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This study aims to analyze the convergence of environmental degradation clubs in the Association of Southeast Asian Nations (ASEAN). In addition, this study also analyzes the influence of renewable energy and foreign direct investment (FDI) on each club as an intervention to change the convergence pattern in each club.

This study analyzes the club convergence of environmental degradation in an effort to find out the distribution of environmental degradation reduction policies. This study uses club convergence with the Phillips and Sul (PS) convergence methodology because it considers multiple steady-states and is robust. This study uses annual panel data from 1998 to 2020 and ASEAN country units with ecological footprints as proxies for environmental degradation. After obtaining the club results, the analysis continued by analyzing the impact of renewable energy and FDI on each club using panel data regression and the Stochastic Impacts by Regression on Population, Affluence and Technology model specification.

Based on club convergence, ASEAN countries can be grouped into three clubs with two divergent countries. Club 1 has an increasing pattern of environmental degradation, while Club 2 and Club 3 show no increase. Club 1 can primarily apply renewable energy to reduce environmental degradation, while Club 2 requires more FDI. The authors expect policymakers to take into account the clubs established to formulate collaborative policies among countries. The result that FDI reduces environmental degradation in this study is in line with the pollution halo hypothesis. This study also found that population has a significant effect on environmental degradation, so policies to regulate population need to be considered. On the other hand, increasing income has no effect on reducing environmental degradation. Therefore, the use of renewable energy and FDI toward green investment is expected to intensify within ASEAN countries to reduce environmental degradation.

This research is by far the first to apply PS Club convergence to environmental degradation in ASEAN. In addition, this study is also the first to analyze the influence of renewable energy and FDI on each club formed, considering the need for renewable energy use that has not been maximized in ASEAN.

This study tests convergence in energy diversification, per-capita income and financial development and explores their interrelationships.

Club convergence tests, Granger tests and panel regressions are employed on 134 countries from 1995 to 2019.

While overall convergence is absent across the entire sample, countries have converged within specific clubs. Low- and lower-middle-income countries show convergence in energy diversification and per-capita income. Positive bidirectional relationships are found between energy diversification and per-capita income, and between financial development and per-capita income. A U-shaped relationship between oil prices and energy diversification is identified.

The findings suggest that achieving a shared equilibrium in energy diversification, economic prosperity and financial development is feasible through technological progress within convergence clubs. Investments in human capital and technology are crucial prerequisites for sustainable development.

This study pioneers testing energy diversification, per-capita income and financial development convergence, investigating the tri-directional relationship between them, and exploring the U-shaped relationship between oil prices and energy diversification.

This article investigates the validity of the different types (conditional, unconditional, deterministic, stochastic) of ß-convergence in per capita GDP for EU-28 and EU-19 between 1990 and 2019.

The study uses nonstationary heterogeneous panel-data methodology.

The panel data reveal that both conditional and unconditional ß-convergence are valid in EU-28 countries However, only conditional convergence exists in EU-19 countries; group-specific findings show that the income levels of 10-EU countries converge toward the EU-19 average and 11-EU countries converge to the EU-28. The convergence speed to EU average varies between 15 and 18%. The robustness of the augmented mean group (AMG) findings are checked with common correlated effects mean group (CCEMG) and are consistent. Moreover, panel unit root tests are applied to examine the stochastic and deterministic convergence of the average EU per capita income in the two groups of EU economies. The findings show no evidence of deterministic or stochastic convergence in EU countries. Besides, conditional convergence has not been experienced in countries such as Bulgaria, Croatia, Czech Republic, Hungary, Latvia, Malta, Romania, Slovakia and Slovenia, which are new members of the EU. As a remarkable aspect of the study, the evidence suggests that the Brexit is economically rational for the UK.

The growth and convergence processes of economies differ from each other. Convergence studies in the literature are generally based on the cross-section OLS methodology. In this context, the study is one of the rare studies to examine convergence using heterogeneous panel techniques and allows the convergence of countries to the EU average to be analyzed individually.

