# Revisiting Environmental Kuznets Curve in Relation to Economic Development and Energy Carbon Emission Efficiency: Evidence from Suzhou, China

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## Abstract

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## 1. Introduction

_{2}) emissions are the most important contributor [5,6] to global warming. Human activities have greatly exacerbated the process of climate warming and caused many adverse effects on the natural ecological environment on the earth’s surface.

_{2}E) [5], adding 600 million tons of CO

_{2}E, equivalent to an increase in the emissions of 400 million vehicles [6]. It is a crystal that, carbon dioxide is the foremost root of global warming. Global warming has brought great repercussions to the natural environment, food, water supply, health, and even social security. Therefore, reducing the quantity of emission of carbon dioxide is a crucial task for all countries in dealing with climate change.

## 2. Literature Review

_{2}E. By using yearly time series data for the period 1970–2012, Salahuddin, Alam, Ozturk and Sohag [13] examined the influences associated with the income of people, energy consumption, and population growth on CO

_{2}emissions for Indonesia, India, Brazil, and China. Using the ARDL bounds assessment method their outcomes expressed that CO

_{2}emissions significantly escalated with a rise in revenue and energy consumption in all four countries. Likewise, a practical survey from the testing of the environmental Kuznets curve (EKC) hypothesis infers that in the cases of Brazil, China, and Indonesia, CO

_{2}emissions will decrease over time when income increases. Al Mamun, et al. [33] used the newly developed dynamic ARDL simulation process to study the impact of energy consumption and economic growth on environmental degradation in Pakistan and proposed the use of renewable alternative energy to reduce environmental degradation. Shan, et al. [34] also analyzed the nexus of CO

_{2}emissions, economic growth, and energy consumption by decoupling greenhouse gas emissions of CO

_{2}and economic growth over the period 1970–2015. Their study applied different estimation techniques such as the ARDL model, FMOLS, DOLS, and impulse response and variance decomposition. The result supports the Environmental Kuznets Curve (EKC) hypothesis which specified that China’s EKC turning point confirms some inconsistencies when compared to other turning points attained from different studies.

## 3. Methodology

#### 3.1. Establishment of the Empirical Model

_{2}E on GDP per capita. That is why we use linear and non-linear approaches to examine the empirical relationship between these variables as suggested by unit root analysis. Our empirical model is based on the following production function:

#### 3.2. Variable Selection and Data Sources

_{2}E). The independent variable of the study includes GDP per capita, energy use, industrial share as a percent of GDP. Trade openness and labor are used as control variables of the study. We also included the GDP per capita square variable to capture the monotonic impact of growth in per capita towards carbon emissions. The data has collected from Chinese annual yearbooks from 1998 to 2019. The collected data has been converted into quarters to avoid a degree of freedom issue (1998q1–2019q4). The data has been converted into quarters by using Eviews 12 software.

#### 3.3. Standard Autograssive Distributed Lag Bounds Testing Approach to Cointegration

^{2}, LL, LIPGDP, LEU, LTO). We get two asymptotic critical values of bounds test of cointegration when the independent variables are I(d) where 0_d_1. A lower value assumes the regressors are I (0), and an upper value assumes regressors have purely I (1). If the F-statistics is above the upper critical value, the null hypothesis of no long-run relationship can be rejected irrespective of the orders of integration for the time series. Conversely, if the test statistic falls below the lower critical value the null hypothesis cannot be rejected. Lastly, if the statistic falls between the lower and upper critical values, the result is inconclusive. The approximate critical values for the F-test were obtained from Pesaran, Shin and Smith [41]. After establishing the long-run relationship, we estimated the conditional ARDL (p

_{1}, q

_{1}, q

_{2}, q

_{3}, q

_{4}) as follows:

_{1}, q

_{1}, q

_{2}, q

_{3}, q

_{4}) model in the seven variables using Akaike information criteria (AIC). Lastly, we obtain the short-run dynamic parameters by estimating an error correction model associated with the long-run estimates. The equation for the short-run dynamic or error correction model of ARDL is as follows:

#### 3.4. Asymmetric ARDL Model

_{BDM}-statistic and F

_{PSS}. Najarzadeh, et al. [46] proposed t

_{BDM}-statistic where null hypothesis as H

_{0}: p = 0 against alternative hypothesis H

_{1}: p < 0. Whereas Pesaran, Shin and Smith [46] proposed F

_{PSS}statistic tests joint null hypothesis as H

_{0}: p = θ

^{+}= θ

^{+}= 0. Pesaran, Shin and Smith [41] bounds testing framework offer a mean to get a valid inference of the presence of both stationary and non-stationary variables. Shin, Yu and Greenwood-Nimmo [45] view this characteristic is highly desirable in the presence of partial sum decompositions and may show complex interdependences. Empirically, Shin, Yu and Greenwood-Nimmo [45] proposed the counting of the regressors in x

_{t}before decomposition and choosing the suitable critical values those from tabulated in Pesaran and Shin [47] to endorse conservatism.

