# Financial Development and Carbon Emissions: Analyzing the Role of Financial Risk, Renewable Energy Electricity, and Human Capital for China

Authors: Yiping Guo
Journal: Discrete Dynamics in Nature and Society
 (2021)

---

## Abstract

This study introduces the role of financial risk index and renewable energy electricity output along with financial development and human capital as new determinants of carbon emissions and uses updated time-series data from 1988–2018 for China, employing novel econometric approaches, i.e., Narayan and Popp unit root test with structural breaks, Maki cointegration, and frequency domain causality test for long, short, and medium run causality. The empirical outcome shows that improvement in human capital index and rising shares of renewable energy in electricity output help to limit carbon emissions. In contrast, gross domestic product, financial risk index, and structural break of 2001 increase carbon emissions. Moreover, structural break year of 2008 and financial development index reduces carbon emissions. The negative association between financial development and carbon emissions supports the positive school of thoughts of financial development which promotes sustainable environment. This study recommends promotion of quality human capital and green financial development along with increasing the shares of renewable energy in electricity for achieving China 2030 climate targets of reducing pollution.


---

## Body

## 1. Introduction

One of the most severe challenges the contemporary world is facing over the years is climate change, which has affected the sustainability of life on Earth. The increasing trend in global atmosphere has created unprecedented problems for the human lives and other lives on Earth. It is widely recognized that due to rapid industrialization, emissions of CO2 in the recent decades has increased to an unprecedented high level, which has caused the global temperature on rise. The worldwide CO2 emissions have increased from 22,149.4 million tons in 1990 to 36,390.3 million tons in 2018 [1]. In recent years, the rapid growth in economic activities, along with population growth and globalization, has caused an increase in CO2 and other greenhouse gas emissions, posing a severe threat to the ecosystem [2]. Continuous rise in global economy with a 3 to 4 percent annual growth rate poses a severe risk to the environmental sustainability due to high-energy demand. These factors, along with globalization and urbanization, cause the temperature to rise by 4–6°C. The total particles of CO2 have reached 413 parts per million in air, which has increased the Earth’s temperature by 1.9 Fahrenheit in the last 50 years [3]. Despite several global negotiations and accords, the level of greenhouse gases is on the rise. In this pursuit, countries are implementing strategies to restrict the emission level [4]. Being the largest carbon emitter, China’s continuous economic growth may inevitably cause more carbon emissions in the future [5]. In 2016, China’s share in worldwide CO2 emissions was 27.3% [6]. However, in line with Paris Climate Agreement, the country is expected to control its CO2 emissions by switching the industrial structure to more sustainable energies.The literature on the responsible factors of environmental degradation is quite rich. Income is the most discussed factor of environmental degradation [7]. The relationship between income and environment is empirically tested by the environmental Kuznets curve (EKC) hypothesis, which demonstrates an inverted U-shaped relationship between the level of income and CO2 emissions. The EKC hypothesis is extensively validated by majority of empirical studies. However, these studies are criticized on the ground of incomplete model specification and incomprehensive proxies used for environmental degradation. Hence, researchers included other important variables while testing the EKC hypothesis. In the literature, several determinants of environmental degradation have been highlighted. The most widely identified determinants of environmental degradation are income, industrialization, international trade, urbanization, deforestation, population, energy consumption, etc. [8]. Moreover, renewable energy consumption, eco-innovation, energy efficiency, and human capital are considered important factors for improving environmental quality [9]. Researchers such as Tamazian et al. [10] and Shahbaz et al. [11] included financial development as an important factor of environmental degradation. Later on, a series of studies were carried out to examine the influence of the financial system on environmental quality. The possible relationship between financial development and CO2 emissions is still debatable and researchers have not been reached to a clear conclusion that whether the later positively related with the former or the other way round. On the effect of financial development on CO2 emissions, three different outcomes such as positive, negative, and insignificant are reported in [12]. It is argued that financial development affects environment via energy consumption [13]. On the one hand, due to the improved financial system, households get more financial products, which enable them to have access to high-energy demanding products. On the other hand, firms can afford to buy more high-energy products, which increase the energy consumption. An increase in households’ and firms’ demand for energy leads to more CO2 emissions. Hence, financial development positively affects CO2 emissions [14]. Moreover, financial development may affect environmental degradation by boosting industrialization and generating wealth by increasing risk diversification, which in turn affects CO2 emissions. However, Tamazian et al. [10] argues that financial development attracts foreign direct investment (FDI), which has positive impact on environmental quality. Knowledge spillovers through FDI and international trade are the most effective way to transfer green technologies from one country to another. Hence, FDI strengthens the positive relationship between financial development and environmental quality. Since, foreign firms invest more in R&D, which improves the environmental quality, because of high R&D expenditures, firms may adopt energy efficient and eco-innovative production methods. These eco-innovative methods switched the industrial structure from less efficient energy production to high-energy efficient production and, hence, have strong positive impact on environmental sustainability [15]. Financial development enables firms and government to invest more in eco-innovative and energy efficient methods, which improve environmental sustainability [16]. This study analyzes the financial development-environment nexus in the case of China over the period of 1988–2018. Previous studies were carried out to examine the financial development-environment nexus keeping in account FDI, R&D, energy consumption, and governance of the country. However, to the best of our knowledge, no study has been carried out to include financial risk and renewable energy electricity in financial development-environment nexus of China.


