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Assessing long- and short-run dynamic interplay among balance of trade, aggregate economic output, real exchange rate, and CO2 emissions in Pakistan

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Abstract

Since the open economy macroeconomic indicators such as the balance of trade and exchange rate interact with the economic and environmental indicators, it is worthy of delving into their interactive linkages. This study investigates the long-run and short-run dynamic interactive links among the balance of trade, aggregate economic output, real exchange rate, and carbon dioxide (CO2) emissions in Pakistan. Bayer and Hanck’s combined cointegration and the auto-regressive distributed lag method are applied on annual time-series data from 1970 through 2018. The key findings are: (1) Balance of trade and real exchange rate imparted the CO2 emissions mitigation influence in both the long run and the short run. In contrast, the aggregate economic output exhibited the CO2 emissions driving influence in the long run and short run. (2) Balance of trade and real exchange rate induced enhancing and impeding influence on aggregate economic output, respectively, in the short run. However, they exposed the aggregate economic output strengthening influence in the long run. Besides, CO2 emissions produced a neutral influence on the aggregate economic output in the short run, whereas it put forward the aggregate economic output hampering influence in the long run. (3) Aggregate economic output revealed a balance of trade improvement influence for both the long run and short run. Nevertheless, the real exchange rate showed the balance of trade deterioration (improvement) influence in the short run (long run), confirming the J-curve hypothesis in Pakistan. Furthermore, (a) a bidirectional causality existed between CO2 emissions and aggregate economic output, and balance of trade and aggregate economic output. (b) A unidirectional causality existed from real exchange rate to balance of trade and aggregate economic output, and from the balance of trade and real exchange rate to CO2 emissions. The diversification of exports and energy mix is recommended to improve the balance of trade, economic aggregates, and environmental sustainability.

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Fig. 1

Source: World Bank (2019) and IMF (2020)

Fig. 2
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Data availability

All data generated or analyzed during this study are included in this article.

Notes

  1. International Monetary Fund.

Abbreviations

CO2:

Carbon dioxide

BoT:

Balance of trade

EXR:

Exchange rate

REXR:

Real exchange rate

AEO:

Aggregate economic output

IMP:

Imports of goods and services

EXP:

Exports of goods and services

ARCH:

Auto-regressive conditional heteroscedasticity

VECM:

Vector error-correction model

VAR:

Vector auto-regressive

PP:

Phillips–Perron

KPSS:

Kwiatkowski Phillips Schmidt and Shin

DF-GLS:

Dicky–Fuller-generalized least square

CUSUM:

Cumulative sum of recursive residuals

IRF:

Impulse response function

AIC:

Akaike information criterion

SBC:

Schwarz Bayesian criterion

HQ:

Hunnan–Quinn

OLS:

Ordinary least square

BH-CC:

Bayer and Hanck’s combined cointegration

ARDL:

Auto-regressive distributed lag

STIRPAT:

Stochastic impacts by regression on population, affluence and technology

P :

Size of population

A :

Affluence

I :

Environmental influence

T :

Technology

e :

Stochastic term

Prob:

Probability statistic

T-stat:

Test statistic

I(1):

First-order integration

ln:

Natural logarithm

EN&GR:

Engle and Granger

BOS:

Boswijk

JS:

Johansen

BAN:

Banerjee

V :

Column vector of variables

θ :

Vector of drift elasticities

ξ :

Vector of error terms

r :

Optimal order of lags

Σ:

Summation

Δ:

Difference

ω :

Speed of correction parameter

ψ :

Short-run elasticity

λ :

Long-run elasticity

ETt − 1 :

Lagged error-correction term

maxd :

Maximum order of integration

References

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Author information

Authors and Affiliations

  1. School of Economics, Zhejiang University, Hangzhou, 310058, China

    Munir Ahmad

  2. College of Management, Research Institute of Business Analytics and Supply Chain Management, Shenzhen University, Shenzhen, 518060, China

