related to high-quality economic developments. Based on the matching of micro-databases from 2000 to 2008, this study used the “Regulations on the Collection and Use of Pollution Fees” policy implemented in China in 2003 as the exogenous impact to construct the intensity Difference in Differences model in order to investigate the effects of pollutant discharge fee on technological innovation of enterprises and the underlying mechanisms. The results showed that governmental environmental regulations significantly improved the level of technological innovation of enterprises, and the conclusion was still valid after a series of robustness tests. The results of the parallel trend verified the rationality of the differential setting and the dynamic effects showed that the pollutant discharge fees had a continuous promoting effect on the technological innovation of enterprises. The results of the placebo tests rejected the original hypothesis of the mistaken model. The mechanism verifications revealed that the strengthening of environmental regulation by the government acted on the innovation level of enterprises through the two mechanisms, i.e., the promotion of enterprises’ fixed asset investments and government subsidies, and finally improved the enterprises’ technological innovation levels.

This article empirically examines the “responsibility-performance” interaction in the process of environmental social responsibility decision-making by enterprises, using the listed companies in the heavy pollution industry as the research sample. We also use the evolutionary game theory to construct a system dynamics model for simulation analysis to explore the evolutionary qualities of the environmental responsibility behavior of heavily polluting enterprises and its relationship with financial performance under the influence of local government environmental regulation. It finds that for environmental social responsibility, a company's expected performance surplus is more likely to promote positive interaction between responsibility and performance than a gap; for regions with relatively negative environmental strategies, environmental social responsibility provides more efficient reputation transformation and motivates companies to invest in it; more competitive industries and enterprises with forward-looking environmental strategies tend to put performance feedback into the undertaking of environmental social responsibility. Therefore, the government and regulatory authorities should urge heavy polluting enterprises to implement environmental responsibility, so that they can fully realize that emission reduction and growth can be achieved at the same time and promote the financial performance of enterprises and environmental responsibility while forming a win–win situation at the ecological level and corporate social level.

With the development of network technology, electronic money as a payment and settlement tool based on the network has been developing at an unprecedented speed. Based on the background of e-commerce, this study uses the data from June 2012 to June 2022 to establish a vector autoregressive model to study the interaction between oil prices, stock performance, and money supply. Such a model can not only further our understanding of the complex relationship between these important variables but also shed light on future oil prices. Granger causality test, impulse response function analysis, and variance decomposition analysis have been applied to variables in the model. The main finding is that oil price responds to changes in stock performance and money supply, stock performance is affected by both oil price and money supply, and changes in money supply can be explained by stock performance fluctuations. Such a relationship can help inform traders in e-commerce and investment banking to generate better predictions of future oil prices.

The impact of logistics performance in the era of sustainable mobility on the overall economic development of a country is inevitable. It can even be said to represent an extremely important component in identifying economic conditions and provides the possibility of defining adequate strategies. In this article, the evaluation of the member countries of the European Union was carried out on the basis of the logistics performance index (LPI) according to the latest report of the World Bank (WB). A unique and original Multiple-Criteria Decision Making (MCDM) approach has been created, and it involves the application of four methods: Criteria Importance Through Intercriteria Correlation, Method based on the Removal Effects of Criteria, and Entropy and Fuzzy ROV (Range of Value). The weighting coefficients of six factors were obtained with the first three methods in crisp form, so they were converted into Triangular Fuzzy Number. The Fuzzy ROV method has been created for the first time in the literature and represents a great contribution from the methodological aspect. The results of the developed model and the applied steps show that there are certain differences in the rankings compared to the World Bank report, with a note that the best-ranked countries have maintained their positions. In addition, verification tests of the originally obtained results were created, with an emphasis on the importance of evaluation parameter values and their impact on the LPI ranking.

