This study investigated the impact of economic, environmental, and social indicators on inclusive growth in 19 member countries of the Asian Cooperation Dialogue from 1995 to 2021. This research employed the Driscoll–Kraay standard error regression technique. The findings reveal that the impact of independent variables on inclusive growth differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). One of the primary contributions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclusive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness; agriculture, forestry, and fishing; the ecological footprint; and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing the ecological footprint, and increasing renewable energy utilization. This paper suggests some future work directions for extending the analysis to other regions and indicators. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer, and green financing.

This study investigates how a range of economic, environmental, and social indicators have influenced the concept of inclusive growth in 19 member countries of the Asian Cooperation Dialogue over the period spanning from 1995 to 2021. To analyze these relationships, the research employs the Driscoll Kraay's Standard Errors regression technique, designed to account for factors such as cross-sectional dependence, hetero-scedasticity, and autocorrelation within the data. Furthermore, various preliminary tests were conducted to assess the data for cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. Additionally, the Westerlund cointegration test was employed to evaluate the potential long-term equilibrium relationships among the variables. The findings reveal that the influence of independent variables on the dependent variable, which in this case is the level of inclusive growth (ING), differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). Notably, the results underscore the overall statistical significance and robust fit of the regression model for all three income groups. One of the primary contribu-tions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclu-sive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness, agriculture, forestry and fishing, ecological footprint, and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. The paper suggests some future work directions for extending the analysis to other regions and indicators, as well as incorporating dynamic panel data models and causality tests. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer and green financing.

This study addresses the interconnected determinants of road traffic safety in the context of the top 10 pollution-emitting countries, employing a data-driven ecological systems approach. The dependent variable is road traffic fatalities, and the goal is to determine the complicated interactions between other factors such as temperature, rainfall, health force density, road length, and ecological impact. The data is analyzed using a wide range of techniques, including panel data analysis, unit root testing, cointegration analysis, and regression using Driscoll-Kraay standard errors. Temperature and precipitation are shown to have a substantial effect on the dynamics of road safety, as shown by the results. Conventional wisdom is put to the test by the crucial roles played by health force density and ecological impact. Given the complex interplay of factors, the research finds that comprehensive approaches to road safety are necessary. To further inform evidence-based policy recommendations for improved road safety, future study might investigate cultural impacts and temporal dynamics. The coefficient of 0.711 has a D/K standard error of 0.212 and the p-value is 0.002. This suggests that temperature has a positive relationship with road traffic fatalities, and the relationship is statistically significant. An increase in temperature is associated with an increase in road traffic fatalities per million. The coefficient of 0.552 has a D/K standard error of 0.132 and the p-value is 0.001. This indicates a positive and statistically significant relationship between rainfall and road traffic fatalities per million. The coefficient of 0.420 has a D/K standard error of 0.101.and the p-value is 0.000. This suggests a strong negative relationship between health force density and road traffic fatalities. Countries with higher health force density experience lower road traffic fatalities.