Resources and Environment

Measuring the Effect of Foreign Direct Investment on CO2 Emissions in Laos

  • XIONG Chenran 1, 2 ,
  • WANG Limao , 1, 2, * ,
  • YANG Chengjia 3 ,
  • QU Qiushi 1, 2 ,
  • XIANG Ning 1, 2
  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. School of Economics and Management, Northwest University, Xi’an 710127, China;
WANG Limao, E-mail:

Received date: 2019-04-23

  Accepted date: 2019-07-09

  Online published: 2019-12-09

Supported by

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20010202)

Major Project of National Social Science Foundation of China(16ZDA041)


Copyright reserved © 2019


This paper aims to explore the determinants of CO2 emissions in Laos by accounting for the significant role played by foreign direct investment (FDI) in influencing CO2 emissions during the period 1990-2017. We apply a Johansen co-integration testing approach to investigate the presence of co-integration, and the empirical findings underscore the presence of a long-run co-integration relationship between CO2 emissions, FDI, per capita GDP, and industrial structure. We also employ an error-correcting model to examine the short-term dynamic effect of FDI on CO2 emissions. The empirical results show that FDI has a significant short-term dynamic effect on changes in CO2 emissions, indicating that the relationship between FDI and CO2 emissions is an inverted U-shaped curve. This is a validation of the EKC. Changes of FDI, per capita GDP, and industrial structure increase CO2 emissions. Based on the analysis results, this paper puts forward policy suggestions emphasizing the need for both Laotian policymakers and Chinese investors to improve eco-environmental quality.

Cite this article

XIONG Chenran , WANG Limao , YANG Chengjia , QU Qiushi , XIANG Ning . Measuring the Effect of Foreign Direct Investment on CO2 Emissions in Laos[J]. Journal of Resources and Ecology, 2019 , 10(6) : 685 -691 . DOI: 10.5814/j.issn.1674-764X.2019.06.014

1 Introduction

Currently, global warming and its consequences have become one of the main challenges worldwide facing human sustainable development and environmental quality (Hajilary et al., 2018). These challenges are affected by the overabundance of greenhouse gas (GHG) emissions, especially carbon dioxide (CO2) emissions from fossil fuel energy consumption (Lotfalipour et al., 2010; IEA, 2019). Therefore, global determination and countermeasures are needed to deal with global warming, and numerous politicians and researchers have become active (Shirazian et al., 2012). The Kyoto protocol (1992), the Doha Amendment to the Kyoto protocol (2012), and the Paris agreement on climate change signed by 197 countries (2015) are some of the results of this determination. All of these require states to reduce GHG emissions (Friedlingstein et al., 2014). In response to President Donald Trump’s decision to pull the US out of the Paris agreement, the European Union and China are preparing to strengthen their promises and accelerate joint efforts to reduce GHG emissions to deal with the global warming (Boffey et al., 2017). Against the background of emerging concern over the global warming, it is very important to examine the impact of foreign direct investment (FDI) on carbon emissions.
The late 1980s saw growth of the world economy, along with increased FDI and CO2 emissions. Global trends recorded in Table 1 show that average annual global per capita GDP increased from USD 2493.59 in 1981-1985 to USD 8579.57 in 2006-2010. This trend of average annual per capita GDP increases reached USD 9984.41 for 2011-2015. Similarly, average annual global per capita FDI of USD 12.03 in the early 1980s increased to USD 415.84 in 2011-2015, an increase of 56.47% over what was estimated for 2006-2010. These global increases in GDP and FDI have resulted in increased demand for energy generated by fossil fuels and increased CO2 emissions. For instance, average annual global per capita CO2 emissions increased from 4.06 tons in 1981-1985 to 4.98 tons in 2011-2015 (see Table 1). Although it is an accepted fact that FDI plays a key role in regional and national economic development, the ecological environmental consequences of increased FDI remain the subject of debate worldwide due to different theoretical hypothesis and empirical results. There is agreement that the effects of FDI on the ecological environment should be quantified to the greatest extent possible.
Table 1 Global trends in per capita GDP, FDI, and CO2 emissions
Year Per capita GDP (US$) Per capita FDI (US$) Per capita CO2 emissions
1981-1985 2493.59 12.03 4.06
1986-1990 3596.67 29.28 4.20
1991-1995 4674.34 38.61 4.09
1996-2000 5205.36 126.37 4.08
2001-2005 6023.82 126.96 4.30
2006-2010 8579.57 265.76 4.75
2011-2015 9984.41 415.84 4.98