## 2. Literature Review

Due to the catastrophic effects of climate change on human lives, the determinants of heat-trapping gases, especially CO2 emissions is the topic of great interest for academia, researchers, and environmentalists. The issue has received growing attention of researchers in recent years due to the various climate agreements which took place. The issue of possible determinants of environmental degradation is extensively discussed in [7, 17–20] and [4, 21, 22]. The literature on the impact of financial development on economic growth and other macroeconomic indicators is quite rich [23, 24]. Moreover, the relationship between financial development and environmental quality is widely studied with contradictory empirical evidences [13, 25–32]. The literature in this regard can be divided into three major schools. The first school of thought backed by Dasgupta et al. [33], Claessens and Feijen [34], Tamazian and Rao [25], Tamazian et al. [10], and Khan et al. [35] argues that the relationship between financial development and CO2 emissions is negative, i.e., an improvement in the financial system improves the environmental quality by enabling firms to adopt energy efficient and eco-innovative production methods. Financial development reduces carbon emissions by declining production cost, enhancing products competitiveness, using energy efficient technology, and controlling energy cost. Projects related to financial development are in line with carbon reduction goals and most focus is given to cleaner energy. Moreover, financial institutions finance those projects which are environmentally friendly and do not increase emissions [36, 37]. Dasgupta et al. [33], in case of developing countries, Acheampong [13], in case of 46 sub-Saharan Africa countries, Ali et al. [30], in case of Nigeria, Baloch et al. [29], in case of Saudi Arabia, Çetin and Ecevit [38] and Cetin et al. [28], in case of Turkey, Charfeddine and Kahia [31], in case of MENA region, and Jiang and Ma [36], in a global perspective, found a negative association between financial development and CO2 emissions. These authors suggest that, in order to restrict carbon emissions, a strong financial system is a prerequisite.The second school of thought backed by Abbasi and Riaz [39] and Sadorsky [14] argues that FD positively affects CO2 emissions. Abbasi and Riaz [39], in case of emerging economies, Karimzadeh et al. [37], in case of Iran, and Sadorsky [14], in case of emerging economies, found that the relationship between financial development and CO2 emissions is positive.The existing literature provides deep insight into the dynamics of the financial development-environment nexus. First, previous studies have used incomprehensive proxies for financial development. For instance, to measure financial development, several authors have used the ratio of the sum of deposits and loans to GDP. Moreover, several authors have proxied financial development by financial intermediation efficiency, financial scale development, and financialization [40]. However, these proxies are greatly criticized on the ground of week and incomplete nature. For example, in case of China, 20–35% of deposits are not transferred into loans in the past decade, and hence, there is widespread difference between the deposits and loans. Henceforth, the ratio of deposits to GDP is an incomprehensive proxy of financial development.In case of China, several studies have been carried out to examine the financial development-environment nexus [40–42]. These empirical studies mostly involve cointegration and causality methods. It is argued that the relationship between FD and CO2 emissions depends on the proxy used for financial development. For instance, Huang and Zhao [42] and Jalil and Feridun [43] argue that financial efficiency and financialization as proxies for financial development are negatively related with CO2 emissions, while financial scale development as proxy of financial development exacerbates CO2 emissions in case of China. The present study uses the most comprehensive proxy for financial development, which takes into account the financialization and efficiency.To sum up, the existing literature on the relationship between FD and CO2 emissions is rich. However, the role of financial development in the presence of financial risk, renewable energy electricity, and human capital has never been studied before. This study introduces renewable energy electricity as new determinant of CO2 emission in the case of China.


