Scientific and technological innovation and human capital savings are important ways to promote the high-quality development of China's economy. On this basis, using the panel data of 30 provinces in China from 2009 to 2017, this paper empathically tests the impact of university research activities on high-quality growth of China's economy, and explores the influence mechanism from two dimensions of talent cultivation and achievement transformation. Based on the spatial perspective, the regional heterogeneity of these influences is analyzed. This paper finds that: First, research and development activities in universities effectively boost the high-quality development of China's economy. Secend, the stimulating effect of university research and development on economic growth. On the one hand, through the talent training function of scientific research activities, it enriches the accumulation of human capital and drives the economic development; On the other hand, constrained by the predicament of scientific research achievement transformation, the stimulating effect of university scientific research activities on economic growth will be limited to a certain extent. Third, there are significant regional differences in the promoting effect of university research activities on high-quality economic development, and this difference may come from the spatial spillover effect of university research activities. This study has important enlightening significance to understand the relationship between university scientific research activities and high- quality economic development.

In this work we aim to identify potential determinants and seek to predict terrorism attack. Thus, to eliminate uncertainty linked to explanatory variables we used the BMA method. We show that, contrary to expectations terrorism in MENA region is no longer purely of economic origin but mainly due to political problems, education, financial development and countries’ demographic characteristics. Likewise, we find that national, international and global terrorism are not of same origins even they present many common roots. In the end, we show that it is possible to predict majority of attacks based on a small number of indicators measuring political risk, financial development and income inequalities.

The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.

All things being equal, and in a truly democratic world, one will expect that the fact that women are the majority in almost every single community of the world should guarantee them at least equal control or ownership over resources and enjoy most of the rights and privileges in that particular community. But things seem not to be working that way as evidence from the literature shows that women are always relegated to the background when it comes to the issue of control or ownership over, and sometimes even access to property in most societies which include the right to own and use land. This undoubtedly has a consequence on the productive and overall development of such a community. In recent times, the issue of gender discrimination, disparities, and inequalities has attracted the attention of researchers over the past decade with enormous literature in this area. There are, however, few studies that provide a comparative analysis of the situation between two or more countries. This paper seeks to fill this void of knowledge based on a review of relevant literature on land rights with an emphasis on the situations in Kenya and Tanzania. The results have shown the prevalence of four types of land rights such as communal property rights, non or open-access property rights, state or crown property rights, and private property rights. The results have further indicated that the controversial land rights of Kenya have led to the reduction in the lands of certain groups of people, increasing land disputes, and also discriminating against women in their land rights. In the case of Tanzania, there were several cases of bias against women concerning the ownership of land, especially under the customary system of Tanzania. Issues of discrimination against women were also identified in the inheritance laws of the land which is a paramount feature within the norms and cultures of the people of Tanzania. The lack of equal access to land by women in both countries is found to harm productivity in several sectors of their economies, especially in agriculture. Policymakers are therefore admonished to ensure equal access by both genders to land as this will lead to efficiency in the management and usage of land which is certainly good for sustainability in the development of any economy.

Financial development and industrial structure optimization are two-wheeled engines for promoting high-quality development, but the causal identification of the two is still poorly understood. This paper addresses this research gap by comprehensively analyzing the relationship between them. Regional economic growth is a necessary condition for achieving high-quality development. Taking China as an example, this study empirically examines the role of financial development and industrial structure optimization in high-quality development using the entropy method, fixed effects, mediation effects and instrumental variable regression. The study results show that (i) financial development has actively promoted regional economic growth. (ii) The driving effect on the central and western regions is greater than that on the eastern region. (iii) There are differences in the mediating effect of industrial structure optimization and rationalization between different levels of financial development and economic growth. (iv) In addition, the mediating effect of industrial structure optimization is larger in the eastern region than in the central and western regions. (v) The mediating effect of industrial structure rationalization is not obvious in the eastern region, but has a masking effect in the central and western regions. Our findings provide new strategies for financial development and industrial structure optimization in developing countries.

This paper aims to study and quantify the impact of agricultural exports on economic growth along with other variables in Tunisia. For that purpose, we estimate the relationship between GDP, agricultural exports, other exports, and imports by applying the ARDL approach under the period 1990-2020. The results show a negative and insignificant impact of agricultural exports on economic growth in the long and short run. Other exports negatively and significantly impact economic growth, while imports are found to have a positive impact on long-term economic growth. The findings have economic implications for sectors and activities that should be promoted to accelerate economic growth.

