# Economic Activities and Management Issues for the Environment: An Environmental Kuznets Curve (EKC) and STIRPAT Analysis in Turkey

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## Abstract

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_{2}emissions have become major environmental concerns that are closely related to climate change and sustainable economic growth. The purpose of this paper is to investigate the long-run relationship among CO

_{2}emissions, energy consumption, economic activities, and management issues for Turkey for the period between 1980 and 2021. The STIRPAT hypothesis and the environmental Kuznets curve (EKC) hypothesis were employed by using dynamic conditional correlation (DCC) and ARDL bound methodologies for these goals. The findings indicate that there is a long-run relationship between variables of the STIRPAT model. The coefficient of economic expansion and energy consumption affected CO

_{2}emissions positively, which means that energy consumption and the expansion of economic activity have significant effects on environmental degradation. Those results are also confirmed by the environmental Kuznets curve (EKC) model. In addition, the N-shaped environmental Kuznets curve (EKC) is developed for Turkey. The DCC model also shows that economic growth increases CO

_{2}emissions significantly, and energy productivity can be considered for decreasing CO

_{2}emissions.

_{2}emissions; energy; economics; management; environmental Kuznets curve; STIRPAT; ImPACT

## 1. Introduction

_{2}emissions, such as the Laspeyres method [9] and the LMDI method [10].

## 2. Literature Review

_{2}in its model is affected by the independent variable GDP and other control variables, which explains an inverted U-shaped relationship between environmental quality and economic development. The researchers investigate this relationship, whether it is U-shaped, N-shaped, or V-shaped.

_{2}emissions per capita and finds an inverted-U shape for Turkey, which is consistent with [33].

_{2}emissions and affluence, population, technology, urbanization, financial development, and globalization. Except for financial development, all the variables have an increasing effect on CO

_{2}emissions in the long run, and short-run dynamics are valid in the model. Another paper that investigates the validity of the STIRPAT model for Turkey is [36], which uses panel data methodology for ten newly industrialized countries (NICs), one of which is Turkey. The empirical analysis consists of a dynamic common correlated effects estimator (DCCE), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS). DCCE shows that all the independent variables (population, affluence, technology, energy intensity, urban employment, and energy mix) have a significant impact on CO

_{2}emissions. The general results show that for the NIC’s population, GDP per capita is the main reason for CO

_{2}emissions. Ref. [37] conducted the quantile regression methodology implemented within the STIRPAT model structure for 154 countries’ data, one of the countries being Turkey. They used ecological footprint per capita as a dependent variable and found that GDP per capita and the financial development index have a positive impact on population, and services negatively impact ecological footprint. Ref. [36] presents a literature review on the extended STIRPAT model, with CO

_{2}as the dependent variable, and summarizes the direction of the variables, which are P (population), A (affluence), and T (technology). Refs. [38,39,40,41] find positive P (population) and A (affluence), and positive T (technology); Refs. [42,43,44,45] find positive P (population) and A (affluence), and negative T (technology), and [46] finds negative P (population) and A (affluence), and positive T (technology) in their STIRPAT model.

## 3. Theoretical Model

_{2}emissions, URB is urbanisation, E is energy components, Y is per capita GDP, and finally, e is the residual error term. Following [16,53,54], we add international trade (TR) as a proxy for the degree of openness, foreign direct investment (FDI), and for energy components, total energy supply (ES), total final consumption (FEC), and environmental and resource productivity (energy productivity)(EP). Therefore, (4) will take (5), as follows, by showing the logarithm by L:

## 4. Empirical Analysis

#### 4.1. Data

#### 4.2. Unit Root Test

_{2}emission, foreign direct investment, and urbanization can be considered as at I(0)), and none of the variables are stationary at I(2). Therefore, the ARDL bound test developed by [62] is considered for testing the long-run relationship of the series.