## 3. Methodology and Theoretical Framework

### 3.1. Theoretical Framework, Model, and Data

This study empirically analyzes the relationship between CO2 emissions with its determinants including financial development, GDP, financial risk, renewable energy electricity and human capital in case of China over the period from 1988 to 2018. The dependent variable used in the empirical models is carbon emissions (CO2), measured in million tons and obtained from [46]. We use the production-based measures of CO2 emissions. Financial development is proxies by Financial Development Index (FDI) of IMF (2019). Financial risk is proxied by an index, which measures the overall financial risk of a country and its financial system ability. The FRI ranges from 0–50. Renewable energy electricity (REE) is calculated by renewable electricity as a percent of total electricity generation. The data on REE is taken from World Bank [45]. The data of human capital index is taken from Penn World. The basic model to estimate the impact of financial development, GDP, financial risk, renewable energy electricity, and human capital on CO2 emissions is given as(1)CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+εt,CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+D2001+εt,CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+D2008+εt.On the effect of financial development on CO2 emissions, we expect a negative impact of FD on CO2 emissions. FD enables firms and government to invest more in eco-innovative and energy efficient methods, which improve environmental sustainability. An increase in economic activities (represented by GDP) is expected to increase CO2 emissions. There is a common consensus among researchers regarding the positive effect of GDP on CO2 emissions. Furthermore, renewable energy in electricity and human capital development are expected to abate CO2 emission in China. An improvement in human capital enables countries to implement the eco-friendly technologies to switch the industrial structure to more sustainable energies. Moreover, the use of renewable energies in electricity generation is important in abating CO2 emissions in the atmosphere. Hence, renewable energy in electricity generation may be considered as an optimal solution to energy security and environmental sustainability. Furthermore, improvement in financial risk is expected to deteriorate the environmental quality in China. The theoretical rationale for the negative impact of financial risk on CO2 emissions is that since improvement in financial risk is associated with several factors such as curbing foreign debt service, improvement in net international liquidity and stability of exchange rate is strongly related with economic activities. Hence, an improvement in financial risk leads to rise in different economic activities which in turn cause energy consumption from fossil fuel to rise and increase carbon emissions.We include the structural break for the years 2001 and 2008. The structural break year 2001 and 2008 are linked with China inclusion into the world trade organization (2001) and the financial crises (2008). Hence, this study examines the impact of financial development, GDP, financial risk, renewable energy electricity, and human capital on CO2 emissions keeping in account the structural breaks of 2001 and 2008. Table 1 provides descriptive analysis of all variables included in this study. The average value of carbon emissions is 6.70 kt, financial development index is 0.690, GDP 12.47 US dollars, financial risk index is 40.74, human capital index is 2.21, and renewable energy electricity output is 1.27% of the total. Moreover, the volatility is indicated through standard deviations. Similarly, the normality of the data is confirmed using Jarque–Bera (JB) test. The null hypothesis of the JB test is accepted, which supports that data are normally distributed [46].Table 1
Descriptive statistics.
CO2FDIGDPFRIHCIREEMean6.7012750.69093412.4748040.741942.2127401.271154Median6.6570960.73292612.4629945.291672.2461711.257401Maximum7.0124970.79087513.0363548.000002.5664271.408900Minimum6.3746570.50921011.8834423.000001.8254411.177162Std. dev.0.2360870.0863200.3676849.0107220.2203680.062061Jarque–Bera3.2926534.4744652.0699343.4517291.5252133.515126Probability0.1927570.1067540.3552380.6654900.4664490.172465Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.


### 3.2. Analytical Techniques

#### 3.2.1. Narayan and Popp [47] Unit Root Test

Before examining the long run relationship among variables in models 1 to 3, we employ Narayan and Popp (NP) unit root test to check the integration order of variables. The NP test is based on the methodology of augmented Dickey–Fuller (ADF) test. The main reason of using the NP test is that it takes into account the structural break. Due to its high power in accurately identifying the structural breaks, the test is considered superior over other conventional unit root tests. NP test can trace up two structural breaks in the data series both in level and difference form. Identifying the structural breaks is important due to the fact that ignoring these may provide biased and inconsistent results [47].