^{+}= π

^{−}. In addition, restrictions of short-run symmetry are considered in two different ways: π

^{+}= π

^{−}for all I = 0, …, q − 1 or $\sum}\_\left(i=0\right)^q\pi \_i^+$ = $\sum}\_\left(i=0\right)^q\pi \_i^-$. However, both forms can be evaluated by applying the standard Wald test. Furthermore, we also examined the symmetry of the impact of multipliers (i.e., H

_{0}= π

^{+}= π

^{−}).

_{t}is related with unit changes in ${x}_{t}^{+}$ and ${x}_{t}^{-}$ and is measured recursively from the parameters of the asymmetric ARDL in level (Equation (6)).

## 4. Empirical Analysis

_{2}). The quadric fit is made by regressing the dependent variable (CO

_{2}emissions) on independent variables (GDP Per capita and GDP per capita square). Figure 1 depicts clearly that the energy carbon emissions increase with an increase in GDP per capita. After GDP per capita reaches a certain level, it starts to diminish with further economic growth.

#### 4.1. Unit Root Test

^{2}, LL, LIPGDP, LEU, LTO) = 5.85; for LGDPPC (LGDPPC|Co2E, LGDPPC

^{2}, LL, LIPGDP, LEU, LTO) = 18.04; for LGDPPC

^{2}(LGDPPC

^{2}|Co2E, LGDPPC, LL, LIPGDP, LEU, LTO) = 6.84; for LIPGDP (LIPGDP|Co2E, LGDPPC, LGDPPC

^{2}, LL, LEU, LTO) = 5.78; for LL (LL|Co2E, LGDPPC, LGDPPC

^{2}, LIPGDP, LEU, LTO) = 4.61 and for LEU (LEU|Co2E, LGDPPC, LGDPPC

^{2}, LIPGDP, LL, LTO) = 19.96. The finding shows that calculated f-statistics are higher than the critical values at 10%, 5%, and 1% level of significance of upper bounds of the bounds test. Therefore, we conclude that a cointegration relationship exists among variables included in the model when the regressions are normalized on Co2E, LGDPPC, LGDPPC

^{2}, LIPGDP, LL, and LEU. These findings are robust from the endogeneity issue as followed by [41] who used to take lag orders to solve this endogeneity problem.

#### 4.2. Empirical Findings from Standard ARDL Model

#### 4.3. Empirical Findings from Asymmetric ARDL Model

## 5. Conclusions and Countermeasures

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Variables | Levels | 1st Diff | Conclusion | Order of Integration |
---|---|---|---|---|

LnCo2E | −2.962 ** | −3.63 ** | Stationary | 1(0) |

LnGDPPC | −2.275 | −4.931 *** | Non-Stationary | 1(1) |

LnGDPPC^{2} | −2.28 | −3.42 * | Non-Stationary | 1(1) |

LIPGDP | 2.95 ** | −3.23 ** | Stationary | 1(0) |

LEU | −2.29 | −3.36 ** | Non-Stationary | 1(1) |

LL | −1.12 | −5.80 *** | Non-stationary | 1(1) |

LTO | −2.87 * | −4.46 *** | Stationary | 1(0) |

Dependent Variable | F-Statistics | Outcome |
---|---|---|

Co2E (Co2E|LGDPPC, LGDPPC^{2}, LL, LIPGDP, LEU, LTO) | 5.85 | Cointegration |

LGDPPC (LGDPPC|Co2E, LGDPPC^{2}, LL, LIPGDP, LEU, LTO) | 18.04 | Cointegration |

LGDPPC^{2} (LGDPPC^{2}|Co2E, LGDPPC, LL, LIPGDP, LEU, LTO) | 6.48 | Cointegration |

LIPGDP (LIPGDP|Co2E, LGDPPC, LGDPPC^{2}, LL, LEU, LTO) | 5.78 | Cointegration |

LL (LL|Co2E, LGDPPC, LGDPPC^{2}, LIPGDP, LEU, LTO) | 4.61 | Cointegration |

LEU (LEU|Co2E, LGDPPC, LGDPPC^{2}, LIPGDP, LL, LTO) | 19.96 | Cointegration |

LTO (LEU|Co2E, LGDPPC, LGDPPC^{2}, LIPGDP, LL, LEU) | 2.47 | No-Cointegration |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

