Zeeshan Khan’s research while affiliated with Curtin University Sarawak and other places

In the recent times, the role of artificial intelligence in social, economic, and environmental decision-making is important. Artificial intelligence is considered a source of enabling countries to achieve sustainable development goals. The economic consequences of the introduction of artificial intelligence are mostly overlooked and yet to be explore empirically. This work aims to empirically determine the impact of artificial intelligence on income inequality in the pioneers of the field, i.e., the G7 economies. Also, it aims to explore the role of fiscal intervention in mediating the impact of artificial intelligence on income inequality in these economies. The panel data techniques such as the test for cross sectional dependence and the test for slope heterogeneity are used. Furthermore, CIPS is used to determine the level of integration of the variables in the model. Westerlund test for cointegration and granger causality test by Dumitrescu and Hurlin (2012) are also used in the study. Furthermore, CSARDL technique is used to find out the impact of artificial intelligence along with control variables on income inequality. The results show that artificial intelligence reduces income inequality in the G7 both in the short and the long run. The absolute value of the long-term coefficients is larger than those in the short run. Based on the empirical findings of the work, it is recommended that appropriate fiscal interventions are needed in the short run to sustain the income inequality reduction impact of artificial intelligence. However, in the long run such interventions can be counter-productive but the requisite skills to optimally utilize artificial intelligence should be imparted to individuals.

Rapidly evolving innovation and digitalization have captured the focus of policymakers and scholars regarding their potent role in influencing environmental quality. The present research analyzes the impact of these variables on the carbon emissions of Brazil, Russia, India, China, and South Africa economies from 1990 to 2021. This research also explores the impact of economic growth, quadratic green innovation, and green energy on carbon emissions. Using several panel diagnostic tests, this research validates heterogeneous slopes, the presence of cross-sectional dependence, and significant cointegration. Due to the mixed integration order, this research uses a cross-sectional augmented autoregressive distributed lag model, and the results show that economic expansion and green innovation are significant drivers of emissions in both the short and long run. However, digitalization, quadratic green innovation, environmental policy stringency, and green energy are significant in improving environmental quality and sustainability. The long-term results are tested by employing a series of parametric and nonparametric regressions. This research recommends further investment in environmental research and development, digital technologies, green innovation, and the strengthening of environmental policies to attain sustainable development.

Climate changes are occurring faster than expected and affecting human lives through increasing carbon emissions, food scarcity and changes in global temperatures. Following this trend, China is one of the top contributors to global pollution, and at the same time, it is a leading producer of electricity production from renewable sources (ELREC). The current research examines the relationship between ELREC, research and development (REDEV), and economic growth and their impact on three environmental quality indicators: carbon dioxide emissions, ecological footprint, and load capacity factor in China from 1985 to 2018. This research is significant because it helps to identify factors contributing to the environmental deterioration in China and how they can be addressed. Using the autoregressive distributed lag and dynamic autoregressive distributed lag model, the research suggests a load capacity curve (LCC) and simultaneously tests the LCC and environmental Kuznets curve (EKC) hypotheses. The findings indicate that the LCC and EKC hypotheses are viable in China and that ELREC favorably influences environmental quality. Additionally, REDEV has a significant impact on environmental indicators. The findings of this research reveal, in general, that sustainable REDEV, prioritizing environmental protection, and transition-ing towards renewable energy sources are crucial elements of China's green development strategy. This study's findings provide valuable insights for policymakers and stakeholders working to improve environmental quality in China. Ó

One of the top priorities of most countries around the world is sustainable development, to achieve which financial inclusion is identified as one of the key elements. Thus, unlike previous studies, the current study aims to channel the impact of financial inclusion on energy efficiency by constructing a parametric-based index that covers availability, accessibility, and usage of the former. The index provides an overall picture of financial services in all the three covered aspects. The analysis based on novel econometric time-series approaches, the method of moment quantile regression and the robust least square approach, shows that financial inclusion increases energy efficiency. The impact is overall positive for each quantile, i.e. 25th-90th. The impact of green innovation, human capital, and political risk is also supportive for energy efficiency. However, the impact of political risk improvement exhibits negative in the initial stages and turns positive later. In terms of policy implications, broadening financial inclusion base with the promotion of green innovation and human capital is important to achieve higher energy efficiency. In the meantime, improvement in the political risk profile should also be considered for a stable financial system to pave the way for increasing energy efficiency.

Environmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO2 emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO2 emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA.

Green finance is one of the emerging research areas, particularly in academia and industries. However, its contribution to green growth remains relatively unexplored. Unlike previous studies, the current research contributes to the existing literature by using green finance as a policy tool for achieving green growth. The method of moment quantile regression is used to investigate the link between green finance and other control variables on green growth in the 19 selected OECD economies from 1990 and 2021. The main findings of the study support the idea that green finance accelerates green growth in the selected countries. Similarly, the results for human capital show a significantly positive relationship with green growth. Additionally, increase in globalization and GDP decrease green growth. To promote green growth and achieve the sustainable environmental goals set by OECD economies, policymakers and regulators must prioritize green finance.

This research examines whether the remittance (REM) inflows cause Dutch disease and how it affects financial development (FD). The sample consists of the top global REM recipients, namely China, Egypt, India, Mexico and the Philippines, based on the latest World Bank data from 1990 to 2019. This study employs three econometric models to evaluate the REM inflows' impact on FD. Using the cross-sectionally augmented auto-regressive distributed lag (CS-ARDL) estimation technique, we reveal that the REM inflows have impeded FD and have triggered Dutch disease issues in the financial sectors. Furthermore, the estimation found a positive effect of economic growth, globalization, human capital and financial risks on FD across all models, both in the short and long runs. In addition, the interplay among the REM, human capital, and financial risks also facilitates FD. The study suggests that robust resource flows and price adjustment processes tackle the hazard of deteriorating FD and emphasize the Dutch disease's effects on the top REM recipients.

In contemporary times, developed economies are adopting environmentally friendly initiatives such as green finance and energy efficiency. However, the factors affecting energy efficiency are widely explored, and green finance remains overlooked in the empirical literature. The present research examines the drivers of sustainable green finance in OECD economies during 2004–2021. The study uses several panel diagnostic tests and validates the mixed integration order of the variables. Still, the long-run equilibrium relationship is valid between financial inclusion, energy efficiency, human capital, foreign trade, composite risk, and green finance. Using panel autoregressive distributed lag model, the study found that financial inclusion, energy efficiency, foreign trade, and composite risk significantly improve green finance in the long run. However, only energy efficiency is effective in the short run. On the other hand, human capital exhibits a significant adverse influence on green finance. The long-run results are robust due to the significant estimates offered by panel dynamic ordinary least square. Besides, there exist one-way and two-way causal associations between variables. The study recommends improving the financial sector performance to enhance financial inclusivity, increase investment in the energy efficiency sector, and encourage trade in environmentally friendly initiatives.