#### 3.2.2. Bayer and Hanck (BH) Cointegration Test (without Structural Breaks)

To check the cointegration among variables in models, this study employs Bayer and Hanck cointegration test, popularized by Bayer and Hanck [48]. The test is based on the methodologies of Johansen (J), Engle-Granger (EG), Boswijk (Bo), and Banerjee (Ba) tests. The necessary condition for employing BH test is that all variables must be first-difference stationary. The test equation is given as(2)EG−J=−2lnPEG+lnPJ,EG−J−Bo−Ba=−2lnPEG+lnPJ+lnPBo+lnPBa,where P is for p values of each cointegration test.


#### 3.2.3. Maki Cointegration Test with Structural Breaks

This study also employs the Maki cointegration test, which tests the long-run relationship among variables in the presence of structural breaks. Maki cointegration is superior to other cointegration methods due to its power to identify more than two structural breaks. The Maki test equation for regime shift and trend is given as(3)FDt=α+∑i=1kπiDi,t+β′Xt+γt+∑i=1kδitDi,t+∑i=1kβi′ZtDi,t+εt,where, in equation (3), X is for all explanatory variables such as FDI, GDP, FRI, REE, and HCI. Similar to the BH test, the prerequisite for the Maki test is that all variables must be first-difference stationary. α is drift and γ is for the trend coefficient.


#### 3.2.4. Fully Modified Ordinary Least Square (FMOLS) and Frequency Domain Causality Test

For long-run association between variables in models 1–3, this study uses the FMOLS method. It is intuitionally clear that FMOLS is an asymptotically unbiased and efficient estimator. To overcome the problem of correlation between explanatory variables and cointegration equations, FMOLS is the superior to other estimators. Moreover, to test the causal relationship among the variables, this study uses frequency domain causality test. Frequency domain causality test finds out the short-, medium-, and long-run causal relationship among the variables. The method is superior to other causality tests due to its power in removing the seasonal variations in dataset with short period of time.


## 3.1. Theoretical Framework, Model, and Data

This study empirically analyzes the relationship between CO2 emissions with its determinants including financial development, GDP, financial risk, renewable energy electricity and human capital in case of China over the period from 1988 to 2018. The dependent variable used in the empirical models is carbon emissions (CO2), measured in million tons and obtained from [46]. We use the production-based measures of CO2 emissions. Financial development is proxies by Financial Development Index (FDI) of IMF (2019). Financial risk is proxied by an index, which measures the overall financial risk of a country and its financial system ability. The FRI ranges from 0–50. Renewable energy electricity (REE) is calculated by renewable electricity as a percent of total electricity generation. The data on REE is taken from World Bank [45]. The data of human capital index is taken from Penn World. The basic model to estimate the impact of financial development, GDP, financial risk, renewable energy electricity, and human capital on CO2 emissions is given as(1)CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+εt,CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+D2001+εt,CO2t=π1FDIt+π2FDIt+π3GDPt+π4REEt+π5FRIt+π6HCt+D2008+εt.On the effect of financial development on CO2 emissions, we expect a negative impact of FD on CO2 emissions. FD enables firms and government to invest more in eco-innovative and energy efficient methods, which improve environmental sustainability. An increase in economic activities (represented by GDP) is expected to increase CO2 emissions. There is a common consensus among researchers regarding the positive effect of GDP on CO2 emissions. Furthermore, renewable energy in electricity and human capital development are expected to abate CO2 emission in China. An improvement in human capital enables countries to implement the eco-friendly technologies to switch the industrial structure to more sustainable energies. Moreover, the use of renewable energies in electricity generation is important in abating CO2 emissions in the atmosphere. Hence, renewable energy in electricity generation may be considered as an optimal solution to energy security and environmental sustainability. Furthermore, improvement in financial risk is expected to deteriorate the environmental quality in China. The theoretical rationale for the negative impact of financial risk on CO2 emissions is that since improvement in financial risk is associated with several factors such as curbing foreign debt service, improvement in net international liquidity and stability of exchange rate is strongly related with economic activities. Hence, an improvement in financial risk leads to rise in different economic activities which in turn cause energy consumption from fossil fuel to rise and increase carbon emissions.We include the structural break for the years 2001 and 2008. The structural break year 2001 and 2008 are linked with China inclusion into the world trade organization (2001) and the financial crises (2008). Hence, this study examines the impact of financial development, GDP, financial risk, renewable energy electricity, and human capital on CO2 emissions keeping in account the structural breaks of 2001 and 2008. Table 1 provides descriptive analysis of all variables included in this study. The average value of carbon emissions is 6.70 kt, financial development index is 0.690, GDP 12.47 US dollars, financial risk index is 40.74, human capital index is 2.21, and renewable energy electricity output is 1.27% of the total. Moreover, the volatility is indicated through standard deviations. Similarly, the normality of the data is confirmed using Jarque–Bera (JB) test. The null hypothesis of the JB test is accepted, which supports that data are normally distributed [46].Table 1
Descriptive statistics.
CO2FDIGDPFRIHCIREEMean6.7012750.69093412.4748040.741942.2127401.271154Median6.6570960.73292612.4629945.291672.2461711.257401Maximum7.0124970.79087513.0363548.000002.5664271.408900Minimum6.3746570.50921011.8834423.000001.8254411.177162Std. dev.0.2360870.0863200.3676849.0107220.2203680.062061Jarque–Bera3.2926534.4744652.0699343.4517291.5252133.515126Probability0.1927570.1067540.3552380.6654900.4664490.172465Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.