LNCO2E_{(t−1)} | −0.4824 *** | 0.1303 | −3.7010 | 0.0024 |

LPCGDP_{(t−1)} | 0.9042 ** | 0.3662 | 2.4687 | 0.0270 |

LGDPPCSQ_{(t−1)} | −0.1708 * | 0.0626 | −2.7278 | 0.0721 |

LL_{(t−1)} | −0.4829 | 1.8539 | −0.2604 | 0.7983 |

LTO_{(t−1)} | 0.4348 * | 0.1835 | 2.3695 | 0.0985 |

LEU_{(t−1)} | 0.3352 | 1.3515 | 0.2480 | 0.8077 |

IPGDP_{(t−1)} | 0.0397 *** | 0.0077 | 5.1234 | 0.0002 |

Short-run coefficients | ||||

∆LGDPPC | 13.122 *** | 1.5839 | 8.2849 | 0.0001 |

∆LGDPPCSQ | −0.213 *** | 0.0128 | −16.6540 | 0.0000 |

∆LL | −0.213 *** | 0.0826 | −2.5863 | 0.0361 |

∆LTO | 0.699 *** | 0.0841 | 8.3060 | 0.0001 |

∆IPGDP | −0.044 *** | 0.0046 | −9.4121 | 0.0000 |

∆LEU | 1.942 *** | 0.2964 | 6.5545 | 0.0003 |

CointEq(−1) * | −0.482 *** | 0.0372 | −12.9622 | 0.0000 |

EC = LNCO2E − (−0.9042 * LPCGDP − 0.1708 * LGDPPCSQ − 0.4829 * LPOP + 0.4348 * LTO + 0.3352 * LEU + 0.0397 * IPGDP) | ||||

R-squared | 0.83 | Adjusted R-squared | 0.75 | |

Akaike info criterion | 28.41 | Schwarz criterion | 24.32 | |

Durbin–Watson stat | 1.99 | F-Statistics: (6, 14) | 11.22 (0.00) | |

Residual Sum of Squares | 0.383 | S.E. of Regression | 0.054295 | |

Diagnostic tests | ||||

Serial Correlation χ^{2}(1) | 0.74 (0.979) | Functional Form χ^{2}(1) | 0.55 (0.469) | |

Normality χ^{2}(2) | 0.96 (0.617) | Heteroscedasticity χ^{2}(1) | 1.63 (0.217) |

Variables | Long-Run Effect [+] | Long-Run Effect [−] | ||||

Coefficient | f-Statistics | p-Value | Coefficient | f-Statistics | p-Value | |

LPCGDP | 8.488 | 24.6 | 0.001 | −0.296 | 0.0086 | 0.930 |

LGDPPCSQ | −0.354 | 20.44 | 0.001 | 0.174 | 0.1385 | 0.717 |

LL | −2.263 | 0.0167 | 0.900 | −0.903 | 0.6924 | 0.452 |

LTO | 0.008 | 0.0001 | 0.992 | −0.461 | 0.1763 | 0.696 |

LEU | 2.451 | 1.723 | 0.260 | 0.003 | 0.0401 | 0.845 |

IPGDP | 0.250 | 4.451 | 0.068 | 0.040 | 0.2341 | 0.641 |

Long-Run Asymmetry | Short-Run Asymmetry | |||||

f-Statistics | p-Value | f-Statistics | p-Value | |||

LPCGDP | 5.239 | 0.084 | 6.005 | 0.070 | ||

LGDPPCSQ | 0.0596 | 0.812 | 1.04 | 0.332 | ||

LL | 2.634 | 0.352 | 5.711 | 0.252 | ||

LTO | 0.332 | 0.595 | 8.648 | 0.042 | ||

LEU | 4.981 | 0.890 | 9.154 | 0.039 | ||

IPGDP | 0.5556 | 0.497 | 5.026 | 0.088 | ||

Cointegration Test Statistics | Model Diagnostics | |||||

F_{PSS} = 8.0734 (upper bound critical value = 4.05 at 5% level of significance) | Functional Form χ^{2}(1) | 1.943 | 0.2013 | |||

Heteroscedasticity χ^{2}(1) | 0.3023 | 0.5825 | ||||

T_{BDM} = −4.0328 (upper bound critical value = 3.99 at 5% level of significance) | Normality χ^{2}(2) | 0.4845 | 0.7849 | |||

R^{2} | Adjusted R^{2} | Root MSE | ||||

0.8474 | 0.7101 | 0.05981 |

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**MDPI and ACS Style**

Wen, M.; Li, M.; Erum, N.; Hussain, A.; Xie, H.; ud din Khan, H.S.
Revisiting Environmental Kuznets Curve in Relation to Economic Development and Energy Carbon Emission Efficiency: Evidence from Suzhou, China. *Energies* **2022**, *15*, 62.
https://doi.org/10.3390/en15010062

**AMA Style**

Wen M, Li M, Erum N, Hussain A, Xie H, ud din Khan HS.
Revisiting Environmental Kuznets Curve in Relation to Economic Development and Energy Carbon Emission Efficiency: Evidence from Suzhou, China. *Energies*. 2022; 15(1):62.
https://doi.org/10.3390/en15010062

**Chicago/Turabian Style**

Wen, Ming, Mingxing Li, Naila Erum, Abid Hussain, Haoyang Xie, and Hira Salah ud din Khan.
2022. "Revisiting Environmental Kuznets Curve in Relation to Economic Development and Energy Carbon Emission Efficiency: Evidence from Suzhou, China" *Energies* 15, no. 1: 62.
https://doi.org/10.3390/en15010062