The processes of convergence are particularly challenging in the Sub-Saharan African frameworks, considering the diversity of contexts and endogenous particularities. Creating conditions to support these nations to improve their socioeconomic dynamics and performance requires additional contributions from international organisations, governments and the scientific community. In this scenario, this paper aims to analyse the convergence process in Sub-Saharan African countries over the past three decades.

To achieve these objectives, data from the World Bank were considered for the gross domestic product (GDP) per capita over the period 1990–2021. This statistical information was assessed through panel data approaches based on the models from the convergence theory. Specifically, the concepts of sigma and beta convergence were addressed, as well as the concept of catch-up rates.

The findings obtained highlight evidence of the existence of clubs of convergence among the Sub-Saharan African countries and the processes of catching up. These results may be relevant support for the policymakers and international funds and programmes.

This research provides a new perspective on the convergence of GDP per capita in Sub-Saharan African countries, based on an analysis focused on groups of countries identified on the basis of catch-up rates. This approach presents a way of dealing with the different specificities of these nations.

As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.

Based on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.

First, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.

Studying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.

This paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.

This paper aims to examine the convergence and divergence of business and production practices in the global wine industry, particularly focusing on Pinot Noir producers in Burgundy, New Zealand and South Africa (SA). This study explores the interplay between firm-specific factors and regional contexts to identify competitive advantage drivers among Pinot Noir producers.

This research uses a comparative analysis approach, using data from a comprehensive winery level survey. This study applies methodologies akin to value chain analysis to unravel the configuration of productive and technology/knowledge creation activities within wineries across three regions.

This analysis reveals both convergence and divergence in business and production practices among Pinot Noir producers in Burgundy, New Zealand and South Africa. Although there is a degree of convergence in marketing, distribution and competition strategies, differences exist in production practices and firms’ capabilities. Burgundy emphasizes tradition and terroir expression, contrasting with the modernization and innovation focus observed in New Zealand and South Africa. However, all regions share a commitment to quality as a competitive advantage.

This study acknowledges limitations such as the focus on a specific grape variety and regions, the absence of performance impact analysis and the need for additional variables like environmental, institutional and cultural factors and consumer preferences to provide a comprehensive understanding of industry dynamics.

The insights from this study offer practical implications for winemakers, industry stakeholders and policymakers. Producers can optimize production and marketing strategies based on regional contexts and market segments, whereas stakeholders can identify emerging trends and opportunities in the global wine market. Policymakers can develop targeted policies supporting innovation, sustainability and competitiveness.

This paper provides a unique contribution by conducting a comparative firm-level analysis across distinct wine-producing regions, shedding light on the nuanced interplay of factors shaping competitive advantage among Pinot Noir producers. This study’s comprehensive data set and methodological approach enhance understanding and offer valuable insights for industry stakeholders and policymakers.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The study assesses the most significant architectural core technological system that converges into a Battery Electric Vehicle (BEV).

Conceptually grounded in the convergence phenomenon and utilizing the graph theory-based network construction approach, based on the Betweenness Centrality (BC) metric, core International Patent Classifications (IPCs) have been empirically identified. Based on these IPCs, the ownership structure of the patents was established through assignee analysis.

Analyzing the networks obtained at different IPC levels, we found that multiple technologies have converged in a BEV, from battery chemistry to electrical engineering and thermal management of electrical machines.

The outcome of this work has led to the identification of BEV technologies, which can be further developed to assess the trends of technologies and associated gaps and aid technology management for the selection, acquisition, and exploitation of technology.

The outcome of this work will aid technology management practitioners in better planning the selection, acquisition, and exploitation of technologies associated with BEV.

The paper adds an evidence-based approach to the body of knowledge to identify the built-in technologies that produce a BEV.

The main motivation of this paper is to present  the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established.

The authors establish a result concerning the L²-convergence rate of the solution of backward stochastic differential equation with jumps with respect to the Yosida approximation.

The authors carry out a convergence rate of Yosida approximation to the semi-linear backward stochastic differential equation in infinite dimension.

In this paper, the authors present the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established.

The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/