## 3.2. Analytical Techniques

### 3.2.1. Narayan and Popp [47] Unit Root Test

Before examining the long run relationship among variables in models 1 to 3, we employ Narayan and Popp (NP) unit root test to check the integration order of variables. The NP test is based on the methodology of augmented Dickey–Fuller (ADF) test. The main reason of using the NP test is that it takes into account the structural break. Due to its high power in accurately identifying the structural breaks, the test is considered superior over other conventional unit root tests. NP test can trace up two structural breaks in the data series both in level and difference form. Identifying the structural breaks is important due to the fact that ignoring these may provide biased and inconsistent results [47].


### 3.2.2. Bayer and Hanck (BH) Cointegration Test (without Structural Breaks)

To check the cointegration among variables in models, this study employs Bayer and Hanck cointegration test, popularized by Bayer and Hanck [48]. The test is based on the methodologies of Johansen (J), Engle-Granger (EG), Boswijk (Bo), and Banerjee (Ba) tests. The necessary condition for employing BH test is that all variables must be first-difference stationary. The test equation is given as(2)EG−J=−2lnPEG+lnPJ,EG−J−Bo−Ba=−2lnPEG+lnPJ+lnPBo+lnPBa,where P is for p values of each cointegration test.


### 3.2.3. Maki Cointegration Test with Structural Breaks

This study also employs the Maki cointegration test, which tests the long-run relationship among variables in the presence of structural breaks. Maki cointegration is superior to other cointegration methods due to its power to identify more than two structural breaks. The Maki test equation for regime shift and trend is given as(3)FDt=α+∑i=1kπiDi,t+β′Xt+γt+∑i=1kδitDi,t+∑i=1kβi′ZtDi,t+εt,where, in equation (3), X is for all explanatory variables such as FDI, GDP, FRI, REE, and HCI. Similar to the BH test, the prerequisite for the Maki test is that all variables must be first-difference stationary. α is drift and γ is for the trend coefficient.


### 3.2.4. Fully Modified Ordinary Least Square (FMOLS) and Frequency Domain Causality Test

For long-run association between variables in models 1–3, this study uses the FMOLS method. It is intuitionally clear that FMOLS is an asymptotically unbiased and efficient estimator. To overcome the problem of correlation between explanatory variables and cointegration equations, FMOLS is the superior to other estimators. Moreover, to test the causal relationship among the variables, this study uses frequency domain causality test. Frequency domain causality test finds out the short-, medium-, and long-run causal relationship among the variables. The method is superior to other causality tests due to its power in removing the seasonal variations in dataset with short period of time.


## 3.2.1. Narayan and Popp [47] Unit Root Test

Before examining the long run relationship among variables in models 1 to 3, we employ Narayan and Popp (NP) unit root test to check the integration order of variables. The NP test is based on the methodology of augmented Dickey–Fuller (ADF) test. The main reason of using the NP test is that it takes into account the structural break. Due to its high power in accurately identifying the structural breaks, the test is considered superior over other conventional unit root tests. NP test can trace up two structural breaks in the data series both in level and difference form. Identifying the structural breaks is important due to the fact that ignoring these may provide biased and inconsistent results [47].