## 5. Results and Discussion

_{2}emissions as a dependent variable, the coefficient of GDP per capita, EP, FEC, and URB are statistically significant, but FDI and TR are not significant. The EC

_{t-1}term is in the acceptable range, which is −2 to 0, and F-bound is 39.60, which is the upper bound of 1% of 3.99, indicating that the variables are cointegrated and there is a long-run relationship among the variables. The coefficient of Y is positive, which indicates that economic activities are caused by ${\mathrm{C}\mathrm{O}}_{2}$ emissions and environmental degradation in Turkey. The total final energy consumption coefficient is 1.17 and positive, the largest coefficient among the factors which have caused environmental degradation. Urbanisation has a negative impact.

_{2}), economic growth, and environmental and resource productivity (energy productivity) in Turkey using dynamic conditional correlation multivariate GARCH (DCC-EGARCH(1,1)) [52] for the period between 1990 and 2021, which reflects investing levels of renewable energy and the impact of economic activities on emissions of carbon dioxide in Turkey. According to the theoretical framework, the testable model is taken as follows:

_{2}emissions, economic growth, and environmental and resource productivity in Turkey.

_{2}emissions have shown a negative response. These results are consistent with the findings of [16,33,53,55,70].

_{2}emissions in the whole selected period, except for the short term in late 1997, and only one dot is negative in 2010. In the case of CO

_{2}emissions and environmental and resource productivity, there is a full and strong negative DCC between CO

_{2}and environmental resource productivity. There is a positive DCC between environmental and resource productivity and growth in 1997–1999, 2002–2004, and 2009–2011. In contrast, the relationship is mainly negative, showing that increased growth was affected negatively when inverting environmental and resource productivity. Nevertheless, at the end of 1997 and from 2009 to 2011, when carbon dioxide had a negative relationship with economic growth, economic growth and environmental and resource productivity had a positive relationship. We find that final energy consumption is the most important factor that has caused environmental degradation in Turkey. This result is consistent with related theories that the European Environmental Agency emphasizes. Additionally, we find that economic activities have an important role in environmental deregulation in Turkey, which is consistent with the EKC hypothesis.

## 6. Conclusions

_{2}emissions was tested for Turkey by employing the STIRPAT and EKC models.

_{2}emissions, economic activities and management, energy consumption components, urbanization, and sustainable development. We estimated three hypotheses and methodology using the ARDL bound test and the DCC model over the period between 1980 and 2021.

_{2}emissions positively, which means that energy consumption and the expansion of economic activity have a significant effect on environmental degradation, which is consistent with [42]. According to the EKC estimation, there is a long-run relationship between variables and energy consumption. Economic activities and management have the main effect on CO

_{2}emissions, which leads to environmental degradation in Turkey. Additionally, in the EKC analysis, we find the N-shaped curve.

_{2}emissions. Therefore, we applied the DCC model. The results of the DCC model indicate that there is a distinct dynamic conditional correlation to alter in response to a time change. Additionally, we find a positive DCC between economic growth and CO

_{2}emissions in the whole selected period, except for the short term in late 1997; it became negative in 1997 and then reached positive values again in 2010. In the case of CO

_{2}emissions and environmental and resource productivity, there is a full and strong negative DCC between CO

_{2}and environmental resource productivity. Hence, if the government invests in energy productivity, it can prevent environmental degradation by reducing economic activities that cause carbon dioxide emissions and manage the economy based on environmental concerns.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A. Stability Diagnostics of the STIRPAT Model

**Figure A1.**Graphs shows the stability results of stability test from the cumulative sum (CUSUM test) and cumulative summed squared (CUSUMSQ). The test curves perfectly between and along the lower and upper bounds at 5% significance level and stable around the mean. Source: own study.

## Appendix B. Stability Diagnostics of the EKC Model

**Figure A2.**Graphs shows the stability results of stability test from the cumulative sum (CUSUM test) and cumulative summed squared (CUSUMSQ). The test curves perfectly between and along the lower and upper bounds at 5% significance level and stable around the mean. Source: own study.

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**Figure 1.**Dynamic condition correlation between CO

_{2}emissions, economic growth, and environmental and resource productivity. Source: own study.

