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Is CO2 emission a side effect of financial development? An empirical analysis for China

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Abstract

Based on panel data for 29 Chinese provinces from 1995 to 2012, this paper explores the relationship between financial development and environmental quality in China. A comprehensive framework is utilized to estimate both the direct and indirect effects of financial development on CO2 emissions in China using a carefully designed two-stage regression model. The first-difference and orthogonal-deviation Generalized Method of Moments (GMM) methods are used to control for potential endogeneity and introduce dynamics. To ensure the robustness of the estimations, two indicators measuring financial development—financial depth and financial efficiency—are used. The empirical results indicate that the direct effects of financial depth and financial efficiency on environmental quality are positive and negative, respectively. The indirect effects of both indicators are U shaped and dominate the shape of the total effects. These findings suggest that the influences of the financial development on environment depend on the level of economic development. At the early stage of economic growth, financial development is environmentally friendly. When the economy is highly developed, a higher level of financial development is harmful to the environmental quality.

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Notes

  1. In a recent study, Yuxiang and Chen (2011a) investigated the relationship between resource abundance and financial development in China, and their empirical analysis confirms that the link between mineral resource abundance and financial development is negative. Considering the fact that the usage of mineral resources, especially fossil energy, is generally positively related with environmental pollution, the conclusions of this study suggest that financial development benefits environmental quality in China.

  2. It should be noted that, partly because of Stern (2004) and Chowdhury and Moran’s (2012) critiques, the usage of different pollution indicators is one important reason why estimation results of EKC are controversial; some researchers have tried to utilize composite or comprehensive indicators gauging environmental pollution. For instance, Zilio and Recalde (2011) used energy consumption as a proxy for environmental pressure to investigate the existence of EKC in Latin American and Caribbean countries. Hao and Liu (2016) used the Air Quality Index (AQI) as a comprehensive indicator for air pollution to examine the existence of EKC for air pollution in China.

  3. China has not monitored city-level concentrations of PM2.5 concentration until 2012. In the beginning, the air qualities of only several megacities, such as Beijing and Shanghai, are reported. The number of cities of which PM2.5 concentrations and AQI are monitored has been growing steadily. As of 2015, the air quality in all of the 338 prefecture-level cities in mainland China is monitored and reported on an hourly basis.

  4. Another important resource for both CO2 and PM2.5 pollutions is automobile exhaust. There has been growing literature about the air pollution from the transportation industry in China, such as He and Chen (2013), Zhang et al. (2015), Chen and He (2014), Yang and He (2016a, b).

  5. Unlike conventional pollutants such as SO2 and NOx (nitrogen oxides), CO2 does not cause direct harm to human beings and the environment; therefore, the emissions of CO2 have not been paid much attention or seriously controlled for a long time. In recent years, as China’s CO2 emissions have ballooned and China has become the largest CO2 emitter of the world, China has faced mounting international pressures to curb CO2 emissions. In 2009, the Chinese government for the first time set its goal of suppressing the growth of CO2 emissions: China’s intensity of CO2 emissions is due to be reduced by 40–45 % of the 2005 level by 2020. For more information, one can refer to http://www.washingtonpost.com/wp-dyn/content/article/2009/11/26/AR2009112600519.html.

  6. The cubic term of logarithmic GDP per capita is used to explicitly investigate the shape of the EKC curve (Lieb 2003; Kaika and Zervas 2013). Concretely, Kaika and Zervas (2013) have summarized all possible shapes of EKC curves when the cubic term of GDP per capita is incorporated into the regression equation.

  7. The GMM estimators essentially belong to IV methods. For instance, the first-difference GMM was originally developed by Anderson and Hsiao (1981), who suggested that at first the regression equation is first differenced to eliminate the individual effect and then Δy it − 2 = y it − 2 − y it − 3 (or simply y it − 2) is utilized as an instrument for Δy it − 1. Arellano and Bond (1991) further developed the first-difference GMM by choosing the predetermined variables (y i1, y i2, …, y it − 2) as the instrumental variables of Δy it-1 . As pointed out by Baltagi (2005), Arellano and Bond’s GMM estimator contains more information, therefore Arellano and Bond’s GMM is employed to conduct the first-difference GMM estimations in this study.

