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.

Keywords

Biomass energy human development economic growth wavelet analysis China

Introduction

For sustainable human development, improvement in economic growth and environmental quality is significant. The modern economic environment has demonstrated globalization’s role in boosting creative corporate practices to achieve sustainable human development.¹ Various factors impact human development, such as economic growth, energy consumption, industrial production, foreign direct investment, trade openness, financial development, environmental quality, etc..^2–6 Pressures on human development's critical factors, including the lower level of education quality, women's status, human capital, and income inequality, make a remarkable contribution to a low level of human development index.⁷ Building on these insights, it is indispensable to attain human well-being in an environmentally and socially sustainable way to construct sufficient human development planning and improve economic growth and environmental quality. In this regard, a systematical understanding of the intercorrelations between human development, energy consumption, and economic indicators is particularly urgent in China, in which different economic and social drivers are resulting in resource depletion.⁸ As a result, it is necessary to empirically investigate two-way causal interactions between human development, biomass energy consumption, and economic indicators such as trade openness, industrialization, gross domestic product, and foreign direct investment.

This topic is essential for China, where quick growth has accelerated natural resource extraction owing to mounting resource demands in production and other sectors. Furthermore, China's environmental regulations struggle to attain sustainable utilization of natural resources on account of enforcement, implementation, and low public participation matters.⁹ In a recent study, Ahmed et al.⁹ uncover that China's economic growth has stimulated resource consumption and energy shortage. Simultaneously, the continuous increase in energy demand and limited resources of the government has considerably influenced policymakers to encourage local communities and private sectors to play their role in sustainable energy production. Obviously, these scenarios cover the saturation of human welfare, and consequent impairment of human health due to environmental pollution, and society’s education level. Therefore, the current study makes novelty support to related literature regarding interrelation between human development, energy consumption, and economic indicators of China.

Along with economic growth, China has also attained specific achievements in human development. As per Lai,¹⁰ there are three-dimensional factors of longevity, education, and resource used in modeling China's human development index. This is because the human development index is one of the parameters allowing China to estimate its position in the global world.

Human development, in this light, is significantly related to the level and quality of economic development and energy consumption. Figure 1 depicts an uptrend in the human development index during 1990–2016. China’s human development is ranked as 4th among 186 countries and territories of the world in terms of human development index. The values of human development for China reached the highest point of 0.75 in 2016 and gradually increased over the last ten years. The present situation of energy in China makes her vulnerable to energy crisis because the sector influences other sectors of the economy and human life. Although energy consumption has a significant impact on economic growth, it also influences human well-being. Energy consumption is a crucial indicator that reflects the level of social development. More precisely, there is a considerable connectedness between the persistence of energy services and the availability of modern health facilities, communication, and education. The lack of energy services may cause the issues of inadequate health facilities, few opportunities for education, and little potential of the population in poverty. Renewable energy enhances the domestic economies, and it directly impacts human development in rural areas. In addition, economic development is a significant determinant to mitigate poverty and generate more resources, which is indispensable for human development. Biomass energy is a type of renewable energy. The influence of biomass has received much attention from academic researchers. Understanding the importance of biomass energy usage for the human development process motivates us to look into the causal associations between human development, economic development, and biomass energy to quantify how biomass energy consumption impacts human development in China. As a result, the contribution of the present study is to examine the interdependence between biomass energy usage, economic growth, and human development, incorporating trade openness, foreign direct investment, and industrialization in the Chinese context.

Figure 1.

Human development index in China.

The nexus between human development and biomass energy usage has received less attention than the energy consumption-human development relationship in the literature.^2,7 The few related empirical studies are those implemented by the following authors,^11–16 of which their outcomes provide insightful motivations for further inquiry. These empirical findings put forward that biomass energy use improves economic development and is environmentally friendly, though some works offer mixed results. As a result, whether biomass energy would be utilized more or less remains a heated issue. Against this background, China is the appropriate case to investigate the impact of biomass energy use on human development.

Our in-depth analysis of co-movement between human development, economic indicators, and biomass energy consumption in China appears to be the first. Wang et al.² examined the impact of biomass energy consumption on human development in BRICS countries using the Dumitrescu–Hurlin panel causality test. However, ours is the first attempt to estimate the time-frequency co-movement between these indicators in China employing wavelet techniques, which may provide governments and policymakers with valuable information. Different from previous works of biomass and human development relationships, we consider the role of macroeconomic fundamentals in explaining the documented co-movement.

There seem to be three research strands in the literature on the association between human development, economic growth, and energy consumption considered the causal association between these variables. It is noted that the empirical evidence does not report the uniform connection between examined indicators. First-strand concentrates on the effect of biomass energy consumption on human development and showed positive;² negative,^17,18 and bidirectional nexus¹⁹ in the previous papers. The second strand of human development and economic growth is observed both positive^2,6,20–22 and negative.^2,23 These mixed and conflicting results need further examination for their interrelatedness separately and collectively, in order to cast light on the causal nexus. This study empirically examines the causality between biomass energy consumption, economic indicators, and human development index in China. Thus, it is indispensable to understand the research questions; how can biomass energy utilization contribute to human development? How can economic development be made sustainable human development? These associated questions will be thoroughly taken into consideration in different time and frequency domains. Based on past studies focus on China, the present study shall collectively reconnoiter all these variables. Consequently, in-depth analysis to determine the causal nexus between these variables would be fundamental and rational. In addition to biomass energy, GDP, trade openness, industrialization, and FDI are also taken into account as a function of human development. This framework has become a widely adopted index reflecting the level of human development.⁷