Variable | Definition | Unit | Data Source |
---|---|---|---|

${\mathrm{C}\mathrm{O}}_{2}$ | Annual total production-based emissions of carbon dioxide (${\mathrm{C}\mathrm{O}}_{2}$) | Million tonnes | (GitHub) ^{1} |

$\mathrm{E}\mathrm{P}$ | Environmental and resource productivity (energy productivity) | % | OECD |

$\mathrm{g}$ | GDP (growth) | % | World Bank |

Y | GDP per capita | Constant (TRL) | World Bank |

TR | The sum of imports and exports | % of GDP | World Bank |

FDI | Foreign direct investment, net inflows | % of GDP | World Bank |

ES | Total energy supply | Petajoule (PJ.) | World Energy Statistics |

FEC | Total final consumption | Petajoule (PJ.) | World Energy Statistics |

URB | Urban population (% of the total population) | (% of the total population) | World Bank |

^{1}: Our World in Data based on the Global Carbon Project (2022), https://github.com/owid/co2-data (accessed on 12 November 2022). Source: own study.

LCO_{2} | LEP | LES | LFDI | LFEC | LTR | LURB | LY | |
---|---|---|---|---|---|---|---|---|

Mean | 5.57 | 3.40 | 8.20 | −0.06 | 7.93 | 3.25 | 4.21 | 9.52 |

Median | 5.57 | 3.40 | 8.16 | 0.15 | 7.91 | 3.29 | 4.21 | 9.51 |

Maximum | 6.06 | 3.83 | 8.72 | 1.28 | 8.43 | 3.66 | 4.33 | 9.97 |

Minimum | 5.02 | 2.85 | 7.65 | −1.18 | 7.43 | 2.81 | 4.08 | 9.14 |

Std. Dev. | 0.33 | 0.27 | 0.34 | 0.73 | 0.30 | 0.22 | 0.07 | 0.27 |

Skewness | −0.09 | −0.24 | 0.01 | 0.07 | 0.001 | −0.32 | −0.09 | 0.26 |

Kurtosis | 1.69 | 2.00 | 1.79 | 1.69 | 1.77 | 2.42 | 1.75 | 1.69 |

ADF | PP | |||
---|---|---|---|---|

Variables | Intercept | Intercept and Trend | Intercept | Intercept and Trend |

LCO_{2} | −2.62 (0) * | −2.13 (0) | −5.68 (20) *** | −1.87 (8) |

$\u2206\mathrm{L}$CO_{2} | −6.20 (0) *** | −5.47 (1) *** | −6.20 (0) *** | −8.48 (15) *** |

CO_{2} | 0.56 (0) | −2.61 (0) | 3.19 (40) | −2.46 (7) |

$\u2206$CO_{2} | −5.64 (1) *** | −5.68 (1) *** | −7.12 (39) *** | −11.97 (39) *** |

LFEC | −1.04 (1) | −3.89 (0) ** | −1.50 (35) | −3.85 (2) ** |

$\u2206\mathrm{L}\mathrm{F}\mathrm{E}\mathrm{C}$ | −8.39 (0) *** | −8.38 (0) *** | −2.01 (16) *** | −18.12 (23) *** |

LFDI | −1.97 (0) | −2.71 (0) | −1.84 (5) | −2.66 (3) |

$\u2206\mathrm{F}\mathrm{D}\mathrm{I}$ | −6.14 (0) *** | −6.02 (0) *** | −10.5 (27) *** | −10.66 (27) *** |

ULRB | −4.30 (9) *** | −2.69 (9) | −6.78 (5) *** | −6.85 (4) *** |

L${\mathrm{Y}}_{\mathrm{P}\mathrm{e}\mathrm{r}\_\mathrm{c}\mathrm{a}\mathrm{p}}$ | 0.07 (9) | −4.58 (7) *** | −0.56 (7) | −2.63 (0) |

$\u2206\mathrm{L}{\mathrm{Y}}_{\mathrm{P}\mathrm{e}\mathrm{r}\_\mathrm{c}\mathrm{a}\mathrm{p}}$ | −3.50 (8) *** | −3.44 (8) ** | −6.98 (4) *** | −6.87 (4) *** |