Source: World Bank, World Investment Report (1992-2017), Shahbaz et al. (2015)

Theoretically, whether FDI has positive or negative effects on the environment is based on which dimension is dominant. There are three dimensions to the FDI-carbon emissions nexus: the pollution haven hypothesis, the pollution-halo hypothesis, and the scale effects hypothesis (Hoffmann, 2005; Pao et al., 2011). The empirical results of an earlier studies by Shahbaz et al. (2011) that selected 110 developed and developing economies showed that increasing FDI contributed to CO2 emissions. Similarly, an analysis of the effect of FDI on CO2 emissions in China by Ren et al. (2014) pointed out that FDI had contributed to increased CO2 emissions. Contrarily, a later study using Chinese regional data by Zhang et al. (2016), and two studies by Liu et al. (2017) and Jiang et al. (2018) using Chinese city-level data, indicated that FDI had a negative effect on CO2 emissions. Researchers have drawn on different evidence to analyze various regions within a single national economy. For example, a study by Peng et al. (2016) of Chinese regions showed that FDI had a positive effect on CO2 emissions in the eastern part of China while it exerted a negative influence on CO2 emissions in central and western China. Focusing on high, middle and low income countries, Shahbaz et al. (2015) reported that FDI increased ecological environmental degradation, supporting the pollution haven hypothesis (PHH). However, while investigating the contributions of FDI net inflows to carbon emissions in the G-20 countries, Lee (2013) found that FDI inflows had no relation to CO2 emissions. These examples show that results are mixed even among studies employing similar methods in the same study areas. This may be due to differences in the variables chosen, the transformations made, or the sample period.
During the past two decades, FDI in developing countries has clearly increased, especially in middle- and low-income countries (Shahbaz et al., 2015). According to data from the ASEAN Secretariat and UNCTAD, FDI inward flows to ASEAN by source country increased from USD 49.01 billion in 2008 to USD 135.62 billion in 2017, and the proportion of FDI inflows to ASEAN increased from 2.7% to 9.5% of total global FDI inflows during years from 2008 to 2017. At the same time, total carbon emissions in ASEAN rose from 1129.79 million tons in 2008 to 1391.53 million tons in 2016. Therefore, the FDI-carbon emissions nexus in ASEAN economies has attracted the attention of researchers. With respect to the ASEAN-5 countries (Malaysia, Indonesia, Singapore, the Philippines and Thailand), Chandran et al. (2013) found that FDI led to a significant increase in CO2 emissions, and this finding was supported with similar empirical evidence from a study by Baek (2016). Contrarily, a study of the ASEAN-5 countries by Zhu et al. (2016) stated that FDI can mitigate CO2 emissions in these high- emissions countries. Other evidence from certain ASEAN member economies, such as studies of Malaysia by Hitam et al. (2012) and Lau et al. (2014), and Vietnam by Tang et al. (2015), found that FDI promoted national economic growth, but was also one of the main determinants of increasing CO2 emissions. Numerous studies have considered the effect of FDI on CO2 emissions in ASEAN countries with high levels of economic development, but few studies have focused on the FDI-carbon nexus in Laos, Myanmar and Cambodia, three ASEAN countries with comparatively low levels of economic development.
The importance of FDI to economic growth and development of the economy (Gui-Diby, 2014; Pegkas, 2015) is undoubtedly paramount, particularly in developing countries like Laos with urgent and significant foreign investment needs. According to the pollution haven hypothesis (PHH), FDI can, on the one hand, help Laos introduce foreign capital to promote its economic development, but may, on the other hand, be intensifying local environmental pollution to generate a “pollution paradise” (Zhou et al., 2018). From the data recorded in Fig.1, we can see that CO2 emissions and FDI in Laos are both trending upwards. In light of what has been discussed above, this article identifies and quantitatively evaluates the relationship between FDI and CO2 emissions in Laos to examine what effects FDI has on CO2 emissions. The study’s purpose is to provide a reference for Laotian policy and Chinese investors as they make decisions in the future about investments and environmental economic policy choices and arrangements.
Fig. 1 Changes of FDI, CO2 emissions and per capita GDP in Laos, 1995-2017