The existing literature investigates the impacts of energy consumption and economic growth on human development extensively across countries and uses different methods, which raises doubt about existing econometric estimation methodology and the effectiveness on sustainable development policies. Besides, the existing econometric models failed to envisage the time and frequency domains due to the failure of these past works to reach mixed findings of linkages between the aforementioned variables. These methods include standard ordinary least squares, generalized method of movements, panel fixed effect, random effect, cointegration, ARDL, etc., which ignores the distinction between short, medium, and long term frequencies in the co-movement analysis. As a result, this paper focuses on recent literature explaining the application of the wavelet analysis framework to explore the frequency domain characteristics of data through time and analyze time localized features.^24–28 One of the wavelet approach's core benefits is its ability to unveil latent processes of evolving cyclic patterns, trends, and non-stationarity that are typical properties of economic and financial time series.²⁹ These techniques, particularly continuous wavelet transform and wavelet coherence, provide more intuitive understandings into the connectedness between the selected indicators signifying short, medium, and long-run interactions; including whether the relationships are positive or negative, and which variables are leading or lagging. Specifically, we apply the wavelet correlation of Rua³⁰ and a novel approach to causality using a time-frequency interrelation developed by Olayeni,³¹ which is much easier to interpret. Moreover, these approaches allow us to capture human development's intercorrelation with other related variables in time and frequency domains, promoting our understanding of possible relationships. We center on co-movement base on wavelet coherence and wavelet correlation’s³⁰ and examine the causal associations between human development and considered variables using a continuous wavelet transform introduced by Olayeni.³¹ To this end, we try to answer the following questions: Are human development, biomass energy, and economic indicators inversely or directly connected? Is this relationship primarily a long, medium, or short-term nexus, and how this nexus co-moves across various frequencies through time? What is the direction of causality across various frequencies and through time? Is there evidence of Granger Causality running from the shortest to the longest scale of one indicator to the scale of others?

The remainder of this paper is organized as follows. Section 2 represents the literature review. Section 3 provides the methodology and data sources. Section 4 reports the empirical results. The last section presents the conclusion.

Literature review

The causal association between human development, energy consumption, and macroeconomic indicators has been well investigated in the energy economics literature. The theory of the causal association between human well-being and energy consumption holds that economic development and biomass energy consumption impair health and well-being, which in turn further aggravates the destruction of human development.^5,6,17,23 As for Wang et al.,² human development is the key purpose of economic growth, so every country in general, and China in particular, strives to foster its human development index. As a result, human development indicators have attracted significant attention from researchers. Based on different methodologies, the co-movements of various economic variables and biomass energy on human development have been examined in many studies which demonstrate the influence of economic development,^3–6 industrialization,² foreign direct investment,^6,22 trade openness,^6,16,23 biomass energy consumption,^13–16 on human development.

Economic growth, energy consumption and CO₂ emission

The causal relationship between energy consumption, CO₂ emission, and economic growth across countries is a heated debate with divergent arguments. Bilgili et al.¹¹ study the effects of biomass energy consumption on CO₂ emission and economic growth in the US using an asymmetric causality test. They report that biomass energy consumption mitigates CO₂ emission and economic growth. Specifically, their findings also unveil that biomass energy consumption may be an efficient policy tool for environmentally sustainable development in the US. In a similar fashion, Baek¹² analyzes the time-varying impacts of nuclear and renewable energy consumption on CO₂ emissions and provides evidence that renewable energy consumption reduces CO₂ emission in the short and long term. Aydin¹³ focuses on the nexus between biomass energy consumption and economic development in BRICS countries using different econometric techniques. He notes that the growth hypothesis is valid in Brazil and India, while the conservation hypothesis is well-founded in China and South Africa. The results also uncover that BRICS countries should increase biomass energy consumption to enhance economic growth. Bildirici and Özaksoy¹⁴ explore the connectedness between biomass energy consumption and economic growth for several European Transition Countries and have the same results as Aydin.¹³ Ozturk and Bilgili¹⁵ determine the long-run relationship between biomass energy consumption and economic growth for 51 Sub-Sahara African countries using time-varying panel analyses. They report that economic development is positively impacted by biomass energy consumption, openness, and population in African nations. Shahbaz et al.¹⁶ resort to cointegration tests to examine the relationship between biomass energy consumption and economic growth by combining trade openness and capital with production function for BRICS countries and find the existence of long-run equilibrium nexus between variables. Eren et al.²⁰ examine the influence of financial development and economic growth on renewable energy consumption. Based on the dynamic ordinary least squares approach, the authors confirm significant positive influences of economic growth and financial development on India's renewable energy consumption. The results also reveal a bidirectional causality between these variables.

Balsalobre-Lorente et al.³² investigate the nexus between economic development and CO₂ emission in the EU-5 countries. The outcomes of the empirical analysis indicate that renewable electricity, natural resource abundance, and energy innovation enhance environmental quality, while trade openness and the relationship between growth and renewable electricity consumption exert a positive influence on CO₂ emission. Doğan et al.³³ examine the impact of economic complexity, renewable energy consumption and population growth on CO₂ emissions in OECD countries, and confirm that economic complexity and renewable energy would help in reducing the environmental degradation problems in these countries.

