Determining the Impact of Financial Development on the Environment based on Biquadratic Equation in ASEAN Countries45304

EDESUS Proceeding 2019 167 of 1531Determining the Impact of Financial Development on the Environment based on Biquadratic Equation in ASEAN Countries Thanh Le 1 * , Khuong Nguyen 2 , An

EDESUS Proceeding 2019 (167 of 1531)

Determining the Impact of Financial Development on the Environment based on Biquadratic Equation in ASEAN Countries

Thanh Le (1) (*) , Khuong Nguyen (2) , Anh Phan (3) , Quang Vu (4) , Lien Vu (5)

(1) VNU University of Economics and Business, Vietnam National University, Hanoi, Vietnam

(2) Thaibinh Department of Finance, Thai Binh, Vietnam

(3) Banking Academy, Hanoi, Vietnam

(4) Hanoi Pedagogical University 2, Hanoi, Vietnam

(5) State Audit Office of Vietnam, Hanoi, Vietnam

*Correspondence: ltthanh@vnu.edu.vn

Abstract: This study explores the impact of financial development in reducing degradation during

the process of economic development and international trade in ASEAN countries A new approach based on biquadratic equation was implemented in period of 24 years from 1990 to 2014,using Panel- ARDL method to examine the factors Outcomes of the research indicate a positive contribution of financial development in long-term in countries: Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam Financial development helps to reduce negative impact on increasing emission from factors such as: fuel and renewable energy consumption, foreign direct investment, economic growth and trade openness This implies that enhancing credit to private sector helps to allocate resources effectively and reduce negative impact to the environment

Keywords: Environmental Kuznets Curve Hypothesis; Pollution Haven Hypothesis; financial

development; CO2 emission; biquadratic equation

1 Introduction

The issue of environmental pollution is attracting more and more attention from countries all around the world Governments desire to pursue sustainable development policy, which can boost the economy while minimize environmental pollution Important policies such as using green and renewable energy, economic management, investment and partnership are studied and proposed

Research shows that the relationship between economic development and environment follows the inverted U shape in the Environmental Kuznets Curve – EKC (Kuznets, 1955) This hypothesis illustrates that income intensifies pollution up to a certain level, after that, it restrains environmental degradation Other studies provided N-shape explain that, at very high-income levels, the scope of economic activity is broaden, therefore, negative impact on the environment cannot be rebalanced (Grossman and Krueger, 1995) However, some other researches considered to replace income by trade openness (TRADE) (Jayanthakumaran, Verma and Liu, 2012; Akin, 2014) This commences standpoint that the influence of these factors on the environment focuses on two channels Initially, they will

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increase demand for energy and natural resources, so they enlarge emissions (positive relationship) After that, by promoting advanced technology, they will reduce environmental pollution (negative relationship)

A common feature of those studies is they will establish an equation in which GDP2

or TRADE2 coefficients describe non-linear relationships When it is negative and statistically significant, we can confirm the existence of EKC and determine an extremely point, after that, increasing GDP (TRADE) can reduce emissions Other factors are also added in model as proposed by the Lau, Choong and Eng (2014), who affirmed that the relationship between GDP and CO2 emissions were only expressed when adding two variables such as foreign direct investment and trade openness In addition, Ang (2007), Chen and Huang (2013) realized economic growth affected environment through energy consumption, etc

Compared with previous researches, we also choose energy consumptions (EC), renewable energy consumptions (REC), foreign direct investment (FDI), income per capita (GDP) and trade openness (TRADE) However, we choose financial development (FD) to play a major sector for ASEAN countries from 1990 to 2013, instead of GDP or TRADE

The main difference in this study is that we will establish a biquadratic equation with assumption that balance will not auto-recover immediately Financial development continuous enlarging emission once again before decreasing degradation at the end of process In addition, our aim is to verify whether financial development helps to reduces damage from energy consumptions, foreign direct investment… through a multiplication between them We combine an autoregressive distributed lag model-ARDL with pooled mean group estimation - PMG in Eviews 9.0 software This has led to new discoveries about relationship between financial development and environment (i) Our results showed an evidence of inverted- W graph implies the relationship occurs sequentially positive- negative- positive- negative Financial development increases pollution initially, up to a certain level (first extremely point) it reduces emission, down to a certain level (second extremely point) it continuous broaden degradation, after that it reduces pollution (at third extremely point).(ii) We also show that financial development have contribution in decreasing the influence of the other factors on environment for ASEAN countries This encourages developing countries in general and ASEAN members in particular prioritize financial resources for the private sector in order to achieve economic efficiency and reduce negative effect on environment