## 3.2.2. Bayer and Hanck (BH) Cointegration Test (without Structural Breaks)

To check the cointegration among variables in models, this study employs Bayer and Hanck cointegration test, popularized by Bayer and Hanck [48]. The test is based on the methodologies of Johansen (J), Engle-Granger (EG), Boswijk (Bo), and Banerjee (Ba) tests. The necessary condition for employing BH test is that all variables must be first-difference stationary. The test equation is given as(2)EG−J=−2lnPEG+lnPJ,EG−J−Bo−Ba=−2lnPEG+lnPJ+lnPBo+lnPBa,where P is for p values of each cointegration test.


## 3.2.3. Maki Cointegration Test with Structural Breaks

This study also employs the Maki cointegration test, which tests the long-run relationship among variables in the presence of structural breaks. Maki cointegration is superior to other cointegration methods due to its power to identify more than two structural breaks. The Maki test equation for regime shift and trend is given as(3)FDt=α+∑i=1kπiDi,t+β′Xt+γt+∑i=1kδitDi,t+∑i=1kβi′ZtDi,t+εt,where, in equation (3), X is for all explanatory variables such as FDI, GDP, FRI, REE, and HCI. Similar to the BH test, the prerequisite for the Maki test is that all variables must be first-difference stationary. α is drift and γ is for the trend coefficient.


## 3.2.4. Fully Modified Ordinary Least Square (FMOLS) and Frequency Domain Causality Test

For long-run association between variables in models 1–3, this study uses the FMOLS method. It is intuitionally clear that FMOLS is an asymptotically unbiased and efficient estimator. To overcome the problem of correlation between explanatory variables and cointegration equations, FMOLS is the superior to other estimators. Moreover, to test the causal relationship among the variables, this study uses frequency domain causality test. Frequency domain causality test finds out the short-, medium-, and long-run causal relationship among the variables. The method is superior to other causality tests due to its power in removing the seasonal variations in dataset with short period of time.