LEP | −1.82 (0) | −1.46 (0) | −1.78 (1) | −1.35 (2) |

$\u2206\mathrm{L}\mathrm{E}\mathrm{P}$ | −6.25 (0) *** | −4.37 (7) *** | −6.29 (3) *** | −7.57 (7) *** |

LES | −1.04 (0) | −3.08 (0) | −1.90 (9) | −2.88 (4) |

$\u2206$ES | −6.77 (0) *** | −6.85 (0) *** | −7.30 (6) *** | −8.56 (8) *** |

LFDI | −2.97 (0) ** | −4.02 (0) *** | −3.03 (5) ** | −4.04 (1) *** |

LY | 0.41 (0) | −2.26 (0) | 1.20 (6) | −2.26 (0) |

$\u2206\mathrm{L}\mathrm{Y}$ | −6.61 (0) *** | −4.09 (5) *** | −6.96 (5) *** | −7.61 (6) *** |

$\mathrm{L}{\mathrm{Y}}^{2}$ | 0.59 (0) | −2.02 (0) | 1.67 (7) | −2.02 (0) |

$\u2206{\mathrm{L}\mathrm{Y}}^{2}$ | −6.48 (0) *** | −4.10 (5) *** | −6.72 (5) *** | −7.77 (7) *** |

${\mathrm{L}\mathrm{Y}}^{3}$ | 0.78 (0) | −1.79 (0) | 2.05 (7) | −1.78 (1) |

$\u2206\mathrm{L}{\mathrm{Y}}^{3}$ | −6.34 (0) *** | −4.09 (5) *** | −6.47 (5) *** | −7.61 (7) *** |

MODEL 1 ARDL (1,0,0,0,2,0,1) | |
---|---|

Coefficient | Long-Run Coefficient Dependent Variable: Log CO_{2} |

${\mathsf{\alpha}}_{0}$ | 0.26 (1.73) |

LY | 0.23 (1.85) ** |

LFDI | −0.009 (−0.89) |

LFEC | 1.17 (5.57) *** |

LEP | −0.14 (−4.72) *** |

LTR | −0.017 (−0.39) |

LURB | −1.34 (−1.82) * |

${\mathrm{E}\mathrm{C}}_{\mathrm{t}-1}$^{a} | −0.73 (−9.11) *** |

F-bounds | 39.60 upper bound of 1%: 3.99 |

${\mathsf{\chi}}_{\mathrm{R}\mathrm{E}\mathrm{S}\mathrm{I}\mathrm{D},\mathrm{L}\mathrm{M},\mathrm{S}\mathrm{E}\mathrm{R}}^{2}$ | 1.44 prob: 0.25 |

${\mathsf{\chi}}_{\mathrm{R}\mathrm{E}\mathrm{S}\mathrm{I}\mathrm{D},\mathrm{A}\mathrm{R}\mathrm{C}\mathrm{H}}^{2}$ | 0.40 prob: 0.52 |

CUSUM | Stable in full period |

CUSUMQ | Stable in full period |

^{a}: ${\mathrm{E}\mathrm{C}}_{\mathrm{t}-1}=\mathrm{L}\mathrm{C}{\mathrm{O}}_{2}-\left(-0.01.\mathrm{L}\mathrm{T}\mathrm{R}+0.23.\mathrm{L}\mathrm{Y}-0.009.\mathrm{L}\mathrm{F}\mathrm{D}\mathrm{I}-1.34.\mathrm{L}\mathrm{U}\mathrm{R}\mathrm{B}+1.17.\mathrm{L}\mathrm{F}\mathrm{E}\mathrm{C}-0.14.\mathrm{L}\mathrm{E}\mathrm{P}+0.2635\right).$ *, **, and *** represent 10%, 5%, and 1% significance levels, respectively. Source: own study. Source: own study.