Due to the impact of the Coronavirus disease 2019 (COVID-19) epidemic isolation, the E-commerce market encountered great impact and changes. Faced with such a transformed situation, E-Commerce administrative managers usually have different individual personalities and transformational leadership to enhance leadership self-efficacy and organizational commitment. The purpose of this study is to investigate the interrelationships among the personality, transformational leadership, leadership self-efficacy, and commitment of E-Commerce administrative managers. The research population is randomly selected from E-Commerce administrative managers who are responsible for E-Commerce administrative affairs. Based on a sample of 408 participants, the Confirmatory Factor Analysis (CFA) is applied to examine the construct validity. Then, the Structured Equation Modelling (SEM) method is used to estimate a series of interrelated dependent relationships and perform a comprehensive model. The research results show that a leader with Big Five personality traits has a positive influence on transformational leadership and leadership self-efficacy. An E-Commerce administrative manager with transformational leadership behaviours has a positive influence on leadership self-efficacy. In addition, an E-Commerce administrative manager with leadership self-efficacy has a positive influence on commitment. The research results contribute to a better evaluation model of E-Commerce administrative manager’s leadership by applying their personalities and transformational leadership to enhance leadership self-efficacy and increase the level of organizational commitment.

This article conducts an exploration of culture’s roles on entrepreneurial activities among countries at different economic stages. Culture factors from the GLOBE are converted into motivation culture and hygiene culture. The framework of this research is derived from Herzberg’s motivation hygiene theory. Total entrepreneurial activity (TEA) and the ratio of opportunity-driven to necessity-driven entrepreneurship (OPP/NEC) from global entrepreneurship monitor (GEM) are taken as measures for both the quantity and quality of entrepreneurial behavior. Panel regression with fixed effects is employed to test both direct and interaction effects. The direct relations show that the improvement of economic conditions discourages the TEA, while promotes the OPP/NEC; motivation culture significantly promotes OPP/NEC, while makes no difference in TEA; and hygiene culture significantly promotes TEA, while reduces OPP/NEC. In terms of interactive effects, motivation culture reduces the containment of economic development on TEA, yet hygiene culture hurts its promotion on OPP/NEC; motivation culture mitigates hygiene culture’s stimulation of TEA, while hygiene culture damages motivation culture’s fostering of OPP/NEC. Finally, a global culture map of quadrant distribution diagram is visually drawn with the corresponding culture scores. Notably, countries or regions in quadrant IV with high motivation culture and low hygiene culture demonstrate the most favorable entrepreneurial quality as well as high economic development.

In this article, an economic natural experiment in China is analyzed and the performance evaluation of the economic relocation effect of informal environmental regulations imposed through the channel of environmental nongovernmental organizations (ENGOs) is suggested. ENGOs are found to have a significantly negative impact on the overall economic location quotient of six major polluting industries. The economic relocation effect from ENGOs exhibits industrial and regional heterogeneity, as the effect is stronger among easy-to-relocate industries and in market-oriented areas. The underlying mechanism is the ENGOs, as informal environmental regulations could motivate firms to enhance their investment in environmental governance, ultimately crowding out these economically polluting industries.

In the Internet intelligent teaching platform, students’ demand for English cultural content is increasingly obvious. To help students quickly locate the overall content of resources in online autonomous learning, this study constructs a video annotation model for online teaching. This method classifies text by designing an optimized Bidirectional Encoder Representation from the Transformers model and designs a Text Rank keyword extraction model that integrates external knowledge and semantic feature weights. The extraction of knowledge points contained in audio and video resources can be realized. In the experimental data set, a relatively complete video content summary could be obtained by combining the first three sentences with the last two sentences. The F1 value of the classification model was up to 91.3%. In addition, the BERT-T model proposed in this article had the best effect on the experiment. Compared with the original BERT model, the macro-F1 was 0.8% higher and 0.5% higher than the Ro BERTA model. In the keyword extraction experiment, B-Text Rank was 2.19 and 2.85% higher than the traditional Text Rank in the two datasets. The experiment shows that the BERT-Text Rank network resource annotation model has excellent application performance in English online autonomous teaching and could guide students to learn.

A multi-level perspective of how environmental regulations affect firm performance receives less attention. By document analysis and semi-structured interviews, this study examines the influences of environmental regulations from the central government, the local governments, and the corporate on firm performance with a case study based on a state-owned enterprise named SINOPEC Yangzi Petrochemical Company (SYPC) in China. The findings show that the regulations from different levels exert distinct but interconnected influences on performance of SYPC. The guiding concepts from the central government, the prescriptive standards from the local governments, and the performance management systems from the SINOPEC headquarters incentivize SYPC to innovate green technology. Technical upgrade, good relations with the government, and popularity in the public due to the implementation of regulations improve economic performance of SYPC, though which is not the priority of the company. SYPC actively implements environmental policies but also acts as a part of the strategy of SINOPEC circumventing regulations.