## 4. Results and Discussion

Table2 provides results of Narayan and Popp [47] unit root test with structural breaks. The results indicate that all variables are nonstationary at the level. It indicates that the mean and variance for these variables fluctuate overtime. It also indicates that the conditional probability distribution changes over time for CO2, GDP, FDI, REE, FRI, and HCI. Moreover, after taking first difference, the data are stationary and the condition probability distribution for variables does not change over time. This suggests that all variables, i.e., FDI, CO2, REE, HCI, and GDP, are stationary at I (1). The Narayan and Popp [47] test also detects possible structural breaks in the data. The key structural breaks are 2001 and 2008. The structural break year 2001 is linked with China inclusion into the world trade organization (WTO) [8]. Similarly, the structural break for the year 2008 is for the financial crises. For the economy, both these structural break years, i.e., 2001 and 2008, have both long-lasting positive and negative impacts on the economy. Post 2001, China exports grew faster and post 2008 China economy suffered due to financial crises [15].Table 2
Narayan and Popp [47] unit root test.
VariablesI (0)I (1)BreaksBreaks in levelBreaks in level and trendBreaks in levelBreaks in level and trendCO2−4.306∗−0.795−5.725∗∗∗−44.20∗∗∗2003–2005GDP−3.669−4.433−6.483∗∗∗−14.002∗∗∗2001–2008FDI−2.755−3.871−7.870∗∗∗−1.0554∗∗∗2001–2004HCI−3.124−4.718−6.254∗∗∗−9.347∗∗∗1995–2005REE−3.324−4.571−7.534∗∗∗−8.097∗∗∗2000–2002FRI−3.019−3.292−8.687∗∗∗−9.646∗∗∗2005–2008Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.Table3 provides the long-run cointegrating association among financial risk index, financial development, human capital index, GDP, renewable energy electricity output, and carbon emissions is confirmed through Bayer–Hanck and Maki cointegration tests. The results of Bayer–Hanck and Maki cointegration confirmed that there is a long-run cointegrating relationship between financial risk index, financial development index, human capital index, GDP, and renewable energy electricity output with carbon emissions for China. Moreover, Maki cointegration also provides multiple structural breaks, i.e., 2001, 2008, and 2014. However, the most important structural breaks associated with different real economic events, i.e., 2001, when China joined WTO, and 2008, for the global financial crises. In order to analyse the effect of these two structural breaks, this study used dummy variables in the main regression analysis to empirically test its effect on carbon emissions.Table 3
Cointegration analysis using Maki and Bayer–Hanck approaches.
Bayer–Hanck [48]Engle GrangerJohansenBanerjeeBoswijkStatistics (P values)−6.8819∗∗∗ (0.0026)84.5881∗∗∗ (0.000)−5.1755∗∗∗ (0.0028)125.1155∗∗∗ (0.0000)Combined statisticsEngle Granger–JohansenEngle Granger–Banerjee–Boswijk57.890∗∗∗ (0.000)124.908∗∗∗ (0.000)Maki [49]ResultsStatisticsKey structural breaksRegime shiftRegime shifts with trend−9.52∗∗−11.107∗∗2001–2008–2014Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.Table4 shows results obtained from both fully modified and dynamic ordinary least square, i.e., FMOLS and DOLS. The results are divided into two parts: first, without considering structural breaks, and second, considering structural breaks and introducing dummy variables. The empirical results confirmed that 1% rise in financial development causes carbon emissions to decline by −0.191%, −0.159%, −0.128%, −0.117%, −0.169%, and −0.129%, respectively, for China. The effect of FDI on CO2 emissions is similar to the empirical outcomes of Tamazian et al. [10] and Khan et al. [35]. The negative effect of FDI on carbon emissions is justified due to declining production cost, enhancing products’ competitiveness, using energy-efficient technology, and controlling energy cost. Project-related financial development is in line with carbon reduction goals, and most focus is given to cleaner energy. Moreover, financial institutions finance those projects which are environmentally friendly and do not increase emissions. Furthermore, the results suggest that increasing economic growth causes carbon emissions to rise in China. This indicates that increasing carbon emissions in China is mostly associated with rise in economic activities. China transformation from a low-income country to an emerging economy causes carbon emission to rise extensively since the last few decades. Starting from economic regulations of the 1990s and later joining world trade organization (WTO) cause emissions to increase. China is the largest carbon emitter and contributes 27.3% to worldwide CO2 emissions [6]. Due to the process of industrialization and globalization, continuous increase in economic activities, the economy is expected to continuously grow in the next decades, which has serious implications for the environmental sustainability of the country.Table 4
Long-run results.
VariablesWithout structural breaksWith structural breaksFMOLS coefficients (std. er)DOLS coefficients (std. er)FMOLS coefficients (std. er)DOLS coefficients (std. er)FMOLS coefficients (std. er)DOLS coefficients (std. er)FDI−0.191∗∗∗ [0.0451]−0.159∗∗∗ [0.0350]−0.128∗∗∗ [0.0314]−0.117∗∗∗ [0.0314]−0.169∗∗∗ [0.0294]−0.129∗∗∗ [0.0310]GDP1.332∗∗∗ [0.0980]1.417∗∗∗ [0.321]1.230∗∗∗ [0.0858]1.257∗∗∗ [0.1028]1.355∗∗∗ [0.134]1.327∗∗∗ [0.1546]FRI0.0044∗∗∗ [0.0022]0.00442∗ [0.0620]0.0035∗ [0.0018]0.0036∗ [0.0020]0.0052∗∗ [0.0020]0.0048∗ [0.0023]HCI−0.905∗∗∗ [0.2112]−0.873∗∗∗ [0.2661]−0.882∗∗∗ [0.1696]−0.918∗∗∗ [0.1921]−0.897∗∗∗ [0.2319]−0.892∗∗∗ [0.2646]REE−0.442∗∗∗ [0.1493]−0.356∗∗ [0.1606]−0.285∗∗ [0.1429]−0.253∗∗∗ [0.0561]−0.488∗∗∗ [0.1532]−0.385∗∗ [0.1752]D1-2001——0.039∗∗ [0.0186]0.030∗∗∗ [0.0087]——D2-2008————−0.0063∗∗∗ [0.0011]−0.0064∗∗∗ [0.0019]Constant−1.051∗∗∗ [0.2214]−1.230∗∗∗ [0.2712]−1.121∗∗∗ [0.2619]−1.413∗∗∗ [0.371]−1.267∗∗∗ [0.2089]−1.101∗∗∗ [0.331]R-squared0.9710.9630.9820.9620.9510.967Coefficient diagnostic testsNormality test (Jarque–Bera)Breusch–Pagan–Godfrey heteroscedasticityBreusch–Pagan–Godfrey serial correlation LM3.773 (0.151)1.464 (0.2324)1.021 (0.3764)Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.In contrast, increasing financial risk index (improvement in financial risk index) for China is positively related with CO2 emissions. The rise in carbon emissions from financial risk index improvement is associated with many factors. These factors include curbing foreign debt service and current account services such as percentage of goods and service exports, improvement in net international liquidity, and stability of the exchange rate. Most of these factors are associated with rise in different economic activities which in turn cause energy consumption from fossil fuel to rise and increase carbon emissions [50].Furthermore, the results show that human capital is negatively related with CO2 emissions, which support the findings of Yang et al. [51]. Human capital is helpful in implementing the eco-friendly technologies, which enhance energy efficiency and hence improve the environmental quality. Similarly, renewable energy electricity output is also helpful to control carbon emissions. As increasing the shares of renewable energy in total electricity generation by 1% cause reduce in carbon emissions by −0.442%, −0.356%, −0.285%, −0.253%, −0.488%, and −0.385%. In the case of China, increasing shares of renewable energy in electricity is rising and causing emissions to decline. The use of renewable energies in electricity generation is helpful in restricting pollution in the atmosphere. Therefore, renewable energy in electricity generation may be considered as an optimal solution to energy security and environmental sustainability. Hence, without reducing the output level, generation of additional emissions can be reduced via development in environment-related technology by increasing the share of renewable energies in electricity generation. The structural break for the year 2001 (China entry into WTO) causes carbon emissions to increase by 0.039% and 0.030%. On the contrary, structural break for the year 2008 (global financial crises) causes carbon emissions to reduce by −0.0063% and −0.0064%. The overall results are significant with normal distribution and no autocorrelation and heteroscedasticity is detected.The structural break for the year 2001 (China entry into WTO) causes carbon emissions to increase by 0.039% and 0.030%. On the contrary, structural break for the year 2008 (global financial crises) causes carbon emissions to reduce by −0.0063% and −0.0064%. The overall results are significant with normal distribution, and no autocorrelation and heteroscedasticity is detected.Table5 shows the outcome from frequency domain causality test and indicates that GDP, FDI, FRI, REE, and HCI cause carbon emissions in the short run, medium run and long run. The results show that any policy targeting these factors shall affect carbon emissions for China [52].Table 5
Frequency domain causality test.
Causality directionLong run,ω=0.05Medium run,ω=1.5Short run,ω=2.5GDP to CO213.586∗∗∗11.316∗∗∗13.275∗∗∗FDI to CO27.177∗∗5.332∗9.989∗∗∗HCI to CO27.602∗∗7.572∗∗5.721∗REE to CO26.432∗6.721∗∗4.126FRI to CO29.217∗∗∗6.031∗∗6.451∗∗Note:P≤0.01, <0.05∗∗, and 0.1∗, respectively.