    Gul Jabeen

  3. School of Economics and Management, North China Electric Power University, Beijing, 102206, China

    Gul Jabeen

  4. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Syed Ahsan Ali Shah

  5. College of Economics and Management, Henan Agricultural University, Zhengzhou, China

    Abdul Rehman

  6. School of Economics, Lanzhou University, Lanzhou, 730000, Gansu, China

    Fayyaz Ahmad

  7. Faculty of Tourism, Anadolu University, Eskisehir, Turkey

    Cem Işik

Authors
  1. Munir Ahmad
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  2. Gul Jabeen
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  3. Syed Ahsan Ali Shah
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  4. Abdul Rehman
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  5. Fayyaz Ahmad
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  6. Cem Işik
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Contributions

MA involved in conceptualization and writing–original draft. GJ involved in formal analysis, data handling, methodology, visualization, and software. SAAS involved in writing–review and editing. AR involved in writing–review and editing. FA involved in writing–review and editing. CI involved in writing–review and editing.

Corresponding authors

Correspondence to Munir Ahmad or Gul Jabeen.

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Appendix 1

Appendix 1

1.1 Correlation methods

Primarily, the Pearson test coefficient (\(r\)) has been calculated employing the following formula:

$$r_{yz} = S_{yz} /S_{y} S_{z}$$
(15)

where Syz is the covariance, whereas Sy and Sz are the standard deviations of the given sample. Although the Pearson test is the most widely utilized test to analyze the degree of association in a bivariate framework, nonetheless, the accuracy of its results is dependent on the assumption of linear relationships between each pair of variables. Thus, more tests are considered for the robustness of correlation findings. For this purpose, another measure of rank correlation test has been proposed by Kendall (1938) given as follows:

$$t_{b} = P - Q/\sqrt {\left( {P + Q + Y_{0} } \right)\left( {P + Q + Z_{0} } \right)}$$
(16)

where P and Q are the numbers of concordant and discordant pairs, whereas Y0 and Z0 are the number of pairs tied only to Y and Z, respectively. According to the definition of concordant pairs, if the two data points (Yi, Zi) and (Yj, Zj) are in the same order with respect to each variable, they are termed as concordant pairs. It means if: (1) Yi < Yj and Zi < Zj, or (2) Yi > Yj and Zi > Zj. Next, according to the definition of discordant pairs, if the two data points exist in the reverse order for Y and Z, they are termed as discordant pairs. It means if: (1) Yi < Yj and Zi > Zj, or (2) Yi > Yj and Zi < Zj. After that, the tied pairs are defined as the two data points are said to be tied if one of the following holds true: Yi = Yj or Zi = Zj. These pairs are used to match the two data points so as to find the association between them. Kendall rank correlation is a nonparametric test and, according to Knight (1966), is sometimes preferred over both Pearson and Spearman rank correlation tests. This preference is based on having features of smaller asymptotic variance and gross error sensitivity, hence providing more reliable statistical outcomes.

Finally, a slightly modified version of Spearman’s rank correlation test by Clef (2014) has been employed, which has the advantage over standard Spearman’s test by considering the tied ranks. The calculation formula is given as:

$$\rho = 1/n\mathop \sum \limits_{i = 1}^{n} \left( {R\left( {y_{i} } \right) - \overline{R\left( y \right)} } \right).\left( {R\left( {y_{i} } \right) - \overline{R\left( y \right)} } \right)/\sqrt {\left( {1/n\mathop \sum \limits_{i = 1}^{n} \left( {R\left( {y_{i} } \right) - \overline{R\left( y \right)} } \right)^{2} } \right).\left( {1/n\mathop \sum \limits_{i = 1}^{n} \left( {R\left( {z_{i} } \right) - \overline{R\left( z \right)} } \right)^{2} } \right)}$$
(17)

where R(y) and R(z) indicate the ranks. Furthermore, the ranks with bars on them indicate the averages of those ranks (Fig. 7; Table 10).

Fig. 7
figure 7

Cumulative density of normal distribution and normality plots of estimated residuals. Note: The left block indicates normality plots, whereas the right block indicates a cumulative density of normal distribution

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Table 10 Results of post-analysis testing
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Ahmad, M., Jabeen, G., Shah, S.A.A. et al. Assessing long- and short-run dynamic interplay among balance of trade, aggregate economic output, real exchange rate, and CO2 emissions in Pakistan. Environ Dev Sustain 24, 7283–7323 (2022). https://doi.org/10.1007/s10668-021-01747-9

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