Facing the ethnic-cultural conflict in the relocation process and to promote ethnic-cultural integration, this study takes the ethnic interlocking communities in Guizhou Province as an example and uses research methods such as interviews and observation to collect data. The study also analyzes the influencing factors of cultural integration through regression analysis and proposes relevant measures. The results of regression analysis showed that cultural belonging, social support, community participation, and linguistic communication skills had a significant positive effect on ethnocultural integration while existing interpersonal relationships and identity had a significant negative effect on ethnocultural integration, with interpersonal relationships having the greatest effect on immigrants’ cultural integration, with a correlation coefficient of −0.260, followed by was the sense of cultural belonging with a regression coefficient of 0.240. Social support, language communication skills, community involvement, and identity were slightly less influential. Overall, the satisfaction levels after the migration were higher than before. Among them, the average satisfaction of immigrants with their income status before migration was 1.15. By building cultural exchange platforms and conducting targeted vocational training, new ideas have been provided for the integration of ethnic cultures in ethnic fusion communities. Research methods can effectively alleviate ethnic-cultural conflicts and promote ethnic-cultural integration. Graphical abstract The study explored the cultural integration path in ethnic-embedded immigrant communities. During the relocation process, there are differences in the values of different influencing factors of cultural integration, and the values of existing interpersonal relationships and identity gradually decrease. In June 2021, the value of identity recognition was 3.76.

Energy consumption, which works as the physical engine of economic development, significantly influences the environment; using renewable energy, which originates from naturally filled resources, helps mitigate these adverse effects. The high price of fossil fuels, carbon dioxide emissions, and electricity generation are the most difficult aspects of this kind of renewable energy, which is often regarded as one of the main factors holding back economic progress. An artificial neural network-enabled economic growth model (ANN-EGM) has been constructed in this research to predict the restraining and pushing energy variables that impede economic growth. ANN-EGM optimizes the limiting and driving energy forces, which helps to improve the use of renewable energy and assist the economy’s growth. The prominent enhancement in driving economic activity and employment rates may result in cost-effective improvement for the effective production of energy from renewable resources for green computing. The proposed article constructs an ANN-EGM and examines its topological structure and the effect of training errors the network allows on its performance to address issues in green computing technology and sustainable social and economic development. Conventional approaches based on liming and driving energy forces are predicted to be less successful than an ANN-EGM in predicting the increase of the renewable energy industry for green computing and its correlation with quicker economic growth. The study’s findings suggest that the ANN-EGM can accurately forecast and verify the limiting and driving factors in renewable energy generation. The experimental outcome demonstrates that the proposed ANN-EGM model increases the prediction ratio by 85.6% and the performance ratio by 86.4% and has a reduced mean square error rate of 10.1% compared to other existing methods.

Population aging has become a significant issue faced by major global economies. The rapid urbanisation process in China has led to a higher rate of aging in rural areas compared to urban areas. Existing studies have predominantly focused on the impact of aging on agricultural economics and production, with limited exploration into its effects on large-scale agriculture. Given the importance of large-scale agriculture as a crucial initiative for expanding agricultural investment and increasing land capital accumulation, it is pertinent to further investigate the implications of aging in this sector. The empirical examination of the impact of population aging on large-scale agriculture holds significant relevance for developing countries globally. These nations face dual challenges: an aging population and the need for agricultural modernisation. Research can aid in formulating targeted policies to address labour shortages, agricultural technological innovation, and market dynamics, thereby promoting sustainable development and food security. This study utilises data from China’s Third National Agricultural Census and employs measurements of aging at both the rural household and county-city dimensions. The analysis includes an examination of the moderating effects of per capita arable land area and the level of mechanisation on the impact of aging on large-scale agriculture. The findings of the study are as follows: (1) From the perspective of rural households, aging leads to an increase in the age of the rural labour force, resulting in a significant decrease in the probability of rural households participating in large-scale agriculture. (2) At the county-city level, aging contributes to a decline in the proportion of the population participating in large-scale agriculture, which is detrimental to the development of this sector. (3) In certain circumstances, the negative impact of aging on large-scale agriculture is exacerbated in areas with higher levels of agricultural mechanisation.

