The Environmental Consequences of Growth: Empirical Evidence from the Republic of Kazakhstan
Abstract
:1. Introduction
2. The Models and Methods
3. Data
4. The Results
5. Concluding Remarks
Acknowledgments
Author Contributions
Conflicts of Interest
References
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1 | It is worth discussing main theoretical explanations for the EKC hypothesis. One of the main theories explaining the ECK is that the shape of the EKC reflects changes in the demand for environment quality with growth, known as the income effect (Lopez 1994). That is, income growth driven by accumulation of production factors increases firms’ demand for pollution inputs. At the same time, demand for environmental quality rises with growth as the willingness to pay for a clean environment increases. An alternatively widely cited explanation for the EKC is the threshold effect (John and Pecchenino 1994; Selden and Song 1995). That is, since pollution could be unregulated entirely at the early stage of development, pollution at first tends to rise with growth. After some threshold has been reached and regulation is implemented, however, pollution tends to decline with growth. The increasing returns to abatement effect argues that as the scale of abatement increases, its efficiency tends to increase, which makes abatement more profitable and hence reduces pollution levels as more abatement is undertaken (Andreoni and Levinson 2001). Finally, the most recent explanation for the EKC is that growth tends to shift economic production system from high polluting industries to low polluting industries, known as the structural change effect (Marsiglio et al. 2016). |
2 | Another advantage of this method is that it has been proven to have superior small sample properties, which makes it a good choice for our sample of less than 30 annual observations compared to other cointegration methods (i.e., Johansen method). |
3 | The authors thank an anonymous referee for raising this issue discussed here. |
4 | It is worth mentioning that since Kazakhstan’s independence from the USSR in 1991, the collapse of demand for Kazakhstan’s heavy industry products has resulted in a sharp contraction of the economy during the 1990s. Since the beginning of economic reforms and opening up to the outside world in the early 2000s, however, Kazakhstan’s economy has grown sharply (except for the global financial crisis in 2009). As illustrated in Figure 1, therefore, CO2 emissions per capita, energy consumption per capita, and income per capita have persistently declined up to 2000 and have increased since then. |
5 | It should be pointed out that when there is no trend in ADF and PV tests, the unit root hypothesis for the two income variables cannot be rejected. With the time trend included, however, we can strongly reject the null for both variables. Thus, the best characterization of the two income variables seems to be as a trend-stationary process; that is, a process that is stationary about its time trend. |
6 | The Schwarz Bayesian Criterion (SBC) generally used for low small size of studies like this paper also identifies ARDL (1, 0, 0, 0) as the optimal model. |
7 | Among five cases of testing for cointegration, case III (unrestricted intercept and no trend) is used for the analysis. The associated 5% and 10% critical value bounds are (3.23, 4.35) and (2.72, 3.77), respectively, which are taken from Table CI (iii) Case III: unrestricted intercept and no trend on p. 300 of Pesaran et al. (2001). |
8 | In order to capture the effects of technological progress or enhanced environmental awareness on CO2 emissions, a time trend is included in estimating Equation (2). However, our findings are more conclusive when the F-test is applied to Equation (2) without a time trend. Further, a time trend is not statistically significant even at the 10% level. Hence, a time trend is excluded from the final model. |
9 | As a cross-check, we also perform the bounds t-test of H0: σ0 = 0 against H1: σ0 < 0. If the null is rejected using the upper critical value bounds tabulated by Pesaran et al. (2001, pp. 303–4), this would support cointegration relationship among the variables. In our case, the t-statistic on ct−1 in −4.58. When we look at Table CII(iii) (Case III: unrestricted intercept and no trend) on p. 303 of Pesaran et al. (2001), the associated 5% and 10% critical value bounds for the t-statistic are (−2.86, −3.78) and (−2.57, −3.46), respectively. Even at the 5% level, therefore, this result confirms our conclusion that there is a long-run relationship among the four variables. |
10 | Using different data from different sources perhaps results in such finding. In this paper, for example, CO2 emissions are taken from Statistical Yearbook published by Agency on Statistics of the Republic of Kazakhstan, whereas income and energy consumption are obtained from World Development Indicator (WDI) database. For this reason, we also re-estimate Equation (2) after collecting CO2 emissions from WDI. However, we obtain almost the same results. |
11 | It should be pointed out that, although we have a relatively small sample size, the regression fits reasonably well (adj. R2 = 0.67) and passes all the necessary diagnostic tests (Panel C in Table 3). Further, we also adopt other alternative cointegration methods such as Fully Modified Least Squares (FMOLS), Dynamic Least Squares (DOLS), and Canonical Cointegration Regression (CCR) for robustness check, although those methods require all the variables to be I(1) processes. We also find almost the same results as those reported in Table 3. Combined with our diagnostic results, therefore, this should somehow mitigate our concern with the relatively short period of dataset and strengthen the credibility of our findings. |
Mean | Standard Deviation | Min | Max | |
---|---|---|---|---|
CO2 emissions | 10.249 | 2.099 | 6.756 | 14.435 |
Income | 452,622.2 | 165,430.3 | 259,194.0 | 738,066.4 |
Energy consumption | 3626.116 | 855.193 | 2324.548 | 4796.144 |
Variable | ADF Test | |||
Level | First Difference | |||
CO2 emissions | −1.855 (0) | −3.574 ** (0) | ||
Income | −3.730 ** (0) | |||
(Income)2 | −3.777 ** (0) | |||
Energy consumption | −1.913 (0) | −4.252 ** (1) | ||
Variable | Perron-Vogelsang Test | |||
Level | Time Break | First Difference | Time Break | |
CO2 emissions | −2.929 (1) | 2003 | −6.569 ** (1) | 2000 |
Income | −6.299 ** (1) | 2000 | ||
(Income)2 | −6.259 ** (1) | 2000 | ||
Energy consumption | −2.195 (1) | 2003 | −5.237 ** (1) | 2001 |
Panel A: Short-Run Results | |||||
∆(income)t | ∆(income)t2 | ∆(energy consumption)t | ect−1 | ||
6.858 (2.208) ** | −0.268 (−2.256) ** | 0.619 (3.315) ** | −0.636 (−3.936) ** | ||
Panel B: Long-Run Results | |||||
incomet | incomet2 | energy consumptiont | Constant | ||
10.779 (1.689) * | −0.422 (−1.725) * | 0.973 (10.448) ** | −74.444 (−1.793) * | ||
Panel C: Diagnostic Statistics | |||||
F-statistic | LM | RESET | Normality | ARCH | Heteroskedasticity |
8.453 ** | 0.561 [0.454] | 0.829 [0.363] | 0.607 [0.738] | 0.236 [0.889] | 1.069 [0.397] |
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Akbota, A.; Baek, J. The Environmental Consequences of Growth: Empirical Evidence from the Republic of Kazakhstan. Economies 2018, 6, 19. https://doi.org/10.3390/economies6010019
Akbota A, Baek J. The Environmental Consequences of Growth: Empirical Evidence from the Republic of Kazakhstan. Economies. 2018; 6(1):19. https://doi.org/10.3390/economies6010019
Chicago/Turabian StyleAkbota, Amantay, and Jungho Baek. 2018. "The Environmental Consequences of Growth: Empirical Evidence from the Republic of Kazakhstan" Economies 6, no. 1: 19. https://doi.org/10.3390/economies6010019