The remainder of this study is organized as follows: section 2 reviews the relation between CO2 emissions and other factors Section 3 presents data collection and methodology The empirical results are shown in Section 4 Section 5 concludes the study

2 Literature reviews

As introduced in Section 1, empirical studies on the relationship between economics and environment based on the EKC theory mainly focuses on two topics: (i) determine relationship complies the invert U-shaped as Ang (2007); Jalil and Mahmud (2009) and (ii)

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calculate transition thresholds as environmental quality improves following the increase in per capita income It means that we can reduce degradation by improving income (Kuznets, 1955) However, Lacheheb, Rahim and Sirag (2015) indicated it not exist in Algeria and Farhani, and Ozturk (2015) denies it in case of Tunisia

When discussing about financial development, most of the studies agree that it not only stimulates economic growth, but also acts as an important determinant of the quality

of the environment, including positive and negative relationship

(i) in positive ways, financial development increases CO2 emissions was found in Sadorsky (2010); Acaravci and Ozturk (2010); Bouttabba (2014) According to Omri et al (2015) stock market development helps listed enterprises to lower financing costs, increase financing channels This enables firms to invest in new projects, expand scale, which promotes the usage of natural resources and spread pollution

In another hand, financial development may attract FDI (Desbordes & Wei, 2014) and international trade It encouraged country to improve its economic performance, including strengthening investment, trade cooperation, etc., which affects indirectly on environmental degradation (Phimphanthavong, 2014)

(ii) in negative way, high level of financial development has given many governments access to new and cheaper sources that can afford to investment in technical innovation and advanced technology to decrease emissions In addition, countries could save a large amount of money because of not having to pay expenditure for environmental protection since much of activities are duty of public sector Some results showed that financial development reduces emissions in countries as Middle East and North Africa (MENA) of Omri et al (2015); Indonesia of Shahbaz et al (2013) Financial development not only contributed on economic growth of Tunisia (Farhani & Ozturk, 2015) but also reduced energy consumption in Malaysia (Islam et al., 2013) and degradation in both short and long term in Pakistan (Shahbaz, Islam & Butt, 2011)

Moreover, environmental protecting activities are also required in the investment

of private firms through environmental standards, output products consumed in developed countries, facilitating capital mobilization and risk sharing It led to conclusion that through improving governance, financial sector development can spur greater environmental performance This indicates that well-developed financial system may provide enough incentive for firms to lower their CO2 emission On another hand, companies’ revenue will

be affected strongly when they cause environmental violations, because people can boycott their goods Investors on the Korean Stock Exchange strongly react to the disclosure of such news of companies in Korea are not complying with Korean environmental laws and regulations The firms’ capitalization or firm’s market valuation will be decreased (Dasgupta et al., 2006)

Besides determining the existence of curve, studies also attempt to explain linear relationship For example, Apergis and Payne (2009); Mercan and Karakaya (2015); Ali, Yusop and Hook (2015) show a positive impact of energy consumption on emissions According to Odhiambo (2009), energy consumption promotes economic expansion and

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financial development This also makes sense with developed countries where financial indicators make the significant contribution to total GDP (Al-Mulali and Sab, 2012) However, it also generates a large proportion of CO2 emissions, one of main causes of global warming Hence, some organization supports renewable energy According to data aggregated by the International Panel on Climate Change (IPCC, 2011) life-cycle global warming emissions associated with renewable energy including manufacturing, installation, operation and maintenance, and dismantling and decommissioning are minimal There was also an evident decrease of CO2 emissions per capita (Silva, Soares & Pinho, 2012) Attiaoui et at (2017) provided the long-run PMG estimates showed that non-renewable energy consumption and gross domestic product increase CO2 emission (CO), whereas REC decreases it Meanwhile, Apergis et al (2010) suggested that it did not contribute to reduce CO2 emissions in the short run In G7 countries, renewable energy consumption makes to increase both GDP and CO2 emissions in the long term (Sadorsky, 2010)