Growing consciousness about sustainability and the onset of the digital era have affected the corporate environmental, social, and governance (ESG) outcomes to the forefront of new debates. This article attempts to use statistical analysis software such as Stata to explore relevant issues. A-share listed firms in Shanghai and Shenzhen between 2006 and 2022 are examined in this research for the sake of exploring how digitalization in businesses affects ESG ratings. Findings suggest a positive correlation between digital initiatives and ESG metrics improvements. These findings are consistent even after multiple robustness checks. Additionally, this article delves into the mechanisms driving this primary relationship, discovering that digital advances in firms enhance ESG scores by easing financial limitations, diminishing information disparities, and bolstering technological innovation. This article further reveals variable impacts based on firm size and geographical location.

This study examines the impact of accounting comparability on firms’ labor productivity. Using a panel data of China’s A-share listed firms from 2011 to 2022, we find that accounting comparability is positively related to firms’ labor productivity, and with every one-unit increase in the level of accounting comparability, the firms’ labor productivity is expected to increase by 2.97 units. In the mechanism analysis, we find that promoting human capital accumulation and reducing agency cost are crucial channels through which accounting comparability improves firms’ labor productivity. In additional analysis, we find that the positive effect of accounting comparability on labor productivity is more pronounced when firms have higher financing constraints and lower levels of corporate governance, and their peer firms have stronger accounting information quality. Our findings add to the body of knowledge regarding the determinants of labor productivity, and the labor-economic consequences of accounting comparability, and provide firms with evidence-based insights into improving their labor productivity.

This article introduces the basic concept of the Regional Comprehensive Economic Partnership (RCEP) agreement and constructs the GTAP model based on the analysis of the current development status of the textile industry in China, the Chinese textile industry as the research object, and the simulation forecast RCEP agreement tariff reduction. The data come from gtap10 database, respectively setting regional tariff reduction to 60% and regional tariff reduction to 100% realize free trade two situations for empirical research. The reduction of tariffs has reduced the cost of international textiles, leading to an increase in imports from China. At the same time, the reduction of tariffs has reduced the export price of Chinese textiles, thereby increasing China’s exports. However, the reduction of non-tariff barriers may increase the export price of Chinese textile products to a certain extent, thereby limiting the output of China’s textile industry. Finally, RCEP has a positive impact on the development of China’s textile industry, on the basis of the future development countermeasures of the Chinese textile industry.

This study employs balanced panel data encompassing Shanghai and Shenzhen A-share listed companies in China spanning the period from 2014 to 2021 as a research dataset, employing a mixed OLS regression model and Bootstrap methods, to investigate the influence mechanism of the overseas background and risk preference tendencies of top executives on the corporate green innovation output. The results show that the top executives’ overseas experience has a substantial influence on corporate green innovation, while top executives’ risk preference level plays a mediating role. In addition, the educational background of top executives is identified as a moderating element in this mechanism. Thus, the conclusions of this article extend prior research by identifying a new connection between top executives’ overseas backgrounds and corporate green innovation and by offering a new perspective to advance and improve the green economy.

Classical inventory models (IM) serve as quantitative tools for determining the optimal order quantities, timing of orders, and safety stock levels for specific inventory items or item groups. Zadeh (1965. Fuzzy sets. Information and Control, 8 , 338–353) introduced fuzzy theory and Dubois and Parade (1988. Fuzzy logic in expert systems: The role of uncertainty management. Fuzzy Sets and Systems, 28 , 3–17) presented the study of fuzzy inventory model, which, however, exhibits limitations in effectively handling uncertainty, inaccuracies, and imprecise data. In 1999, Smarandache presented the idea of neutrosophic set theory to handle uncertainty. Using trapezoidal neutrosophic numbers, this study extends the idea of neutrosophic sets to inventory management, concentrating on resolving the uncertainty associated with holding costs, ordering costs, and shortage costs. First time within the literature of the neutrosophic set, our new method not only addresses existing problems but can also tackle other issues that no other authors have successfully resolved so far. Additionally, we conduct a comparative analysis of our proposed model against existing models in this article. Based on this comparative study, our findings assert the superior performance of our proposed model in relation to some of the existing models. In conclusion, we wrap up our research by presenting graphical, logical, and tabular comparisons with the existing methods.