Economic growth, energy consumption and human development

Several authors demonstrate that the link between energy consumption and human development is statistically significant. This implies that consuming more energy resulted in more remarkable human development. Van Tran et al.¹⁷ reveal that increased energy consumption mitigates human development. Their findings suggest no significant causal association between energy consumption and human development. Wang et al.² examine the impacts of biomass energy consumption on human development in BRICS countries. Based on various econometric frameworks, they document that biomass energy consumption increase human development in BRICS countries, and that there exists a bidirectional nexus between these indicators. Yumashev et al.³⁴ also look into the impact of the quality of energy consumption on human development and the environment using a sample of many countries. This paper's outcomes establish that the size and rating of human development are affected by several determinants like urbanization, economic growth, gross domestic product, and energy consumption. Wang et al.¹⁹ confirm a bidirectional causality between human development and renewable energy consumption. Ouedraogo³ and Martinez and Ebenhack⁴ point out that energy consumption has a neutral impact on human development. Wang et al.¹⁸ study the association between renewable energy consumption, economic growth, and human development in Pakistan. Their findings report that renewable energy consumption reduces human development, and trade openness discourages the human development process in this nation. Karmaker et al.⁵ investigate the influence of biomass energy consumption on human development in Asian countries between 1995 and 2016. The authors report that biomass energy utilization can improve human development in these nations, and there exists bidirectional causality between these two indicators. These results are in agreement with Wang et al..² Khan et al.⁶ model the nexus between communication technology, economic growth, and human development index considering foreign direct investment, urbanization, and trade openness in Pakistan. The findings document that economic growth positively influences human development, ad trade openness and foreign direct investment discourage human development in this country. Mustafa et al.²³ investigate the nexus between human development, economic growth, and trade openness in developing Asian countries. Their results document a unidirectional relationship between human development and economic growth in the medium run.

Several studies have analyzed the impact of energy consumption, economic growth, and environmental degradation on human development in China’s context. For example, based on CO₂ emission and energy consumption gathered in Southwest China, Chen et al.³⁵ provide evidence of carbon emission per capita decouples from the human development index from 2000 to 2015 in this region. Bechtel²¹ reveals that economic growth positively influences human development in China and Pakistan. Azam et al.²² assess the links among energy consumption, CO₂ emission, human development, foreign direct investment, and economic growth over 1995–2016 for China. The paper employs the canonical cointegrating regression model and finds that energy consumption has a significant positive influence on FDI, human development, and economic growth. Using the neutral network, U test, and ARDL model, Sarkodie et al.⁸ illustrate that fossil fuel energy consumption and human capital are conductive catalysts for climate change. The study also sheds light on the significant human capital-energy consumption relationship in China. Similarly, Ahmed et al.⁹ examine the impact of natural resources abundance, human capital, and urbanization on China's ecological footprint and report the long-run equilibrium connection among variables. Farhani and Balsalobre-Lorente³⁶ utilize the FMOLS, DOLS, and CCR econometric regression to explore the time-varying relationship between coal, gas, oil consumption, economic development, and CO₂ emission in the three largest economies (China, the US, and India) between 1965 and 2017. The findings report that the US and India show a U-inverted EKC between CO₂ emission and economic development, while China exhibits U-shaped EKC.

Summarizing all, the existing empirical studies in connection with the connectedness between human development, biomass, and economic indicators unveiled how using various panels and different methodologies resulted in inconclusive findings. In addition, these works merely centered on investigating the causality and cointegration between the variables. Nevertheless, no prior study has extensively explored the interdependence and causal influence of TRO, FDI, GDP, IND, and BIO on HDI in the case of China using the wavelet analysis. Specifically, this will further probe, significantly, the inter-links between these variables in China because China is the world’s largest population and pollution emitting country. Besides, the advantage of the wavelet approach is that it can capture the causality and interdependence between series at different time and frequency domains. As a result, this current study fills the gap in the existing literature. Put differently, this paper is an extension of Chen et al.,³⁵ Azam et al.,²² Sarkodie et al.⁸ and Ahmed et al.⁹ by analyzing the impact of TRO, FDI, GDP, IND, and BIO on HDI in China.

Wavelet methodology

Wavelets are mathematical techniques that are widely used for analyzing time series. In this paper, continuous wavelets, cross wavelet transforms, wavelet coherence and causality in continuous wavelet transforms are employed to explore how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.^25–28 Moreover, the correlation measure in continuous wavelet transforms (CWT) developed by Rua³⁰ offers the background for the causal association between variables under study. Recently, Adebayo,³⁷ Al-Rdaydeh et al.,³⁸ and Haseeb et al.,¹ have performed a wavelet-based investigation to analyze the lead-lag relationship between energy consumption, environmental pollution, and economic growth. In this section, we briefly introduce on wavelet framework.

Continuous wavelet transform

The continuous wavelet transform is a constant wavelet transform, which helps investigate the scale-dependent association between the two series. The wavelet application as the bandpass sieve to the original variables is the idea behind the CWT.^25,27

The continuous wavelet transform $W_{x} (s)$ allow us to explore the joint behavior of time series for both frequency and time. The wavelet is defined as

W_{x} (s) = \int_{- \infty}^{\infty} x (t) \frac{1}{\sqrt{s}} ψ^{*} (\frac{t}{s})

(1)

where * represents the complex conjugate and the scale parameter s determines whether the wavelet can detext higher or lower elements of the series

x (t)

, possible when the admissibility condition yields. Further information about amplitude of the variables may also be obtained based on the CWT. As a result, the wavelet power spectrum defined as the square of the amplitude

| W_{X} |^{2}

, uncovers that the variance distribution of multiple frequency elements of the original time series co-move through time, a large variance with regard to a large power.