People supporting pollution deem with their own hypothesis that FDI is one of the main factors causing pollution According to Pollution haven hypothesis, in developed countries, highly expensive costs for waste management caused companies tent to move production facilities to developing countries through international trade and FDI, which broadens pollution in these countries Balibey (2015) indicated the positive relationship between FDI and CO2 emission Al-Muladi (2012) emphasized that FDI was the major cause

of the expansion of CO2 emissions in Middle Eastern countries However, FDI also promotes technology transfer that will help to control pollution in the country receiving investment through environmental standards and output products In fact, FDI contributes to boost economic growth and energy consumption without raising CO2 emissions in G20 countries (Lee, 2013) and BRICSAM (Khachoo and Sofi, 2014) and decreases CO2 in Turkey (Ozturk and Oz, 2016) Kivyiro and Arminen (2014) defined both positive and negative effects of environmental pollution in sub-Saharan Africa

Last but not least, by reallocating resources between more and less polluted sectors, trade openness affects directly CO2 emissions (Jalil and Mahmud, 2009); Sharma, 2011; Lau, Choongand Eng, 2014) This finding is also identified in the case of Iran economy according

to Bouttabba (2014) In contrast, Maji and & Habibullaha (2015) provide evidence that trade liberalization encourages the change in production technology, enhancing comparative advantages for developing countries, creating more financial resources to reduce pollution and facilitates growth towards diversification in order to avoid excessive dependence on resource-based exports

3 Methodology and data

The main objective of this research is to analyze whether financial development reduces and determine if some identified factors reduce CO2 emissions To answer this question, we estimate equation (1) as follow

EDESUS Proceeding 2019 (171 of 1531) CO= f(FD 2 , FD 4 , EC, REC, FDI, GDP,TRADE, FD*EC, FD*REC, FD*FDI,

where: CO: per capita of CO2 emissions (metric tons per capita); FD: financial development, is represented by domestic credit to private sector, GDP: per capita income (current US$); EC: per capita of energy consumption (kg oil per capita); REC: renewable energy consumption (% of total final energy consumption); FDI: foreign direct investments net inflow (BoP, current US$); TRADE: trade openness, is calculated as the ratio of the total value of exports and imports to total real GDP (%)

In equation (1) we consider FD follows two channels Firstly, FD has a non-linear relationship with environment In previous studies, this relationship was illustrated by invert U-shaped with a negative and statistically significant coefficient as GDP2 or TRADE2 However, we develop a biquadratic equation with expectation that the coefficient of FD4 is negative and FD 2 is positive The invert –W shape is described as follows figure 1:

Fig 1 The invert –W shape between financial development and environment

Source: Author’ assumption

(i) Initially, financial development improves economic development and boost using energy and natural resource Hence, increasing it to enlarge degradation (positive relationship)

(ii) Next, financial development promotes advanced technology that helps to reduce environmental pollution (negative relationship) It has given many governments access to new and cheaper sources to investment in technical innovation and advanced technology to decrease emissions

(iii) At middle–upper level, manufacture cost is lower; make goods become cheaper, people consume more, then increase pollution (positive relationship)

(iv) At very high level, financial development can afford to have high technology, people set higher domestic environmental standards and output products, emission decreases (negative relationship)

In the second aspect, all EC, REC, FDI, GDP and TRADE have direct impact on emission and they have linear relationship We consider FD has a positive or negative effect

on the polluting process of these factors through a multiplication between them When the coefficient is negative or positive but smaller it can be seen that FD has positive role for the environment

Method estimation

There have been many modeling techniques used in the case of single country or panel data of a group nation In the first case, with some methods such as Ordinary least squares- OLS model, Vector error correction model- VECM and Johansen cointegration, the

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existence of hysteresis and constraints observed sample could affect the results of analysis, hence ARDL is chosen It is standardized least-squares regression models that include the latency of dependent variable as well as the explanatory variables in the model In second case, however, with panel data related to the separate effects, ARDL models may cause deviation problems by correlation between mean-difference repressors and the error components To solve this problem, we used Pooled Mean Group (PMG) method proposed

by Pesaran et al (1999) It allows consideration of the coherent form of ARDL model and adjusts it with panel data by allowing for shear coefficients, short-term coefficients, and co-composition changes between variables