Given the inclusiveness of digital inclusive finance (DFI) and its complex impact mechanism on the labor economy, this study uses the characteristics of adaptive and self-learning ability of artificial neural network (ANN) to simulate the process of delivering stimuli to nerve cells in the human brain through linear weighted summarization and functional mapping, and implement the optimization learning algorithm to adjust the weights in the network structure, thus completing the hierarchical analysis of index weight. At the same time, the neural network structure is used to approach the greatest extent and Garson algorithm is used for sensitivity analysis. We use data on the labor economy and digital financial inclusion in Heilongjiang, Jilin, and Liaoning provinces in China from 2011 to 2021 as a training dataset. The study found that (1) the indexes of DFI have different importance to the indexes of labor economy, among which the most important are the number and amount of insurance per capita and the proportion of the number and amount paid by digital technology, which have a normalized importance of 100 and 99.3%, further, R -square coverage is above 0.95, respectively, for labor economy indicators; (2) For different subdivided indicators, the indexes of DFI determine different significance. This study employs tools and policies related to DFI to address labor economy challenges, so as to promote the overall economic construction. This study studies the response and transmission mechanism of the concept of DFI to the labor economy, and explores the labor economy problems such as improving labor productivity and labor mismatch in the economy under its “inclusive” principle. Compared with the traditional weight analysis, it is closer to the real situation and has a stronger ability to fit the reality. In the future, the model could be rebased and measured against absolute indicators and a wider dataset could be adopted for extension to more areas.

“Asset pricing” in the context of financial economics pertains to the investigation and formulation of two fundamental pricing ideas and the models that go along with them. Various models exist for different scenarios, but they can be traced back to either general equilibrium asset pricing or rational asset pricing. Asset pricing models, as the name suggests, serve as valuable tools to assess the value of assets. The general equilibrium theory states that supply and demand interact to determine market prices. In this context, asset prices collectively satisfy the market clearing condition, which dictates that the supply and demand for each asset are equal at the prevailing price. Another crucial aspect of financial planning is portfolio management (PM), which aims to maximise investment profits while minimising losses. PM involves implementing effective asset allocation strategies to enhance returns and mitigate risks. Numerous studies have been conducted worldwide on various types of asset pricing models and investment portfolios, with some incorporating machine learning and deep learning techniques. In several models, the predictive accuracy has exceeded 90%. To shed light on the current research landscape in the realm of asset pricing and portfolio investment, we conducted a scientometric analysis.

The publication presents the result of extensive research analysing how we can make smart cities more sustainable and inclusive. The central problem was to explore the readiness to adopt inclusive smart services that can sustainably foster, primarily, the inclusion of people with disabilities and the elderly. The secondary audience included the general population, facilitating a comprehensive examination of the varying needs among these demographics. The methodology involved an online questionnaire, formulated based on the Unified Theory of Acceptance and Use of Technology 2 model, and administered to a diverse sample of 302 participants from developing countries, Serbia, Bosnia and Herzegovina, and Montenegro, ensuring a broad representation of gender, age, educational backgrounds, and disability statuses. Key findings indicate a positive inclination towards smart city services across all demographics, particularly among the elderly and people with disabilities. Factors such as ease of use, and opportunities for social engagement are key drivers in the adoption of smart technologies. Research shows that all groups are interested in using smart city services if these would provide extensive environmental information, thereby improving their quality of life. The research contributes to the field by providing actionable insights for policymakers and urban developers to create more accessible and sustainable city environments.

The study explores the functioning of Rural Collective Economic Organizations (RCEOs) and looks at how they are governed and optimized in rural areas. RCEOs play a significant role in ensuring income stability, poverty reduction, and rural development because rural communities establish them to address economic challenges. In order to identify strategies that can lead to an efficient, sustainable, and impactful role of RCEOs within the rural context, stakeholders with involvement in RCEOs provided perceptions, experiences, and interpretations. Using qualitative methods such as in-depth interviews and focus groups, this work collected the views and recommendations of professionals and practitioners who are knowledgeable about RCEOs. Those discussions identified the main themes and tactics to improve RCEOs. For instance, among such strategies are governance reform to make the institution strong, openness and proper communication, establishment of capacity-building programs, adaptations in its operational framework especially revenue sources diversification; lobbying for enabling legislation to stimulate such initiatives, fostering partnerships, engaging the communities with actions instead of just words through cases that have been successful stories with long-term planning put into action. The article underscores how the collective asset ownership by RCEOs is instrumental in promoting the rural economy.