Wavelet coherence

The wavelet coherence has similar properties to the conventional linear correlation. However, it differs because it shows the intercorrelation between two-time series in the joint time-frequency domain. The wavelet coherence is computed based on the cross-wavelet transform and wavelet power spectrum of each variable. More precisely, whilst the wavelet power spectrum measures contribute to the variance of the series at each time scale, cross-wavelet power estimates covariance contribution in the time-frequency space. The cross-wavelet of two series $x (t)$ and $y (t)$ can be defined as

W_{n}^{X Y} (u, s) = W_{n}^{X} (s, τ) W_{n}^{Y *} (s, τ)

(2)

where u denotes the position, s is the scale, and * denotes the complex conjugate. Consequently, the wavelet coherence can be calculated as follows

R_{n}^{2} (s, τ) = \frac{| S (s^{- 1} W_{n}^{X Y} (s, τ)) |^{2})}{S (s^{- 1} | W_{X} (s, τ) |^{2}) S (s^{- 1} | W_{Y} (s, τ) |^{2})}

(3)

where S connotes smoothing process for both time and frequency at the same time.

R_{n}^{2} (s, τ)

is in the range

0 \leq R^{2} (s, τ) \leq 1

, which is similar to correlation coefficient. If its value is close to zero, evidence of weak interdependence will be identified and vice versa.^25,39

Phase

We cannot capture the casual association between positive or negative dependency using the wavelet coherence because the coherence wavelet is squared. As a result, the phase difference tool is applied to estimate the dependency and causality interrelatedness between time series.²⁸ The phase difference between $x (t)$ and $y (t)$ is defined as follows

ϕ_{X Y} = \tan^{- 1} (\frac{ℑ {S (s^{- 1} W_{X Y} (s, τ)}}{ℜ {S (s^{- 1} W_{X Y} (s, τ)}})

(4)

where

ℑ

and

ℜ

are the imaginary and real parts of the smooth power spectrum, respectively. Phase association between two-time series are presented in the coherence phase by means of arrows: the correlation is positive (negative) when the arrows point to the right (left); and the second (first) variable leads the first (second) variable by 90° when the arrows point to down (up).

Wavelet correlation

To provide the background for the casual association between variables, the Rua³⁰ wavelet correlation measure is given by

ρ_{X Y} (s, τ) = \frac{ξ {s^{- 1} | ℜ (W_{X Y}^{m} (s, τ)) |}}{ξ {s^{- 1} \sqrt{| W_{X}^{m} (s, τ) |^{2}}} ξ {s^{- 1} \sqrt{| W_{Y}^{m} (s, τ) |^{2}}}}

(5)

where

ξ (Q) = ξ_{scale} (ξ_{time} (Q))

with

ξ_{scale}

as the smoothing operator along scale axis, while

ξ_{time}

as the smoothing operator along the time axis. As a traditional correlation analysis,

ρ_{X Y} (s, τ)

is bounded from −1 to +1.

Causality in continuous wavelet transform

The continuous wavelet transform for the Granger causality developed by Olayeni³¹ is employed, which extends the CWT-based correlation measure by Rua.³⁰ It can be written as

G_{Y \to X} (s, τ) = \frac{ξ {s^{- 1} | ℜ (W_{X Y}^{m} (s, τ)) I_{Y \to X} (s, τ) |}}{ξ {s^{- 1} \sqrt{| W_{X}^{m} (s, τ) |^{2}}} ξ {s^{- 1} \sqrt{| W_{Y}^{m} (s, τ) |^{2}}}}

(6)

where

W_{Y}^{m} (s, τ)

W_{X}^{m} (s, τ)

and

W_{X Y}^{m} (s, τ)

are the wavelet transformations and

I_{Y \to X} (s, τ)

as the indicator function which is defined as

I_{Y \to X} (s, τ) = {\begin{array}{l} 1, i f ϕ_{X Y} (s, τ) \in (0, π / 2) \cup (- π, - π / 2) \\ 0, otherwise \end{array}

(7)

It is clear that the big difference between the wavelet correlation and the CWT-Granger causality measure is the inclusion of the causal information through the indicator function $I_{Y \to X} (s, τ)$ .⁴⁰

Data

The current study uses the annual observations with data from different sources from 1990 to 2019. The adopted time frame and scope are based on data availability constraints. We have expanded the period of biomass energy usage data by applying interpolation to obtain the longest possible period. Furthermore, the variables used are carefully selected in connection with the existing empirical literature as well as the economic theory. Table 1 reports descriptions and data sources of the examined variables.

Table 1.

Variable description.

