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Using cointegration and error correction modeling techniques, this paper examines the dynamics of carbon dioxide (CO
_{2}
) emissions in relation to economic growth, energy consumption, trade openness, urbanization and foreign direct investments in the case of the Philippines. The study finds that 1) economic growth and CO
_{2}
emissions have a significant positive linear relationship, suggesting that income growth policies must be subject to reasonably stringent environmental constraints related to CO
_{2}
emissions; 2) CO
_{2}
emissions are inelastic with respect to energy use in the short run, but its response becomes elastic in the long-run; and 3) CO
_{2}
emissions have a positive elasticity with respect to FDI which conforms with the Pollution Haven hypothesis.

The first objective of this study is to test the Environmental Kuznets Curve (EKC) hypothesis in the Philippine setting. The EKC proposes an inverted U-relationship between economic growth and environmental degradation. The EKC suggests that environmental degradation such as pollution and deforestation increases in the early stages of development, reaches a maximum at the middle income level, and then decreases with further income expansion [

Interests in the EKC hypothesis have spread since its use in the World Bank Development Report in the 1990s [

This paper focuses on one country, the Philippines, and one air pollutant, carbon dioxide. The Philippines is currently one of the fast growing economies in Asia, and the environmental consequences, if any, of this long- desired growth must be investigated and accordingly addressed. Carbon dioxide is said to account for about 64% of the greenhouse gasses that cause global warming. The need to be watchful of increased carbon dioxide emissions that may accompany income growth is dictated by both internal and external factors. Due to its geographical location, topography and current socio-economic structure, the Philippines is considered highly vulnerable to climate change impact [

While focusing on the Philippines, this paper shall include additional economic variables to the basic EKC relationship between carbon dioxide emissions and income. This model expansion aims to partially address the omitted variables bias and lag selection bias in past literature [

This paper adopts the empirical model of Hossain’s study on the long-run and short-run dynamics of carbon dioxide emissions in relation to economic growth, energy consumption, trade openness and urbanization in Japan. Hossain incoporates additional variables to the core EKC relationship between carbon dioxide emissions and economic growth in order to address the statistical issues of omitted variables bias and lag selection bias raised against earlier studies on the EKC [

Energy consumption is the factor most proximate to carbon dioxide emissions as the combustion of fuel in energy generation produces carbon dioxide. The amount of emissions depends on the type of fuel (different types of fuel have different carbon content) and on the kind of energy use. Energy consumption of transportation and industrial sectors, for instance, has higher carbon dioxide emission coefficients. Generally, energy sources in less developed countries are less efficient and more polluting than in developed countries. Likewise, both trade openness and urbanization may be positively correlated with carbon dioxide emissions. Trade entails movement of goods and services, and hence, greater energy consumption and more carbon dioxide emissions [

The inclusion of FDI in the model is based on the Pollution Haven Hypothesis that suggests the tendency of high-income economies to relocate polluting industries to countries with less stringent environmental policies in order to save on production costs [

This study estimates a linear logarithmic equation for two model specifications. The first model is a core EKC model with only Gross Domestic Product (GDP) and the square of GDP (GDPSQ) as explanatory variables:

The second is an expanded model with energy consumption (EN), trade openness (OPEN), urbanization (UR) and foreign direct investments (FDI) as additional explanatory variables:

Coefficients estimated from these models represent the elasticities of carbon dioxide emissions with respect to each of the explanatory variables. The signs of the coefficients representing the hypothesized relationships between carbon dioxide emissions and each explanatory variable discussed earlier are indicated below the corresponding variable.

Empirical tests on the relationships among the variables are done in three steps: 1) Augmented Dickey-Fuller (ADF) unit root tests for non-stationarity, 2) Autoregressive Distributed Lag (ARDL) bounds test to determine the existence of co-integrating relationships among the variables, and 3) Error Correction Model (ECM) to inspect short-run and long-run dynamics among the variables.

Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. Non-stationarity is an undesirable statistical issue for time series data as it results in a joint probability distribution that changes over time [

where Y is the data series being investigated, _{0} is a constant, α_{1} is the coefficient on a time trend, and p is the lag order of the autoregressive process. By including lags of the order p the ADF formulation allows for higher-order autoregressive processes. The lag length p is determined using the Akaike Information Criterion. The unit root test is then carried out under the null hypothesis δ = 0 which means that there is a unit root and the time series of the variable Y is non-stationary. Conversely, rejection of the null hypothesis indicates that the time series process contains no unit root which implies a stationary process.

A time series process will have to be integrated to the order of x, denoted by I(x), where x is the number of times the series has to be differenced in order for the process to become stationary. If a series is found to contain a unit root, an OLS regression can still be applied if the time series data are first made stationary by differencing the series to the order in which it is integrated. This method, however, has some serious consequences. As the error term also ends up being differenced, a moving average error will occur and the procedure will not be capable of yielding long-run properties. To avoid this, the cointegration method, an alternative way of analyzing time series data that have a unit root, is employed.

Autoregressive Distributed Lag (ARDL) bounds test for cointegration. Cointegration happens when the cumulated error processes between variables produce a stationary process. Asteriou and Hall claims that cointegration is an over-riding requirement for any economic model in non-stationary time series data. The particular technique used for this study is the ARDL bounds testing approach introduced by Pesaran et al. [

The bounds test approach to determine the existence of a cointegrating or long-run relationship among variables in a model is done by running an unrestricted regression equation. For the expanded model of this study, the unrestricted regression equation is:

ADRL tests the null hypothesis:

Error Correction Model (ECM). Once a cointegrating relationship is established, an ECM is performed to arrive at estimates of long-run and short-run elasticities of carbon dioxide emissions with respect to each of the explanatory variables. Long-run elasticities are derived from the following cointegration model (for the expanded model):

The difference between the observed value of the dependent variable, lnCO_{2}, and its long-run estimate is the error correction term, ECM:

Short-run elasticities are then derived by regressing the first difference of the dependent variable with the first difference of the independent variables plus ECM:

The coefficient of the ECM, λ, represents the speed of adjustment for short-run to reach long-run equilibrium. These regression procedures are done with the use of Gretl and Microfit statistical softwares.

This paper uses annual time-series data for the Philippines for the period 1971-2010. The data were obtained from the World Bank Development Indicators database. A summary description of the time series data used in the study is given in _{2} and measured in metric tons. Gross domestic product (GDP) is the sum of gross value added by all resident producers in the economy, plus any product taxes and less any subsidies not included in the value of the products. GDP is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. GDP data are in constant 2005 US dollars. Per capita GDP is GDP divided by midyear population. Energy use refers to use of primary energy before transformation to other end-use fuels. It is equal to indigenous production plus imports and stock changes, less exports and fuels supplied to ships and aircraft engaged in international transport. Trade openness is indicated by the share of total trade, the sum of exports and imports of goods and services, in GDP. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Foreign direct investment (FDI) is the net inflow of investments to acquire a lasting management interest (10% or more of voting stock) in an enterprise operating in an economy other than that of the investor. FDI is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as reflected in the balance of payments. The series used in the study is net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, divided by GDP.

The unit-root tests performed using the ADF method reveal that at the 5% significance level, all variables are found to be non-stationary with the exception of lnFDI (

The ARDL bounds test does not reveal a cointegrating relationship among the variables in the core EKC model. For the expanded EKC model, cointegration among variables becomes conclusive only at the 10% significance level. Because of these initial and subsequent findings of a non-existent EKC relationship, an expanded model without lnGDPSQ is run. The ARDL bounds test for this third model specification reveal a cointegrating relationship among the variables up to the 5% significance level (please refer to