Significant corporate tax reform was agreed upon by more than 140 nations in 2021, with implementation set for 2023 and beyond. Many people throughout the globe are discussing the possible effects of the Organization for Economic Cooperation and Development (OECD) planned adjustment to the global minimum tax (GMT) on their respective economies. The OECD has formulated a “two-pillar” scheme for international tax rules for 137 countries and regions within the overall framework. Pillar 2 is to set the tax rate at 15%, the world’s lowest corporate tax. The GMT is a reclassification of the taxing authority mechanism for the profits of multinational corporations, which has made significant adjustments to the global tax coordination and governance mechanism. People living in China and earning money from China must pay university fees, which are all of a nationality. Progressive rates ranging from 3 to 45% apply to the complete income. Therefore, taking the initiative in policy is crucial to address this challenge actively. This article makes an in-depth assessment and prediction of the impact of the GMT reform on China’s tax system and enterprises and proposes countermeasures. However, approximately 20% of the earnings can be exposed to the 15% tax rate that governments can apply. If the entire process is mixed in tandem, the tax rate equals 0.2%. Hence, an important amount of tax revenue can proceed down the drain. From the numerical outcomes, the minimum tax with jurisdictional blending was set at 12.5% under Pillar 2; the disparity between the sample’s top and lowest EATRs would decrease by 2.8% points.

To reach carbon peak and neutralization, a low-carbon supply chain is a clean practice that turns supply chain management into a green economy. This approach aims to decrease energy consumption and pollution and accomplish sustainable development across the supply chain. The worldwide logistics business has transformed into an intelligent service provider to the adoption of real-time management tactics made possible by the Internet of Things (IoT). The impact of the IoT on China’s logistics industry and its impact on the environment are examined in this article. This study, a new model is proposed for incorporating the most popular carbon regulations to accomplish the above advantages and create a more sustainable supply chain. This work discusses how to implement effective carbon regulating systems to promote sustainable development. Main contribution of this research is considering the impact of carbon regulations on environmental benefits and enhancing regional development levels in a supply chain network design challenge. In addition, judgments on cargo consolidation are used to lessen financial and ecological footprints. This article examines and analyses the development of a low-carbon supply chain from the two aspects of the IoT and environmental responsibility to better support the sustainable growth of a low-carbon supply chain economy and raise people’s standards of living. Research results show that the industrial scale of China’s low-carbon supply chain economy is expected to exceed 300 billion by 2025. At present, there are still economic development barriers, but integrating the IoT technology and environmental responsibility consciousness into economic development, it has the potential to strengthen the development of a low-carbon supply chain and improve the conditions for long-term, sustainable growth.

The competitiveness of higher education is an important symbol to measure the level and potential of economic and social development. Enhancing the competitiveness level of higher education is an important driving force to improve our education system and realize the high quality and sustainable development of higher education. At present, the measurement and multi-dimensional comprehensive analysis of China’s higher education competitiveness are relatively scarce. Higher education system is a complex system composed of multiple factors. This article uses DPSIR model to transform the complex system operation mechanism into a relatively simple description. The TOPSIS method considers the weights and interrelations among the indicators. It is able to fully consider the importance of the indicators. This method can not only avoid the influence of subjectivity and uncertainty but also evaluate the decision scheme more comprehensively. The study uses panel data from 31 provinces in China from 2008 to 2020 and utilizes the DPSIR model to construct a multidimensional evaluation index system for measuring China’s higher education competitiveness level. The entropy weight TOPSIS method is employed to measure the higher education competitiveness level and analyze its spatiotemporal patterns. Traditional and spatial Kernel density estimation methods, as well as Markov chain analysis, are used to explore the dynamic evolution and long-term transfer trends of higher education competitiveness levels. The Dagum Gini coefficient is employed to analyze the differences and sources of higher education competitiveness level. The research findings indicate that China’s overall level of higher education competitiveness shows an increasing trend, with the eastern region having a significantly higher level compared to other regions. This study suggests integrating the concept of sustainable development, facing the gaps between regions, adopting tailored development strategies, and reducing the disparities in higher education competitiveness among regions. These policy insights aim to provide theoretical references and foundations for enhancing China’s higher education competitiveness level as well as promoting high-quality and sustainable development in higher education.