Variables	Description	Data source	Measure
HDI	Human development index	UNDP	Index
FDI	Foreign direct investment	World Bank	% of GDP
BIO	Biomass energy use	Material Flows	Tons per capita
TRO	Trade openness	World Bank	% of GDP
IND	Industrialization	World Bank	% of GDP
GDP	Economic growth	World Bank	US dollar per capita

In this paper, we take into account the leading sustainability indicator: the Human Development Index (HDI), designed as a ranking system to track and compare national levels of human development.⁷ The HDI is first developed by the United Nations Development Program (UNDP) as a statistical composite index consisting of adult literacy, life expectancy at birth for a healthy life, and popular logarithmic value of per capita gross national income at purchasing power parity corresponding to the standard of living.⁷ This study selects the HDI to estimate human well-being since HDI construction has considerable theoretical underpinning in Amartya Sen’s notion of fundamental capability framework, which suggests that human well-being would be assessed expanding freedoms, opportunities, and capabilities to achieve divergent functions. Therefore, this sustainability indicator HDI may give a diverse picture for attaining sustainable human development by achieving a high standard of living within the resources available. Such a sustainability indicator successfully fits the study purpose.

The annual data for this paper is transformed into quarterly frequency, applying the quadratic match-sum technique. This approach also performs amendments for seasonal deviations because the dataset is transformed from low to high frequency by dropping the point-to-point data deviation.¹ In addition, this method based on previous studies like Haseeb et al.¹ Sharif et al.,^41,42 and Shahbaz et al.,⁴³ has perfectly captured the required larger frequency of data without undermining the real essence of examined indicators. Finally, the aforementioned variables are converted into natural log form because they assume that findings are more efficient in returns than actual value.⁷

As mentioned earlier, the primary purpose of this paper is to investigate the relationship between HDI, FDI, TRO, IND, BIO, and GDP. Table 2 illustrates the findings of the descriptive statistics for HDI and other selected variables (TRO, FDI, GDP, IND, and BIO). The average values for GDP, IND, and TRO are positive, while the other considered variables are negative in China. Specifically, the human development index has an average value of –1.858, which fluctuates between –2.080 and –1.669. Besides, HDI shows less variability than other examined variables, while GDP has the highest fluctuation among the selected variables, which is consistent with the study of Sharif et al.⁴¹ Also, the Jarque-Bera test results are statistically significant at 10%, which means that all considered variables are not normally distributed in the case of China.

Table 2.

Descriptive statistics.

Variables	Mean	Minimum	Maximum	Std. Dev	Jarque-Bera	Skewness	Kurtosis	Observations
HDI	0.166617	0.124914	0.243453	0.032508	19.13818***	0.977138	3.091895	120
FDI	0.858292	0.206901	1.675396	0.319446	0.128905	0.054624	2.882332	120
GDP	857.8093	179.7438	2099.915	582.3963	12.35389***	0.629180	2.058029	120
TRO	10.51481	6.035564	16.15763	2.747545	8.325643*	0.554714	2.340985	120
BIO	0.588500	0.368672	1.089375	0.180655	31.25660***	1.194336	3.738622	120
IND	11.10228	9.069318	11.90182	0.720010	29.28057***	–1.18275	3.510435	120

*, **, *** represent the values are significant at 10, 5 and 1%, respectively.

Figure 2 provides us further insight into the data distribution and correlation structure of the variables under investigation. More importantly, Figure 2 depicts significant cross-correlations between variables. HDI exhibits a higher level of correlations with GDP, FDI, TRO, IND, and BIO, suggesting the partial robustness of the results.

Figure 2.

Heatmap correlation and data distribution of examined variables.

Empirical results

Continuous wavelet transform

To investigate the power variance of the related variables, the CWT power spectrum is plotted. The results of a three-dimensional contour-plot of the wavelet power spectrum of HDI, TRO, FDI, GDP, IND, and BIO for China are shown in Figure 3. Wavelet spectrum displays the domestic market variance evolution in relation to the time and frequency component in which higher intensity spectra show a larger variance. In Figure 3, the frequency scale ranging from 4 to 32 quarter scales is presented by the vertical axis, and the horizontal axis covers study periods from 1990 to 2016, while the intensity is shown on the third scale in the form of color (blue (low) to yellow (high)). The intensity levels eventually rise from blue to yellow.

Figure 3.

The continuous wavelet power spectrum of HDI, FDI, TRO, GDP, IND and BIO.

According to Figure 3 and Table 3, all variables under examination show an evolution of variances, indicating significant volatilities at high and medium frequency bands during the sample period. From these plots, FDI, IND, and TRO are relatively risky where a big island of yellow color is scattered over the period 1996–2015. This uncovers that the global financial crisis, European debt crisis, and the Russian financial crisis have greatly influenced China’s economy. On the other hand, the non-existence of localized variation in the short-run (two to eight quarter scales) in the considered variables implies the non-existence of co-movement in some scales and over time. In general, the wavelet power spectrum results indicate that all variables under study in China are highly volatile at high-frequency bands from 1996 to 2015 and stable or less volatile at low-frequency bands. Therefore, we can conclude that all the related variables cluster at high frequencies for the Chinese economy.

Table 3.

The results of wavelet power spectrum for variables.

Quarters	Scale	HDI	FDI	TRO	GDP	IND	BIO
2–8	Short term
8–16	Medium term		1998 2012–2015	1996–2011		1996 2012
16–32	Long term	2000–2008	1998–2015	1996–2014	1999–2011	2000–2015	1993–1996 2000–2007

Wavelet coherence

Cross wavelet transform is analogous to the continuous wavelet transform power spectrum plots. They are used for the characteristic feature extraction in which the localized similarities are taken into account. Figure 4 shows the cross-wavelet power spectrum (XWT) across the time series of China. It can be noticed that the arrows are suggesting phase information to help us recognize the interrelatedness in the variety of various series. The XWT reflects the local covariance between HDI and other examined variables at various scales and periods. The yellow (blue) colors show high (low) power, the yellow (warmer) colors suggest that the two variables have high joint power, while the blue (cooler) colors suggest that HDI and related variables have low power. The XWT indicates that the causal association between HDI and other variables in China is significant at high frequency, revealing that the two variables have similar volatility in the long run. Besides, phase differences illustrate that the interrelationship between variables is not homogeneous across time and frequency domain, as indicated by arrows that point up, down, right, and left through various time and scales.

