Huizheng Liu

Purpose: The aim of the paper is to assess the investment risks associated with foreign direct investment in the Opole Voivodeship from 1991 to 2024. Design/methodology/approach: The methodology combines quantitative analysis, using statistical data (e.g., GDP growth, inflation, exchange rates, and FDI inflows), with qualitative methods, including case studies of key enterprises with foreign capital in the region. Findings: The research identifies several factors that initially deterred FDI in Opole Voivodeship, including macroeconomic instability, competition from larger agglomerations, and socio-political sensitivities. Research limitations/implications: The study is region-specific, focusing on Opole Voivodeship, which may limit the generalisability of findings to other regions with different socio-economic contexts. Practical implications: The study emphasises the importance of leveraging local diaspora networks and cultural ties to attract capital while diversifying the sources of foreign investment to mitigate risks associated with investor monoculture. Social implications: The research underscores the role of socio-cultural factors, such as historical ties and community acceptance, in shaping investment dynamics. Originality/value: This study contributes to the relatively underexplored field of regional FDI determinants in Poland. Keywords: foreign direct investment, investment risk. Category of the paper: Case study.

The rapid advancement of generative artificial intelligence (GAI) and the extensive use of social media have transformed how students engage with educational materials and interact with their peers. Collaborative learning (CL) platforms, empowered by artificial intelligence (AI) algorithms, have gained popularity due to their potential to enhance learning outcomes and provide personalised educational experiences. This research examines the effects of generative AI (ChatGPT‐4) and social media use on young students' academic performance and psychological well‐being, focusing on CL. The study conceptual framework was examined based on a sample of 441 Chinese university students. The statistical technique PLS‐SEM is put into practice to measure the structural framework of academic performance and psychological well‐being. The findings of this study show that generative AI (ChatGPT‐4) and social media positively influence young students' academic performance and psychological well‐being. Additionally, the results of this research study show that CL positively mediates between social media, academic performance and psychological well‐being. Conversely, it negatively mediates the association between generative AI (ChatGPT‐4), academic performance (AP), and psychological well‐being. The findings can facilitate a better understanding of the implications of technologies in the educational context and subsequently aid in formulating evidence‐based strategies to optimise their impact on students's academic success and well‐being.

Optimizing export growth patterns and improving the quality of export trade have become prominent issues that need to be addressed urgently in the context of increasing global economic uncertainty. This study utilizes matched data from the CEPII database (1995–2020) and WTO tariff database, adopts a product-level perspective based on the triple margins of exports, and uses an individual-time two-way fixed-effects model to investigate the impact of import trade liberalization on China's export growth pattern. The research findings are as follows: (1) Increasing levels of import trade liberalization are beneficial for enhancing the extensive margin, intensive margin, and quantity margin of products, while exerting a restraining effect on the price margin. This strengthens the price advantage of Chinese products, indicating that import trade liberalization helps optimize China's export growth pattern. (2) Compared with the intensive margin, import trade liberalization has a more significant promoting effect on the extensive margin. (3) The conclusions drawn from heterogeneity analysis suggest that trade liberalization has a more pronounced effect on the extensive margin, intensive margin, and quantity margin of products in medium-high technology industries, differentiated products, and intermediate goods. This study holds important theoretical and practical significance for evaluating the economic benefits of expanding imports, optimizing China's export growth pattern, and improving the quality of export trade.

There is a consensus among the empirics regarding the positive role of renewable energy in mitigating the effects of climate change. Hence, it is vital to search for the factors that can promote renewable energy demand. As a result, this analysis investigates the impact of educational attainment, environmental law, and innovation on renewable energy consumption (REC) in China. From empirical estimates, we confer that the long-run estimates attached to the environment-related taxes and environmental policy stringency are positive and significant, implying that both these factors increase the REC in China in the long run. Similarly, the estimated coefficients of environment-related technologies and patent applications are significantly positive, confirming that environmental and other technologies give rise to REC in the long run. Likewise, the long-run estimates of education are significantly positive in both models, which confer that REC increases along with an increase in average years of schooling. Lastly, the estimates of CO₂ emissions are significantly positive in the long run. These results imply that policymakers should invest in research and development activities that are crucial for promoting eco-innovation and renewable energy demand. In addition, strict environmental laws should be introduced to induce firms and businesses to invest in clean energy.

This paper explores the importance of outward foreign direct investment (OFDI) of Chinese firms for the Chinese economy. Using the Chinese Bureau of Statistics dataset from 2008 to 2020 and applying the Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) methodology, we evaluate China’s OFDI environment. Firstly, we determine the main inputs of Chinese OFDI and run a PCA to reduce their dimension. Secondly, when employing the DEA method, we take the year as the decision-making unit which allows us to evaluate the trend of China’s OFDI efficiency from 2008 to 2020. Finally, some policy implications are raised pointing to the fact that to efficiently promote OFDI, China needs to adjust its policies and resource allocations. This can be achieved by strengthening its ability to transform scientific research results towards the enterprises’ competitive advantages. Furthermore, the Chinese government should enhance its policies to assist and guide Chinese companies to invest abroad.

To understand the nexus between economic growth and energy sources, in this study, we have selected Pakistan and collected data over the period 1980–2016. The neoclassical production function of Pakistan is augmented with conventional and renewable energy, capital, and labor. Conversely, the conventional and renewable models are being constructed by using GDP as an independent variable. This paper applied linear and nonlinear ARDL models to see whether the influence of conventional and renewable energy consumption on GDP per capita of Pakistan is symmetric or asymmetric and vice versa. Furthermore, the asymmetric causal effects between the energy variables and economic growth are also discussed. From the findings of the study, we deduce the long-run asymmetric effects of renewable energy on the economic growth of Pakistan. Similarly, the asymmetric effects of GDP, in the long run, are confirmed in both energy models. The symmetric and asymmetric causality results have recommended growth and conservation hypothesis. The findings propose that renewable energy is a significant factor in boosting the economic growth of Pakistan and a decline in the use of renewable energy could actually stem the economic growth of Pakistan.