The payment and clearing system can objectively record the operation of funds for social and economic activities, and the use of payment and clearing data can be made to monitor regional economic changes sensitively. Employing panel data of cities and prefectures in Sichuan Province from 2010 to 2021, this article employs econometric analysis to examine the link between payment and clearing data and the regional economy. The paper’s focus includes the payment and clearing system and economic indicators. The increase in the total amount of payment and clearing data has a significant positive correlation with digital economic development. Only by regression between the total amount of payment and clearing and regional gross domestic product (GDP), it can be found that every 1% increase in payment and clearing can increase regional GDP by 0.476%. After controlling factors such as important production factors, openness, and government intervention, the elasticity coefficient is still 0.1%, and the correlation is significant. Therefore, the amount of payment and liquidation is a valuable predictor of regional economic development. And thus providing Intelligent decision-making references for the policy formulation and strategic planning of governmental departments and regulatory agencies.

In the context of the global spread of the Corona Virus Disease 2019, improving enterprise performance, promoting regional economic development, and solving the problem of unbalanced and inadequate regional development are important considerations for regional policy formulation. To understand the relationship between regional policies and corporate performance, this study starts with theories related to regional policies and corporate performance, and proposes two hypotheses. The Western Development, Northeast Revitalization of the Old Industrial Base, and Central Rise Plan are selected as typical representatives of regional policies, and the hypotheses are verified through a double difference model. The results showed that regional policies could improve corporate performance, especially for state-owned enterprises (SOEs). The correlation between the revitalization of the old industrial base in Northeast China and the performance of SOEs was strong, with a correlation coefficient of 0.832. When conducting robustness tests by changing time periods, the correlation values between the performance of SOEs and policies in the three policy implementation areas of revitalizing Northeast China, western development, and central rise were 0.887, 0.764, and 0.652, respectively. Compared to non-SOEs, SOEs had a more significant effect on regional policy improvement. This conclusion was consistent with the empirical results of hypotheses 1 and 2. Based on this foundation, recommendations are offered for the suitability of regional policies to furnish theoretical frameworks for enhancing the regional policy structure and augmenting the precision and inclusivity of regional policy development. The research model is utilized to reveal the important role of regional policies in promoting enterprise performance improvement and promoting regional economic development. It can provide strong evidence support for policy makers. The study offers a scientific theoretical basis and empirical support that assist policymakers in comprehending policy objectives and implementation strategies with greater precision. Therefore, the coordinated advancement of regional economic development is facilitated.

Investment decisions are important because they involve significant capital in a business. According to the literature, experts agree that a net present value (NPV) approach is better suited for evaluating investment projects’ feasibility. Practically, the NPV method does not account for uncertainty in calculating the expected return on investment. Investments may be susceptible to random events such as natural disasters or economic instability that hinder the expected return on investment. While previous research has attempted to address uncertainty using fuzzy approaches, these approaches mainly focus on data-centric uncertainty. The focus of this study is on the uncertain environmental conditions where changes in macroeconomic, political, and other indicators can influence decision outcomes. This study conceptualizes and implements combining the NPV approach with robust analysis as an efficient and practical method in decision-making under uncertainty and unpredictability. The proposed algorithm was implemented in a case study on investment project selection in Tannakabon, Iran. The results indicate that considering future scenarios, the restaurant investment project is the most suitable among the seven projects. In contrast, the cafe investment project is the least appropriate option available.

In the world of modern industry, which is constantly evolving, the manufacturing sector is intricately connected to the revolutionary currents of the digital economy. Considering the on-going digital revolutions, this study aims to evaluate the robustness of supply chains in the industrial sector. This result was reached by conducting in-depth interviews and analyzing the resilience performance of business systems in the manufacturing industry. The findings indicated that redundancy is the most significant component of resilience. The major purpose is to ascertain how these chains can hold up amid constantly shifting digital dynamics. Utilizing an innovative index contribution technique, our approach uniquely measures the manufacturing sector’s resilience in the ever-changing digital economy. An index contribution model driven by digital economy measurements is included in this case study, focusing primarily on a particular manufacturing company. This research aims to build a comprehensive framework for measuring the resilience of industrial chains. Identifying important indications of resilience, particularly for the case study company, is the first phase of the technique. These indicators include the robustness of the digital infrastructure, the supply chain’s flexibility, and the technology’s adaptation. During the research, the strengths and weaknesses of the manufacturing industry chains are investigated, which leads to the discovery of nuanced insights. Stakeholders can utilize the one-of-a-kind model’s actionable insights to navigate and enhance their resilience in the constantly shifting industrial sector. Considering the dynamic nature of the manufacturing industry, it is of the utmost importance to adopt preventative measures to guarantee the firm’s ongoing development and adaptability. By providing a fresh analytical framework, this study contributes to a better understanding of the complexities of contemporary industrial resilience.

