The United States (US) as the second leading economy in global ecological pollution has become a fertile ground for a rigorous analysis of the ecological neutrality using load capacity factor (LCF) and carbon-based consumption (CCO2). This is important for the global drive of achieving environmental sustainability by 2050. The LCF is one of the most comprehensive ecological proxies that incorporate the biocapacity and ecological footprint. In this context, this research examines the effect of natural resources, technological innovation, renewable energy and financial globalization on ecological neutrality in the US for the date spanning from 1990 to 2021. Using the novel augmented auto-regressive distributive lag (AARDL) model, the empirical findings of this study indicate that (i) natural resources, renewable energy, and technological innovation promote ecological neutrality by increasing LCF and decreasing CCO2 emissions; (ii) financial globalization and economic growth mitigate the LCF and increase CCO2 emissions; and (iii) a causal effect is observed among technological innovation, natural resources, renewable energy, CCO2 and LCF. In light of the findings, this study recommends several crucial measures for ecological neutrality targets set out in sustainable development goals (SDGs) 7 and 13, respectively.

Purpose

Since financial sector plays a critical economic role in Hong Kong, the current research aims to comprehensively analyze the association between financial development and economic growth in the country to draw correct conclusions about the impact that financial sector's development has on the growth of the economy. This requires both using of more comprehensive data that includes all or nearly all elements of the country's financial sector and utilizing advanced econometrics techniques to provide more reliable evidence based on the findings. In the study, both issues have been addressed more academically to aid the relevant authorities better.

Design/methodology/approach

This study empirically examines the financial development-economic growth nexus in Hong Kong employing data covering 1980–2019. The quantile-on-quantile (QQ) approach of Sim and Zhou (2015) is utilized to investigate certain subtle aspects of the association linking financial development and economic growth. In addition, the authors benefit from applying the nonlinear Granger causality test of Diks and Panchenko (2006) to assess the variables' nexus in a nonlinear manner.

Findings

In contrast to the evidence of a unidirectional linkage documented in many related studies, the empirical findings suggest that a bi-directional relationship exists between financial development and economic growth for Hong Kong. This is a helpful input for the relevant policymakers and implies that they can set appropriate policies and regulations to balance financial development and economic growth in this country.

The present study endeavors to explore the dynamic causal relationship between economic growth and financial development in Kuwait, covering the time span between 1991 and 2017. Based on the objective of presenting robust results in relation to the research focus, a combination of time and frequency-domain methodologies has been applied. Using the Toda–Yamamoto and the Fourier Toda–Yamamoto time-domain techniques, no causal direction is found through the former test, while the causality flowing from financial development to economic growth is demonstrated by the latter. Nevertheless, the spectral causality test developed by Breitung and Candelon [(2006). “Testing for short- and long-run causality: A frequency-domain approach.” Journal of Econometrics 132 (2): 363–378. https://doi.org/10.1016/j.jeconom.2005.02.004] gives a more thorough overview of the dynamic causal relationships because it allows the temporary and permanent movements in the linkages between variables to be differentiated. The findings show proof of a bidirectional causality between financial development and economic growth. Our findings highlight the emphasis on covering the frequency causality to provide greater insight into the interrelationship between the variables under consideration.