MODEL 1 ARDL (4,2,2,2,1,1,2,2) | |
---|---|

Coefficient | Long-Run Coefficient Dependent Variable: Log CO_{2} |

${\mathsf{\alpha}}_{0}$ | −33.95 (−2.53) ** |

LY | 117.75 (2.36) ** |

$\mathrm{L}{{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}}^{2}$ | −11.49 (−2.18) ** |

$\mathrm{L}{{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}}^{3}$ | 0.35 (2.02) ** |

LTR | 0.01 (0.30) |

LFEC | 0.76 (2.13) ** |

g | −0.04 (−1.99) * |

LES | −0.30 (−1.1) |

${\mathrm{E}\mathrm{C}}_{-1}$^{a} | −1.27 (−4.67) *** |

F-bounds | 3.33 upper bound of 5%: 3.21 |

${\mathsf{\chi}}_{\mathrm{R}\mathrm{E}\mathrm{S}\mathrm{I}\mathrm{D},\mathrm{L}\mathrm{M}\_\mathrm{S}\mathrm{E}\mathrm{R}}^{2}$ | F = 3.09 (prob = 0.09) |

${\mathsf{\chi}}_{\mathrm{R}\mathrm{E}\mathrm{S}\mathrm{E}\mathrm{T},\mathrm{A}\mathrm{R}\mathrm{C}\mathrm{H}}^{2}$ | F = 0.02 (prob: 0.86) |

CUSUM | Stable |

CUSUMSQ | Stable |

^{a}: ${\mathrm{E}\mathrm{C}}_{\mathrm{t}-1}=\mathrm{L}{\mathrm{C}\mathrm{O}}_{2}-\left(117.75\mathrm{L}\mathrm{Y}-11.49\mathrm{L}{{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}}^{2},+0.37\mathrm{L}{{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}}^{3}+0.76\mathrm{L}\mathrm{F}\mathrm{E}\mathrm{C}+0.01\mathrm{L}\mathrm{T}\mathrm{R}-0.04\mathrm{g}-0.30\mathrm{L}\mathrm{E}\mathrm{S}-399.95\right).$ *, **, and *** represent 10%, 5%, and 1% significance levels, respectively. Source: own study. Source: own study.

Parameters | Coefficient (t-Value) | |||
---|---|---|---|---|

DCC (1,1) | alfa | 0.259 *** (2.81) | ||

beta | 0.67 *** (5.48) | |||

${\rho}_{21}$ | 0.51 ** (2.40) | |||

${\rho}_{31}$ | −0.55 * (−1.77) | |||

${\rho}_{32}$ | 0.09 (0.34) | |||

Autocorrelation and heteroskedasticity test | ||||

Parameters | Q | p-values | ${Q}^{2}$ | p-values |

Hosing (5) | 53.40 | 0.13 | 57.23 | 0.79 |

Hosking (10) | 107.1 | 0.08 | 82.03 | 0.51 |

Li-McLeod (5) | 54.07 | 0.126 | 59.61 | 0.097 |

Li-McLeod (10) | 108.32 | 0.081 | 88.21 | 0.61 |

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## Share and Cite

**MDPI and ACS Style**

Ojaghlou, M.; Ugurlu, E.; Kadłubek, M.; Thalassinos, E.
Economic Activities and Management Issues for the Environment: An Environmental Kuznets Curve (EKC) and STIRPAT Analysis in Turkey. *Resources* **2023**, *12*, 57.
https://doi.org/10.3390/resources12050057

**AMA Style**

Ojaghlou M, Ugurlu E, Kadłubek M, Thalassinos E.
Economic Activities and Management Issues for the Environment: An Environmental Kuznets Curve (EKC) and STIRPAT Analysis in Turkey. *Resources*. 2023; 12(5):57.
https://doi.org/10.3390/resources12050057

**Chicago/Turabian Style**

Ojaghlou, Mortaza, Erginbay Ugurlu, Marta Kadłubek, and Eleftherios Thalassinos.
2023. "Economic Activities and Management Issues for the Environment: An Environmental Kuznets Curve (EKC) and STIRPAT Analysis in Turkey" *Resources* 12, no. 5: 57.
https://doi.org/10.3390/resources12050057