This study contributes to the literature on portfolio choices of elder persons by examining the influence of digital literacy on the propensity to own risky assets, when considering individual- and country-level variables. Our empirical analysis is based on data from Survey of Health, Ageing and Retirement in Europe and a set of macroeconomic indicators retrieved from international databases. To capture the impact of individual- and country-level factors, we have utilized multilevel models with a two-level sampling design. The results obtained provide evidence for a positive relationship between individual- and country-level digital literacy and portfolio riskiness, as well as heterogeneity in the portfolio choices of older individuals across the European Union (EU) countries. In addition, age is found to influence differently the portfolio riskiness of elder persons across the EU countries. Bridging the digital divide may provide access to balanced-risk portfolios for elderly persons.

One of the most relevant features in the context of international trade in recent decades is the increase in the depth of trade agreements. The aim of this article is to explore the heterogeneous effect of preferential trade agreements (PTAs) on bilateral trade flows including their depth in addition to other agreement characteristics such as the geographical scope of the member countries, their degree of development, or their nature. To measure depth, we follow the most recent works that propose indirect instead of direct measures. Once we control for depth, our results reveal that (i) the positive effect of regional PTAs is notably larger for deep agreements whereas the shallow interregional agreements do not seem to increase bilateral trade flows; (ii) North–North PTAs only boost trade when they exhibit a high depth level; and (iii) the depth is not a relevant factor for plurilateral agreements and those that consist of the adhesion of a country to an existent PTA.

The aim of this article is twofold. First, we analyze whether the decision of where to import from is affected by firms’ ex-ante characteristics. Second, we analyze how the origin of imports affects firms’ productivity, export sales, and the number of export markets. Using extensive data on Swedish manufacturing firms from 2007 to 2020, we uncover several significant insights. Nearly 80% of the firms engage in international trade. The smallest firms operate exclusively as exporters, medium-sized firms as importers, and the largest firms engage in two-way trading. While most imports originate from high-wage countries, there has been a gradual shift to low-wage countries over time. Self-selection is evident, with highly productive firms importing from all sources, followed by firms that exclusively import from either low-wage or high-wage countries, and the lowest-productive firms not importing. By controlling for self-selection using the Event Study approach and difference-in-differences matching estimator, we find that large importing firms exhibit no significant differences in productivity and export sales in comparison to their non-importing counterparts. However, small importing firms show increased productivity growth, driven by high-wage imports. Both small and large firms importing from high- and low-wage countries tend to access more high-wage export markets than non-importers.

In this article, we will analyse the effects of public spending on education on productivity, wages, and growth at European Union level. We are trying to find out to what extent education expenditures and human capital contribute to improving productivity, achieving higher wages, and enhancing the standard of living of the population, as measured by per capita growth. Our results do not show a clear and direct relationship between public expenditure on education and productivity, but an indirect relationship through the acquisition of a higher level of human capital and technological skills, which leads to output increases.

Do carbon taxes reduce CO 2 emissions in the countries that adopt it? Andersson (2019. Carbon taxes and CO 2 emissions: Sweden as a case study. American Economic Journal: Economic Policy , 11 (4), 1–30) provides a clear, affirmative answer. His article has been widely cited as evidence that carbon taxes “work.” To check whether the estimates from Andersson (2019) are reliable, I replicate his article using its publicly available data and codes. I modify his synthetic control method (SCM) by using a more restricted set of control units (excluding one potentially treated unit). I also use a more efficient methodology, the Prais-Winsten estimator, to estimate price effects on gasoline consumption. In addition, I compute prediction intervals (PIs) and add these to the SCM estimates, using the newly developed scpi R package. My best estimate is that carbon taxes reduced per-capita CO 2 emissions in Sweden’s transport sector by 7.7%, confirming Andersson’s main finding. I then extend Andersson’s approach to the Norwegian transport sector, estimating a smaller reduction of 2.4%. However, this effect falls within the PIs of the estimates assuming no carbon taxes. When I extend the analysis to the national level in Sweden, I estimate wide PIs and obtain inconclusive results.