Variable | Notation | Unit of measure |
---|---|---|

Per capita carbon dioxide emission | CO_{2} | Metric tons |

Per capita gross domestic product in real terms | GDP | US dollars |

Per capita energy use | EN | Kilograms (of oil equivalent per capita) |

Trade openness (share of trade in GDP) | OPEN | percent |

Urbanization (share of urban population in total population) | UR | percent |

Foreign direct investment (share in GDP) | FDI | percent |

Variable | Test statistic | P-value | Stationary or non-stationary^{a} |
---|---|---|---|

lnCO_{2} | −1.1463 | 0.5509 | Non-stationary |

lnGDP | −0.7816 | 0.8237 | Non-stationary |

lnGDPSQ | −0.7098 | 0.8426 | Non-stationary |

lnEN | −2.1039 | 0.2433 | Non-stationary |

lnOPEN | −1.6452 | 0.4593 | Non-stationary |

lnUR | −2.0127 | 0.2815 | Non-stationary |

lnFDI | −2.9526 | 0.0396 | Non-stationary (only at 1% significance level) |

^{a}Acceptance of the null hypothesis implies that there is a unit root and that the time series is stationary.

Model | F-statistic^{a} | 95% | 90% | ||
---|---|---|---|---|---|

Lower bound | Upper bound | Lower bound | Upper bound | ||

Core model | 2.9875 | 4.0957 | 5.2263 | 3.3678 | 4.3697 |

Expanded model (with lnGDPSQ) | 3.9166 | 2.8115 | 4.2017 | 2.3521 | 3.6020 |

Expanded model (without lnGDPSQ) | 4.7265 | 3.0317 | 4.3340 | 2.5201 | 3.7260 |

^{a}If the F-statistic lies between the bounds, the test is inconclusive. If it is above the upper bound, the null hypothesis of no cointegration is rejected. If it is below the lower bound, the null hypothesis of no cointegration cannot be rejected. The critical value bounds are computed by stochastic sumulations using 20,000 replications.

In the expanded model (Model 2), the signs of the coefficients of lnGDP and lnGDPSQ are reversed, but they remain to be insignificant. As both the core and expanded EKC models provide no evidence for an inverted-U relationship between CO_{2} emissions and GDP, the expanded model is modified by removing lnGDPSQ in the equation, which is referred to as Model 2a. The results of Model 2a are shown in the last two columns of _{2} emissions. The results for the other macroeconomic variables included in the study are robust. The significance and the signs of the coefficients are consistent in models 2 and 2a, and the magnitudes of the coefficients are very close.

There is a marked improvement in the adjusted R2 value with the inclusion of additional explanatory variables. The predictive value of the expanded model is almost double that of the core model. Among the added variables, EN and FDI turn out to be significant both in the short- and long-run. A 1% increase in energy use is associated with a 0.73% - 0.87% increase in carbon dioxide emissions in the short-run and a 1.28% - 1.59% increase in the long-run. The impact of FDI, albeit much less, is significant and positive, a 1% increase in FDI is associated with a 0.007% increase in carbon dioxide emissions in the short-run and 0.013% increase in the long-run. This result conforms with the Pollution Haven hypothesis, indicating that foreign direct investors in the Philippines are bringing in less clean technologies and industries. Another significant short-run predictor of carbon dioxide emissions is urbanization. The study’s negative and highly elastic impact of urbanization on carbon dioxide emissions in the short-run is in contrast with results of previous studies. Interestingly, the highly elastic impact of urbanization on carbon dioxide emissions seems to disappear in the long-run. The error correction term is likewise significant and negative, implying that the short-run estimates adjust at a rate of 54.5% - 56.8% per year towards their long-run equilibrium values.

Regressor | Coefficient | Standard error |
---|---|---|

Long-run | ||

lnGDP | 4.1484 | 57.3399 |

lnGDPSQ | −0.3301 | 4.0808 |

Constant | −13.2397 | 201.3661 |

Short-run | ||

dlnGDP | 1.9646 | 8.4397 |

dlnGDPSQ | −0.0477 | 0.5995 |

ECM (−1) | −0.1445^{*} | 0.0700 |

R2 | 0.4613 | |

Rbar2 | 0.3979 | |

F-statistic | 9.7048^{**} |

^{*}Indicates 0.10 significance level, ^{**}indicates 0.05 indicates level, ^{***}indicates 0.01 significance level.