Economic model

1 1

p

it it ij it j i ij

j



where Sit1 Yit1  xit1 2)

With Sit-1 the variable arising from the long-term equilibrium at any time for group

i (country) and  is the correction factor (EC), which reflects the correction rate; Vector θ reflects the long-term elasticity of CO2 emission with explanatory variables (X) The short-term regression coefficient of the explanatory variables for CO2 emission is expressed by the coefficient δ Vector ηit is the unobserved error (country-specific, invariant over time) and the vector ζit is observable error

Data are collected from the World Bank Indicator in the period 1990 –2013 of ASEAN countries The selection of ASEAN because all member states (i) are low-income, middle-income countries; (ii) co-operate in the pursuit of sustainable economic development; (ii) their economic role is increasingly appreciated and plays an important role in the Asia-Pacific To ensure model accuracy, we only select 7 countries which has full indicators including: Indonesia, Malaysia, Singapore, Thailand, Philippines, Cambodia and Vietnam

4 Results

Table 1 reports summary statistics of the annual data For each country, we have 24 observations for per variable Combining 7 countries, we have total 168 observations Result

of ADF test confirms that all variables are non-stationary at level but stationary at first difference, at the 5% level of significant

Table 1 Results of statistical analysis

Source: Calculation from Worldbank indicate

According to Pesaran et al (1999), the PMG estimator constraints the long run coefficients to be identical, but allows the short run coefficients and error variances to differ across group Result of Panel Cointegration test is presented in Table 2 At the significant level 5%, so we could reject null hypothesis of no cointegration, accept alternative hypothesis All variables have cointegration so it is feasible to use Panel- ARDL model

Table 2 Panel Cointegration test Individual intercept Individual intercept

and individual trend No intercept or trend Statistic Prob Statistic Prob Statistic Prob Panel PP-Statistic -9.077 0.00 -15.982 0.00 -2.894 0.00 Panel ADF-Statistic -2.011 0.02 -3.848 0.00 -1.030 0.15

Source: Author’s calculation

Table 3 Results of estimation

D(FD*GDP) 0.445 0.31

Log likelihood 325.602

Source: Author’s calculation

In the long- run, with the coefficient of FD4 is negative and significant in Table 3, our result confirms an evidence of biquadratic equation This implies that finance for private sector in ASEAN countries helps to reduce emissions However, unlike the EKC curve, our result indicates that rebalance does not happen immediately The economy will continue to enlarge pollution before the increasing in credits helps to reduce pollution In other words, the inverted U curve process will continue again

Both EC and REC have positive impact on emission An increase of 1% in EC leads

to 3.374% emissions, higher than 1.200% of REC It means that using renewable energy helps

to reduce a large amount of CO2 emission Meanwhile, with coefficient of both FD*EC and

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FD*REC are negative means financial development reduces the demand for energy This result is in contrast to Sadorsky (2010); Bouttabba (2014) More domestic credit to the private sector facilitates them to innovate advanced environmentally-friendly technologies to reduce pollution

Trade openness and GDP also have positive relationship with emission Each an added percent of TRADE or GDP increases 1.714 % and 1.209 % environmental pollution This is explained that almost country in ASEAN is low middle income, except for Singapore and Thailand, has less commercial activities, mainly imported Financial development has the effect of improving incomes and promoting export-oriented production, thereby contributing to the reduction of pollution It can be seen that increasing credits, financial resources for commercial activities help to reduce 0.472% of emissions and for economic growth decrease emissions 0.297% This implies that increasing credit to the private sector helps achieve the goal of reducing environmental pollution

5 Conclusion

By using Panel –ARDL, our research indicates that biquadratic equation financial development has good contribution on reducing emission The result indicates a positive contribution of financial development in long-term in ASEAN countries It helps to reduce the negative impact on increasing emission from factors such as: fuel and renewable energy consumption, foreign direct investment, economic growth and trade openness This implies that enhancing credit to private sector helps to allocate resources effectively and reduce negative impact on the environment

As our result, the extreme point at 203% gross domestic product, beyond that, increasing financial development would make degradation reduced It still has a big gap for each country to reach this point However, the impact of FD on other factors is significant,

so it is important to continue to allocate finance resource to the private sector In addition,

by generating less influent to emissions, we support the usage of renewable energy as a green energy strategy for economic development and environmental protection

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