## 5. Conclusion

On the nexus between financial development and carbon emissions, very few studies are available especially considering the role of financial risk index, renewable energy electricity, and human capital. Therefore, this study attempts to fill the gap by studying the impact of financial development on carbon emissions for China from 1988–2018. This study introduces novel variables in the model, i.e., financial risk index, renewable energy electricity, and human capital index by employing novel time-series econometric tests. This study used Narayan and Popp unit root test with structural breaks, Bayer–Hanck and Maki cointegration tests, and fully modified and dynamic OLS techniques for long-run results. Moreover, frequency domain causality test is employed for short-, medium-, and long-run causal relationship among the variables.Narayan and Popp unit root tests detect important structural breaks linked with the structural changes in China economy, i.e., 1993, 2001, and 2008. The cointegration tests, i.e., Maki and Bayer–Hanck confirmed long-run cointegrating relationship for carbon emissions, financial risk index, financial development index, renewable energy electricity, gross domestic product, and human capital index. Moreover, the empirical outcomes for FMOLS and DOLS indicate that financial risk index and gross domestic product cause carbon emissions to rise. In contrast, renewable energy electricity, financial development, and human capital index limited carbon emissions. Moreover, the structural break year 2001 causes to surge carbon emissions, and on the other hand, the break year 2008 helped to limit carbon emissions in China. Based on the outcomes from this study, the following policy implications are recommended: (1) promotion of financial development is in line with the goals set by China to achieve its target of limiting carbon emissions; (2) financial risk index improvement is indeed good for the overall economy; however, regulations should be encouraged to ensure that further expansion does not harm the environment and it should be environmental friendly with less use of fossil fuels; (3) shares of renewable energy should be increased in total electricity output especially through investment in energy industry; (4) more capital should be devoted towards human capital as controlling carbon emissions can only be done through quality human capital.



---

*Source: 1025669-2021-07-27.xml*