Figure 4.

The cross-wavelet power spectrum (XWT) and wavelet coherence (WTC) of variables.

Figure 4 is also presented the outcomes of wavelet coherence (WTC). The WTC detexts the areas in which the two-time series co-vary in the time and frequency domain. More precisely, we examine the co-movements and lead-lag connectedness between HDI and other related variables in China using pairwise wavelet coherence plots. Plots of wavelet coherence are the same as those of the cross-wavelet transform (XWT). These pictures clearly show zones through time and scales where every pair of series is remarkably dependent or otherwise, corresponding to the local correlation coefficient ranging from 0 to 1. The relationship between variables is weak if the correlation is 0 and strong if the correlation is 1. Specifically, wavelet coherence has consistent findings on the causality of the series by using phase difference. Hence, WTC tends to explore co-movements in index pairs of China. At the same time, wavelet phase difference identifies the time-varying connectedness of series by observing lead-lag nexus across different investment horizons. Arrows are showing phase difference and cause-effect relationship. The yellow region at the right hand (left-hand) side of the wavelet coherence suggests the persistence of the significant correlation at the end (beginning) of the sample period, while the yellow area at the bottom (top) of the wavelet coherence indicates a strong correlation at low (high) frequencies.

According to Figure 4, the degree of correlation between HDI and other related variables was substantial from 4 to 8 quarter scales for the whole sample period. This meant that the nexus between them was significant in short and medium time investment horizons, and they persisted from about 2000–2012. Co-movements of HDI, TRO, FDI, and IND illustrate high coherence in the short and medium run, and these relationships are also statistically significant in the long run. Notably, the HDI is exhibiting a stronger correlation with BIO and GDP in comparison with other variables. These correlations existed at all scales, namely, the short, medium, and long term over 1990–2016; nevertheless, the highest level of coherence was recorded at scales ranging from 16 to 32 quarter scales from 1996 to 2015.

In the significant island, it is noteworthy to depict phase-related information, as indicated by arrows. The phase pattern between HDI, IND, TRO, BIO, and GDP, the arrows point rightward and downward, suggesting that HDI and these variables are positively correlated and HDI leads IND, TRO, BIO, and GDP. On the other hand, HDI–FDI, wavelet coherence is correlated at all times and across low, medium, and high frequency. The phase pattern shows the existence of a negative association. In anti-phase, the arrows suggest that HDI and FDI are negatively correlated and present a cyclic effect that FDI is leading. Moreover, the arrow points upward downward, showing a causal association between HDI and other related variables under consideration in China. The causal association is not homogenous through time. In general, the findings of wavelet coherence between HID and related variables confirm that they have a bi-directional, unidirectional causal association or no relationship at different time and frequencies during the sample period. We document the wavelet coherence results based on three major periods, such as short, medium, and long term, summarized in Table 4.

Table 4.

Wavelet coherence findings summary.

Frequencies	Cross-wavelet coherence
HDI–FDI
High frequency	$↑ HDI \to ↑ FDI$ $↑ HDI \to ↓ FDI$
Medium frequency	$↑ HDI \to ↑ FDI$ $↑ HDI \to ↓ FDI$
Low frequency	$↑ HDI \to ↓ FDI$
HDI–TRO
High frequency	$↑ HDI \to ↓ TRO$ $↑ HDI \to ↑ TRO$
Medium frequency	$↑ HDI \to ↑ TRO$
Low frequency	$↑ HDI \to ↑ TRO$
HDI–IND
High frequency	$↑ HDI \to ↓ IND$ $↑ HDI \to ↑ IND$
Medium frequency	$↑ HDI \to ↑ IND$
Low frequency	$↑ HDI \to ↑ IND$
HDI–GDP
High frequency	$↑ HDI \to ↑ GDP$
Medium frequency	$↑ HDI \to ↑ GDP$
Low frequency	$↑ HDI \to ↑ GDP$
HDI–BIO
High frequency	$↑ HDI \to ↑ BIO$ $↑ HDI \to ↓ BIO$
Medium frequency	$↑ HDI \to ↑ BIO$ $↑ HDI \to ↓ BIO$
Low frequency	$↑ HDI \to ↑ BIO$

Note: $↑$ denotes an increase in, $↓$ denotes a decrease in, $\to$ denotes the variable on the left side of arrow leads the variable on the right side of the arrow.

Causality in the continuous wavelet transform

Figure 5 provides the results of causality in the continuous wavelet transforms. We represent the time-frequency plots of the CWT of the casual effects from HDI to TRO, IND, FDI, BIO, and GDP and TRO, IND, FDI, BIO, and GDP to HDI in level curves since there are three dimensions involved. The color code suggests the height of the level curves, which runs from 0 to 1, and indicates the strength of the causal effects between the two series. The vertical axis shows the frequency indicated in quarters whilst the horizontal axis presents the time.