In order to avoid the real economy development lags behind and the deterioration of ecological problems in the process of traditional urbanization, China has been promoting city-industry deep integration, but the connection between city-industry integration and green economic growth, especially the spatial effect, has not been systematically explained. Based on the panel data from 2007 to 2018, this paper constructs an evaluation index system and uses the SEEA method to measure city-industry integration (CII) level and green economy growth (GEG) level. Then, by employing spatial Durbin model and intermediary effect model, it further systematically investigates the spatial impact of CII on GEG and the potential mechanism. The study found that: (1) On the whole, CII shows “slow-steady integration” trend, but regional heterogeneity was obvious and accompanied by “slow gap expansion”. GEG experienced “sharp increase” with “polarization” characteristic. (2) CII can directly promote regional GEG (with a marginal effect of 0.689), more effectively than traditional urbanization, and CII has obvious spatial spillover effects on GEG in both “local effect” and “neighboring effect”. (3) Interestingly, technological innovation and consumption structure upgrading are significant mediating mechanisms. (4) The direct positive effect of CII shows the regional imbalance characteristic. Finally, the corresponding policy implications are put forward.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

Economic globalization has intensified economic volatility around the world, and the importance of economic resilience has become increasingly prominent in the face of rising uncertainty due to frequent domestic and foreign shocks. Based on provincial panel data in China from 2008-2019, a comprehensive indicator system is constructed to measure regional economic resilience (RER), and the impact of financial driver (FD) on RER is explored from staged, pathway and non-linear perspectives. The main findings are as follows. (1) Both national and regional levels of FD and RER show an upward trend, and the impact of FD on RER has an “inverted-U” shape: as the capacity of FD increases, its impact on regional economic resilience shifts from promoting to inhibiting. The RER has significant spatial agglomeration characteristic. (2) Interestingly, staged analysis shows FD can significantly promote RER only when it is in the “recovery stage”. (3) The pathway analysis of its effect on regional economic resilience shows the following characteristics: financial scale > financial efficiency > financial deepening. (4) From different regions, there are significant differences in the effect of FD on the promotion of regional economic resilience: central region > western region > eastern region. Finally, the corresponding suggestions were provided.

The study analyses the long growth rate period contribution to human development in West African Economic and Monetary Union (WAEMU) from 1996 to 2019 introducing time and institutional indicators effects analyses. Time and institutional effects both greatly improve model's diagnostics statistics. Time effects reduce growth variable coefficient and its significance, but not institutional effects. Combined effects inhibit growth contribution to human development. It appears essential to increase the resiliency of growth and the efficiency of government institutions.

The long-term extensive economic development has caused China's resource and environmental problems, especially the resource misallocation. The way China prioritises its limited resources is being significantly impacted by the rise of the digital economy and the interconnectedness of new technologies and the real economy. This paper quantitatively examines the linear and nonlinear impacts and mechanisms of digital development represented by internet development. With a series of empirical tests, we found that the internet development has significantly inhibited the resources misallocation, and the conclusion is still valid in the robustness test with internet popularization and internet infrastructure as the core explanatory variables. In addition to the marketization, internet development can further inhibit resource misallocation by promoting financial development, openness, urbanization and industrial structure. The findings of threshold regression suggest that the inhibitory effect of internet growth on resource misallocation becomes more visible as the degree of financial development and industrial structure increases; with the higher degree of urbanisation and marketization, although the internet development has always played an inhibitory role on resource mismatch, the inhibitory effect first increases and then decreases; with the improvement of openness, the hindering impact of internet growth on resource mismatch becomes more visible as the degree of financial development and industrial structure increases.

The present paper empirically examines the validity of the environmental Kuznets curve (EKC) hypothesis, which posits an inverted U-shaped relationship between CO2 emissions and economic growth, in Egypt, India, Mexico, Pakistan, Thailand, and Turkey for the period 1970-2020. The EKC validity is highly debated due to varying results across countries. This study is significant to addresses this controversy by estimating the EKC's robustness while considering the influence of financial development, globalization, and government expenditure. We employ two cointegration techniques, the autoregressive distributed lag (ARDL) and vector error correction model (VECM), also considering potential structural breaks in the data. Out key findings indicate that the EKC hypothesis is supported in India, Pakistan, and Turkey, with carbon emissions eventually declining after reaching a peak. The hypothesis is not supported in Egypt and Thailand, where emissions rise monotonically with economic growth. In Mexico, the results are mixed, with ARDL suggesting a U-shaped relationship and VECM supporting the EKC. From our empirical analysis of the EKC hypothesis, we derive the following policy implications: 1) countries should adopt comprehensive policy strategies that integrate these goals and fully consider the long-term environmental and societal impacts of their decisions; 2) countries should invest heavily in research and development to accelerate the creation of new technologies that can effectively reduce carbon emissions; and 3) countries must actively raise awareness about the environmental repercussions of economic growth.

