Effect of economic indicators, biomass energy on human development in China

Abstract

Previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales. We re-visit the causal association between biomass energy (BIO), economic growth (GDP), trade openness (TRO), industrialization (IND), foreign direct investment (FDI), and human development (HDI) in China on a quarterly scale by scale basis for the period 1990 to 2019 using the tools of wavelet, i.e., wavelet correlation, wavelet coherence and scale by scale Granger causality test. The main findings uncover that IND, TRO, GDP, and BIO positively drive the HDI at low and medium frequencies, while FDI negatively impacts HDI during the sample period. Additionally, there is a bidirectional relationship between GDP and HDI at different time and frequency domains. Specifically, we discover that the positive co-movement is more robust in the aftermath of the global financial crisis, particularly for HDI, BIO, GDP, and TRO at medium frequencies throughout the period under research. Our empirical insights have significant implications for achieving human development sustainability in China.

Improving human development through urbanization, demographic dividend and biomass energy consumption

“…Thus, increasing the use of biomass for its energy is associated with a higher level of human development (HDI, human capital, life expectancy and infant mortality). This result confirms the studies by Wang et al (2020) and Hung (2021) that found that biomass energy consumption improves human development in BRICS and China. Despite the unsuitability of the traditional uses of biomass because it leads to pollution, soil degradation, forest degradation, ample time spent collecting firewood, food insecurity and ultimately severe health risks and poverty, more than 70% of the African population relies on it for economic, household and cooking activities.…”

“…The results revealed that biomass energy usage enhances human development in BRICS countries and that bidirectional causality exists between these two variables. Hung (2021) found the same results in the case of China. Using the Panel Quantile Regression (PQR) approach Asghar et al (2022) analyze the impact of electricity access and biomass energy consumption on infant mortality rate.…”

“…It includes extracted material used in an economic interest or any case will be processed (GMF, 2021). Following the studies by Wang et al (2020) and Hung (2021) that highlighted that biomass energy enhances living standards, it is expected that the usage of biomass energy will have an enhancing (positive) impact on human development.…”

The dynamic relationship between trade openness, foreign direct investment, capital formation, and industrial economic growth in China: new evidence from ARDL bounds testing approach

“…This suggests that FDI influxes contribute to the level of industrialization, supply the resources and sophisticated technologies necessary for the development of China's industrial economy, and perform an essential function in terms of economic growth, employment, income generation, and increased productivity, since when FDI increases by 1%, the industrial economy will increase by 0.071%, and conversely when the industrial economy increases by 0.071%, FDI will increase by 11.61%. This result is consistent with that of Liu (2002), Liu and Burridge et al ( 2002), Mohammed and Ruslee (2015) and Hung (2022). However, Rahman (2015) comes to the opposite conclusion and argues that FDI has a significantly negative impact on the development of industrialization, while Gui-Diby and Renard (2015) and Osuji (2015) suggest that FDI has no significant effect on the development of industrialization in African countries (Carkovic and Levine, 2002).…”

Causal relationship between globalization, economic growth and CO2 emissions in Vietnam using Wavelet analysis

“…The method uses the local quadratic polynomial for all of the observations in the original yearly series to fill in the observations of the higher frequency associated to the period. 60,63 We create the quadratic polynomial by fitting a quadratic into sets of three end-to-end values from the actual data. To eliminate the problem of heteroscedasticity, all series were transformed into logarithmic form.…”