Figure 5.

Wavelet-based causality and correlation HDI and other considered variables in China. The green line is the cone of influence, earmarking the regions impacted by the edge effect or phase. The white (yellow) contour shows a 5% (10%) significant level. The significance levels are based on 3000 draws from a Monte Carlo simulation estimated on an ARMA (1,1) null of no statistical significance.

We notice from Figure 5 that the wavelet causality from HDI to other related variables is statistically significant around the years of 1992, 2002, and 2015, for the 0.5–1-year band. For example, the causal effect from HDI to TRO and IND is observed between 1992 and 2000 on 0–8 quarter and between 2007 and 2015 on 8–16 quarter frequency, and this is a relatively strong causal effect. Less a similar causal effect holds with the cases of FDI and BIO. Nevertheless, a strong causal effect is found over the period shown on 0–16 quarter frequency for GDP. Figure 5 shows that the GDP, TRO, IND, BIO, and FDI Granger-cause the HDI in two different periods in a similar fashion. The first period arises around 1994 for the 0–8 quarters band, while the second period can be noticed after the global financial crisis, for the 16–32 quarters band, when TRO, IND, and BIO significantly Granger-cause the HDI in China. These results are in agreement with the findings generated by Figure 4.

More importantly, in contrast to the linear causality analysis, and different from previous studies,^17,21,23,35 we notice causal effects from the HDI to energy consumption, economic growth. The wavelet causality is stronger for the periods of 1992–1994 and 2008–2015.

Figure 6 shows the Rua³⁰ measure of XWT correlation. It is evident from these plots that the period of high positive and negative correlations between variables is similar to the periods of causal connections explanation as mentioned above. The graphs affirm the findings in wavelet coherence. The relationship between HDI and other considered variables is highly positive during 1990–2000 and 2007–2015 at 1–16 quarters band of scale. However, this positive relationship is significant for the entire period at low frequency. Negative co-movement between them is found for the remaining sub-periods. More specifically, it is observed that the correlation between HDI, GDP, TRO, and BIO is high compared to the correlation between HDI, FDI, and IND. These results are in line with the unconditional correlation indicated in Figure 2.

Figure 6.

Wavelet-based correlations. The color code for Rua’s wavelet correlation indicates the degree of correlations, which goes from blue (negative relationship) to yellow color (positive relationship).

Empirical evidence reveals that there is a bidirectional relationship between biomass energy consumption and human development. This means that biomass energy consumption can give rise to deforestation, resource depletion, biodiversity loss, and food insecurity. In addition, conventional forms of biomass used like wood and waste would harm people's health and make the environment polluted. Nevertheless, biomass energy is necessary to help meet human beings' energy demand in cooking, heating, electricity generation, and transportation. More importantly, biomass energy also helps rural workers have job opportunities and raise incomes.² Biomass energy is taken into account to be cleaner and environmentally friendly in comparison with fossil energy. This might be the reason why biomass energy use raises human development in China. These results are consistent with the studies of Eang et al.,² Yumashev et al.,³⁴ and Wang et al.¹⁹ revealing that energy consumption has significant impacts on biomass energy consumption, but do not confirm that of Van Tran et al..¹⁷ Energy policies would enhance the biomass energy infrastructure and biomass supply, which are the appropriate options for China because biomass energy usage positively impacts human development. Consequently, the Chinese government should invest more in renewable energy sources and mainly increase biomass energy to have a sustainable environment, mitigating energy dependency and CO₂ emissions and improving human development. New policies and programs are needed to tackle institutional barriers to extending the use of biomass in China and guarantee that biomass is used for energy in an environmentally sensitive way. Further, policymakers need to monitor more future empirical evidence seeking for the net influence of biomass sources within the scope of sustainable development policies and constraints of biomass sources.

It is noteworthy that there is a strong relationship between GDP and human development in different wavelet scales, which implies that increasing GDP per capita helps people have a higher standard of living. They might get better access to health and education services. More so, along with economic growth, Chinese authorities should focus more on social welfare and environmental protextion, which could make a dramatic contribution to enhancing the quality of life and health. This finding supports the papers of Khan et al.,⁶ Mustafa et al.,²³ Bechtel,²¹ and Yumashev et al.³⁴ Their findings uncover that industrialization has a significantly positive impact on human development. This outcome is entirely consistent with several papers in the previous literature. China is known as an emerging country, and industrialization is often considered to have a prominent part in developing the economy, creating jobs, promoting productivity, and generating income for people. Therefore, the industrialization procedure assists in enhancing human development. Wang et al.² also document the same results for BRICS countries.

Specifically, trade openness increases human development in China since it has a significant positive impact on human development. This situation is the same as the industrialization process and the trade openness make an outstanding contribution to economic growth, job opportunities, and income per capita growth might be reasonable for this outcome which is supported by the past study of Wang et al.² and inconsistent with Wang et al.¹⁸

However, the Chinese government should be careful about its trade openness policies to avoid pollution in acceleration economic growth for human development. They should tighten the environmental regulations to prevent pollution because foreign firms drive Chinese exports.⁹ Also, the government needs to encourage R&D investment since it will promote the technical efficiency of local companies. These strict rules would help to decrease pollution in China and then improve the quality of human beings.