Following the seminal paper on Optimum Currency Areas (OCA) by Mundell (1961) a wealth of literature has been published on the business cycles synchronisation and its main determinants. This work provides a systematic review of this research field both at country-level synchronization and regional level. The paper aims to evaluate the contribution of the related literature’s methodologies to the measurement of the business cycle and the estimation of the level of synchronization. The discussion of the collected papers is expected to substantially assist researchers, practitioners, and policy makers keen to employ themselves in the area of business cycles synchronisation.

Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.

This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

The impact of international tourism on emerging markets has been overwhelmingly beneficial. Despite the obvious benefits of tourism, it comes at a high price for the environment in the form of pollution. Brazil's annual tourist influx has the potential to boost economic development and damage the country's ecosystems. The objective of this investigation is to analyze, using time series data ranging from 1990 to 2019, the effects of tourism and economic growth (GDP) on carbon dioxide (CO₂) emissions in Brazil. The stationarity of the data was examined by employing unit root tests, and an autoregressive distributed lag (ARDL) technique was used to investigate the link between the factors, taking both the long- and the short-run into consideration. This research shows that there are long-term and short-term ties between Brazil's tourism industry, GDP, and CO₂ emissions. Yet, both tourism and economic expansion have had serious negative effects on Brazil's ecology. These results indicate that in order to maintain environmental quality in Brazil, policymakers need to pursue more eco-friendly economic expansion as well as environmentally conscious tourist regulations.

The long-term low level of residents' property income is not only detrimental to the wealth accumulation of farmers, but also aggravates the urban-rural income imbalance. This paper uses an empirical approach to explore the mechanism of digital literacy's impact on farm households' property income. The main findings of this paper are as follows: (1) Digital literacy and its sub-dimensions can significantly increase the level of farmers' property income, with the strongest contribution of digital information acquisition literacy. (2) Digital literacy has a greater boosting effect on the property income of farm households with low education level heads, low physical capital households, and villages with better economic development. (3) Digital literacy increases the property income of farm households mainly through expanding social capital and improving risk preferences. The innovation of this paper is to construct a digital literacy evaluation index system based on a micro perspective that fits the behavioral characteristics of farmers, and reveal the impact and mechanism of digital literacy and its sub-dimensions on farmers' property income. This study expands the theoretical research related to digital literacy and farmers' property income, and provides a scientific basis for the choice of strategies to enhance the digital literacy of the majority of farmers, which is important for promoting wealth accumulation of farmers and achieving common prosperity.

Access to electricity, a fundamental element of contemporary life, is essential for economic success. Its impact extends to the fundamental foundations of industrial development and has the power to improve a wide range of industries, including healthcare, transportation, utilities, and education. This article aims to analyse the effect of electricity access on primary education in Central Africa over the period 1997-2019. To this effect, we employ data mainly from the World Development indicator on 9 countries of Central Africa. Using the pooled ordinary least squares estimation technique, the results indicate that access to electricity contributes positively and significantly to primary educational attainment in Central Africa during the study period. The results are Robust to the use of alternative estimation strategy and eventual endogeneity problems in the results are account through the two stage least square estimation techniques which confirm our baseline results as well as the nature of the relationship between access to electricity and primary education in Central Africa. These results have important implications for policies in overcoming barriers to electricity access.

The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.

The vigorous development of the digital economy has brought new opportunities and challenges to the construction of local fiscal revenue efficiency. Based on the panel data from 2011 to 2020, this paper uses the fixed effect model, instrumental variable method and other empirical studies to investigate the impact of the development of the digital economy on the efficiency of local fiscal revenue. The research results show that the development of the digital economy has directly improved the efficiency of local fiscal revenue, and the regression results of instrumental variables are still significant. The mechanism analysis shows that the development of digital economy mainly promotes local fiscal revenue such as efficiency improvement by cultivating tax sources.