Currently, the transition to a circular economy is becoming a development trend of many countries around the world to cope with climate change and reduce carbon emissions. Vietnam is also one of the countries in the process of taking steps to transition to a circular economy. However, to make a successful transition to a circular economy, citizen participation is essential. Thus, the question is, are people ready to participate in the circular economy? Therefore, this study surveyed 431 people regarding their willingness to participate in the circular economy based on the theory of planned behavior and the structural equation model. The results of empirical research have shown that the factors attention to the environment, and attitude towards intention are the factors that have a strong impact on willingness to participate in the circular economy. Based on the given influencing factors, the authors make some policy suggestions for the Vietnamese government in the transition to a circular economy.

From 1990 to 2019, Vietnam entered a period of economic growth, attracting foreign investment. However, during this period, CO₂ emissions also increased significantly. This paper will examine the short- and long-term relations of Vietnam’s relationship among FDI, economic growth, and CO₂ emissions based on the data on CO₂, GDP, and FDI of Vietnam in 1990–2019. This article uses the VECM model to test the effect among variables. Research results identify an impact and causal correlation among CO₂ emissions and economic growth and foreign direct investment in Vietnam. Research results show a long-term relationship between CO₂ emissions, GDP, and FDI. The authors did not find a short-run relationship between the above variables. Assessing impacts of CO₂ emissions on economic growth and foreign direct investment will help policymakers make reasonable policies to balance the increase in foreign investment capital and economic growth and reduce CO₂ to achieve the commitment in COP26.

Vietnam’s exports expanded dramatically from $14.5 billion in 2000 to $405 billion in 2024, elevating the country to the world’s 22nd largest exporter despite persistent global shocks. This paper introduces the application of the Causal Machine Learning Approach to Resilience Estimation (CLARE) to industry-level trade analysis, utilizing a comprehensive panel of 97 HS2 sectors from 2000 to 2024 (2425 observations) drawn from UN COMTRADE and WITS databases. We implement Double Machine Learning to estimate causal effects of the Global Financial Crisis (2008–2009) and COVID-19 pandemic (2020–2021) on export growth. Results reveal stark industry disparities: electrical machinery (HS85) exhibits exceptional resilience, fueled by 72% high-technology content and low product concentration, while knitted apparel (HS61) proves highly vulnerable. Fixed effect regressions substantiate core hypotheses: a 10-percentage-point increase in high-tech share elevates the resilience index by 0.031 points (approximately 4.1% relative to the sample mean); a one-standard-deviation reduction in product HHI (0.14 units) yields a 0.026-point gain (3.6% relative); and each additional FTA contributes 0.047 points (approximately 6.2% relative), with all estimates significant at conventional levels. Robustness encompassing alternative learners, detrended outcomes, and synthetic controls upholds findings. Policy recommendations center on accelerating high-tech global value chain integration—targeting semiconductors and electric vehicles—while optimizing CPTPP and EVFTA utilization (currently 35%) and mitigating US–China market concentration (45% of exports). These insights chart pathways for Vietnam’s Vision 2045 high-income ambition amid intensifying geopolitical and climate risks, providing a replicable framework for other export-reliant emerging economies.