Finally, foreign direct investment negatively influences human development, which suggests that foreign direct investment decreases human development. Foreign direct investment's environmental effects would be a plausible reason as it gives crucial resources for industrialization and economic growth. Nevertheless, reducing standards of the environment to attract FDI would turn China into pollution havens. As a result, foreign direct investment results in environmental degradation and significantly impacts people’s health and livelihoods. Our findings do not support the papers of Azam et al.²² and Kan et al.⁶ who put forward the positive association between human development and foreign direct investment but agree with Mustafa et al.²³

Conclusion

The study takes into consideration the influence of foreign direct investment, trade openness, industrialization, economic growth, and biomass energy on human development in China using quarterly data for the period 1990–2019 using multiple wavelet frameworks. The increasing importance of human development in China makes the examination of co-movement between human development and other variables, as mentioned above, incredibly significant. Nevertheless, previous papers have neither estimated this nexus in a non-linear approach nor investigated this traditional econometric technique scenario. Based on the wavelet coherence and the CWT-based causality framework developed by Olayeni,³¹ our main results unveil that IND, TRO, GDP, and BIO positively drives the HDI at low and medium frequencies, while FDI has a negative impact on HDI during the sample period. Additionally, there is a bidirectional relationship between GDP and HDI at different time and frequencies domain. Specifically, we discover that the positive co-movement is more robust in the aftermath of the global financial crisis, in particular, for HDI, BIO, GDP, and TRO at medium frequencies throughout the research period. Finally, on the one hand, empirical evidence uncovers that HDI impacts the other related variables in the medium and long run, confirming the leading role of the HDI in China. On the other hand, FDI leads HDI in the short run.

Relevant results from the study of causal effects between biomass energy, economic growth, trade openness, industrialization, foreign direct investment, and human development in China would help government, regulatory authorities, and policymakers to make sound decisions when they have to regulate human well-being, economic growth, and environmental quality policies to achieve sustainable development. It is undeniable that biomass energy usage increases the human quality of life, so the Chinese government should raise awareness about biomass energy and encourage the production and consumption of this novel energy. In the same vein, economic growth, industrialization, and trade openness enhance human development. Consequently, policies are required to foster the positive influences of these factors on human development. This implies that authorities should make more investments in health and educational services, controlling income inequality, protexting the environment, and clean energy; and technology in industries should be encouraged. Specifically, there is a negative relationship between FDI and HDI, so China should boost its requirements when attracting foreign direct investment projects, particularly environment regulations.

In general, by considering all tests implemented within the text, we found a significant and substantive impact of biomass energy consumption and economic variables on human development in China. Most outcomes agree with economic theory, and the implications of the model could be used for policymaking. Specifically, biomass energy use is recognized to influence human development; access to this energy is remarkably connected with improved welfare and human development. More renewable energy is a crucial determinant for society's social and economic development because modern energy such as biomass has a direct influence on productivity, health, education, and communication. Hence, enhancing access to adequate energy services that will be affordable, reliable and effective in environmental terms are vital for economic and human development. Our findings suggest that policymakers should pay more attention to projects for renewable energy production in China. The country is able to generate energy from the wind, solar and hydel sources in the various regions of the country. Meanwhile, policymakers need to revise tax policies and make it favorable to attracting foreign and local investors in the exploit of the renewable energy sources in this country. By doing so, it will tackle the problem of lower HDI in China and promote the quality of life and economic empowerment of poor people.

The significant influence of economic growth on human development reveals that government needs to allocate more budgetary share to education and health in order to promote human development. More importantly, there is a need to generate employment opportunities in rural areas of the nation to improve their income and enhancing the living standard of the people. Besides, the government should concentrate on industry and invest more in the agricultural sector, which suggests installing small-scale industrial projects to raise the income of poor people. This is because poverty is the main reason for lower human development.

Finally, even though significant empirical evidence is acknowledged in this study, some limits are worthy of exploration in future research. This study can be implemented for other case studies, and several factors should be considered, such as corruption, political instability, and institutional quality, which need further examination. Additionally, the same model could be employed in other developing countries to help policymakers systematically understand the role of renewable energy and economic growth in the human development process in these countries. Specifically, the impact of biomass energy consumption on human welfare in the developed countries or other regions should be considered to support policymakers with a comprehensive policy guideline.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Authors’ contributions

NTH conceived of the study, carried out drafting the manuscript.

Availability of supporting data

Please contact author for data and program codes requests. R and Matlab are used to organize data.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ngo Thai Hung

Author Biography

Ngo Thai Hung graduated PhD in Finance at Corvinus University of Budapest, Hungary. Currently, he works as a lecturer in Finance at University of Finance-Marketing, Ho Chi Minh, Vietnam, where he delivers different finance and economic-related courses. His interest research is primarily concentrated on market integration and the non-linear dynamics of financial prices. He also concerns about macroeconomics, economic and sustainable development. He has published many research papers in refereed journals.

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

Abstract

Keywords

Introduction

Literature review

Economic growth, energy consumption and CO2 emission

Economic growth, energy consumption and human development

Wavelet methodology

Continuous wavelet transform

Wavelet coherence

Phase

Wavelet correlation

Causality in continuous wavelet transform

Data

Empirical results

Continuous wavelet transform

Wavelet coherence

Causality in the continuous wavelet transform

Conclusion

Footnotes

Acknowledgements

Authors’ contributions

Availability of supporting data

Declaration of conflicting interests

Funding

ORCID iD

Author Biography

References

Economic growth, energy consumption and CO₂ emission