  8. In fact, some studies using multinational data found that the relationship between financial development and economic development is nonlinear (e.g., Deidda and Fattou 2002; Favarra 2003). In a recent study, Gaffeo and Garalova (2014) found that financial development impedes economic growth in the short run for the Central and Eastern Europe transition countries, although the long-run effects of financial development on economic performance are largely positive.

  9. It is noteworthy that the estimated coefficients of trade openness by OLS in both tables are negative, and the ones by FE are positive but insignificant. These undesirable results reflect the flaws of OLS and FE estimators that are prone to the potential endogeneity problem and the omitted-variable bias. Therefore, the first-difference GMM method is more reasonable and preferred to OLS and FE estimators.

  10. It is noteworthy that, although the majority of the coefficients of FE and OLS estimations carry the same sign as the GMM estimates, many of the coefficients by FE and OLS are insignificant, and the OLS estimate of f d even has a negative sign, opposite to the GMM estimates. These comparisons also reflect the fact that OLS and FE estimators are flawed and would lead to biased results.

  11. The orthogonal-deviation GMM estimation results using f e as the indicator for financial development shown in the second column of Table 4 suggest an inverted-N-shaped relationship between the CO2 emissions and the GDP per capita, although the coefficients of the GDP per capita and its cube are insignificant.

  12. As discussed in Du et al. (2012), trade openness may have both positive and negative effects on CO2 emissions. On the one hand, when more energy-intensive products are domestically produced and exported, higher trade openness means more pollution. On the other hand, trade openness generally encourages technology diffusion, which may help to reduce CO2 emissions. Therefore, the net effect of trade openness on CO2 emissions might be ambiguous.

  13. The reason for choosing 1998–2012 and 1995–2008 as two subsamples is that in 1998 and 2008, two external economic crises affected China’s economy. In late 1997 and 1998, the Asian financial crisis burst and rapidly developed into regional economic crisis, which caused China’s economic growth rate to decrease from two-digit level in 1995 and 1996 to below 8 % in 1998 and 1999. In 2008, a more severe global economic crisis hit China and made China’s economy to enter so-called new-normal period, when China’s economic growth rate is considerably lower than previous levels. Taking into account of the significant influences of external economic crises on China’s economy, 1998 and 2008 are chosen as the beginning and ending time of the two subsamples, respectively.

  14. In recent years, although the shadow banking and underground financial system grew rapidly, bank loans from commercial banks still account for the major sources of credit provided to China’s real economy (Li 2014; Sharma 2014). Moreover, because it is difficult to gauge the scale of the shadow banking system, we use the official statistics for formal banks in China.

  15. There is a growing amount of anecdotal evidence for the relationship between the 2008 economic stimulus package and the increasing air pollution. For example, one could refer to http://theconversation.com/chinas-growing-pains-expect-more-smog-on-the-horizon-12872 and https://www.washingtonpost.com/world/choking-smog-paralyzes-cities-in-northeast-china-closing-schools-airports/2013/10/22/ba2c46d6-3b04-11e3-b0e7-716179a2c2c7_story.html.

  16. Concretely, the range of GDP per capita corresponding to the negative elasticity of f d with respect to CO2 emissions per capita is between 1880 and 19,048 yuan (constant 1978 prices), while for f e , the range is between 712 and 27,202 yuan.

  17. As a reference, see http://www.bloomberg.com/news/articles/2015-06-24/china-moves-to-cancel-cap-limiting-bank-loans-to-75-of-deposits.

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Acknowledgments

The authors acknowledge the financial support from the National Natural Science Foundation of China (71403015, 71521002), Beijing Natural Science Foundation (9162013), and the Scientific Research Foundation for Returned Overseas Chinese Scholars, State Education Ministry of China (20152132001). The authors also appreciate three anonymous reviewers’ helpful and valuable comments. The usual disclaimer applies.

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Authors and Affiliations

  1. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China

    Yu Hao, Zong-Yong Zhang, Hua Liao & Yi-Ming Wei

  2. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Yu Hao, Zong-Yong Zhang, Hua Liao, Yi-Ming Wei & Shuo Wang

  3. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China

    Yu Hao, Zong-Yong Zhang, Hua Liao & Yi-Ming Wei

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  1. Yu Hao
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Hao, Y., Zhang, ZY., Liao, H. et al. Is CO2 emission a side effect of financial development? An empirical analysis for China. Environ Sci Pollut Res 23, 21041–21057 (2016). https://doi.org/10.1007/s11356-016-7315-8

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