Regressor | Model 2 (with lnGDPSQ) | Model 2a (without lnGDPSQ) | ||
---|---|---|---|---|

Coefficient | Standard error | Coefficient | Standard error | |

Long-run | ||||

lnGDP | −17.0954 | 16.1339 | 0.3063^{**} | 0.1191 |

lnGDPSQ | 1.2430 | 1.1514 | - | - |

lnEN | 1.5921^{**} | 0.4223 | 1.2854^{**} | 0.2699 |

lnOPEN | −0.0582 | 0.1216 | −0.0577 | 0.1172 |

lnUR | −0.4339 | 0.2229 | −0.4221^{*} | 0.2167 |

lnFDI | 0.0134^{*} | 0.0053 | 0.0134^{**} | 0.0051 |

Constant | 50.7144 | 54.5587 | −8.3335^{***} | 1.8562 |

Short-run | ||||

dlnGDP | −8.3930 | 8.1929 | 1.1017^{***} | 0.1923 |

dlnGDPSQ | 0.6774 | 0.5844 | - | - |

dlnEN | 0.8677^{**} | 0.1958 | 0.7297^{***} | 0.1563 |

dlnOPEN | −0.0317 | 0.0614 | −0.0327 | 0.0618 |

dlnUR | −5.5989^{**} | 1.3006 | −6.0014^{***} | 1.2606 |

dlnFDI | 0.0073^{*} | 0.0028 | 0.0076^{***} | 0.0028 |

ECM (−1) | −0.5450^{**} | 0.1231 | −0.5681^{***} | 0.1222 |

R2 | 0.8041 | 0.7951 | ||

Rbar2 | 0.7434 | 0.7404 | ||

F-statistic | 17.014^{**} | 19.399^{***} |

^{*}Indicates 0.10 significance level, ^{**}indicates 0.05 indicates level, ^{***}indicates 0.01 significance level.

Both the core and expanded models do not lend support for the EKC hypothesis in the case of the carbon dioxide pollutant in the Philippine setting. There is no evidence of a significant inverted-U relationship between economic growth and carbon dioxide emissions in the Philippines during the period covered in the study. Instead, they are found to have a significant positive linear relationship, suggesting that income growth policies must be subject to reasonably stringent environmental constraints related to CO_{2} emissions. The other macroeconomic variables that turn out to be significantly associated with CO_{2} emissions are energy use, urbanization and foreign direct investments.

The study shows that urbanization has a highly elastic negative impact on carbon dioxide emissions. This result is in contrast to Martinez-Zarzoso panel data study which reveals that lower-middle income countries (to which category the Philippines aptly belongs) typically exhibit a positive and less-than-unity urbanization elasticity of carbon dioxide emissions. A negative elasticity is typically observed in higher income countries where structural changes, advancements in clean technology and improvements in energy-intensity allow for carbon dioxide emissions reductions in the face of urbanization. Wan [

The positive elasticity of carbon dioxide emissions with respect to FDI which conforms with the Pollution Haven hypothesis may be explained by the sectoral distribution of FDI in the Philippines. The manufacturing sector accounts for the greater part of FDI in the country. From 1996 to 2009, the share of the manufacturing sector in total approved foreign investments averaged 53%. Though an association between carbon dioxide emissions and FDI is statistically established, the magnitude of the impact is small (very low elasticity) which may imply that the manufacturing FDIs in the Philippines are not using very dirty technologies.

Carbon dioxide emissions are inelastic with respect to energy use in the short run, but its response becomes elastic in the long run. This finding underscores the need for policies that promote cleaner sources of energy and more efficient use of energy so as to dampen the effect of energy use, an indispensable production input for the currently fast growing Philippine economy, on the environment.

RosalinaPalanca-Tan,Timothy AllenDy,AngelaTan, (2016) Relating Carbon Dioxide Emissions with Macroeconomic Variables in the Philippine Setting. Low Carbon Economy,07,12-20. doi: 10.4236/lce.2016.71002