Source: World Bank, World Investment Report (1996-2018)

2 Methods and data

2.1 Methods

This study aims to examine the relationship between FDI and CO2 emissions. Existing studies suggest that FDI can affect the scale, composition, and technology effects of CO2 emissions (Shahbaz et al., 2018). According to Cole et al. (2003) and Shahbaz et al. (2018), the scale effect refers to the fact that FDI may increase CO2 emissions by driving increases in the size of GDP and per capita GDP in an economy. Economic growth can lead to more energy consumption, thus increasing CO2 emissions (Pazienza, 2015). The composition effect refers to the structural shift from agriculture to industry in an economy, and then from industry to service sectors. The impact of the composition effect is dependent on the competitive advantages of an economy (Cole et al., 2003). The technology effect refers to the impact of the transfer or introduction of new technologies that may improve energy efficiency and result in reduced CO2 emissions.
Taking into consideration the theoretical background presented above, we can construct a general model describing the relationship between CO2 emissions and FDI:
$\text{C}{{\text{O}}_{2t}}=f\left( FD{{I}_{t}},PCGD{{P}_{t}},I{{S}_{t}} \right)$
where CO2 is annual carbon emissions, FDI is annual foreign direct investment, PCGDP represents per capita GDP, and IS refers to industrial structure or industrialization of Laos annually. Like other studies, we have transformed all the variables in Eq. (1) into a natural-log in order to employ a log-linear specification rather than a linear specification for the empirical model. Shahbaz et al. (2012) pointed out that the log-linear specification can provide more consistent and reliable empirical results, compared to the linear case. Hence, the log-linear specification is modelled as follows:
$\text{lnC}{{\text{O}}_{2t}}={{\alpha }_{0}}+{{\alpha }_{1}}\text{ln}FD{{I}_{t}}+{{\alpha }_{2}}\text{ln}PCGD{{P}_{t}}+{{\alpha }_{3}}\text{ln}I{{S}_{t}}+{{\varepsilon }_{t}}$
where α represent the regression coefficients and${{\varepsilon }_{t}}$is the regression error term.
We also examine whether the FDI-carbon emissions nexus has an inverted-U shape or a U-shape (Shahbaz et al., 2015). When this is done, the linear term for FDI in Eq.(2) must be transformed into a quadratic term. The augmented CO2 emissions function with the squared terms for FDI is reconstructed as follows:
$\begin{align}& \text{lnC}{{\text{O}}_{2t}}={{\beta }_{0}}+{{\beta }_{1}}\text{ln}FD{{I}_{t}}+{{\beta }_{2}}{{(\text{ln}FD{{I}_{t}})}^{2}}+ \\ & \ \ \ \ {{\beta }_{3}}\text{ln}PCGD{{P}_{t}}+{{\beta }_{4}}\text{ln}I{{S}_{t}}+{{\varepsilon }_{t}} \\ \end{align}$
where, it is likely that β1 is positive while β2 is negative or vice versa. Based on the theory of the environmental Kuznets curve, when β1>0 and β2<0, the relationship between FDI and carbon emissions is an inverted U-shaped curve, which represents the shape of the environmental Kuznets curve hypothesis (Shahbaz et al., 2015); but, if β1<0 and β2>0, it is U-shaped.

2.2 Data and variable selection

The data used in this paper are annual. Following Pao et al. (2011) and Omri et al. (2014), this study uses CO2 emissions as the dependent variable, and data are derived from World Development Indicators from the World Bank. FDI can be expressed by FDI inflows and outflows. In this study, we select FDI inward stock to represent FDI. The data on FDI are derived from The World Investment Report (1992- 2018) by UNCTAD. Previous empirical studies from researchers such as Li et al. (2005) and Agarwal (2012) used per capita GDP to test the impact of economic growth on CO2 emissions. Two existing studies show fixed results for the economic growth-carbon emissions nexus. One shows an inverted U-shaped relationship (Al-Mulali et al., 2016), and the other shows an N-shaped trajectory (Onafowora et al., 2014). With these studies in mind, we use per capita GDP (constant 2010 USD) as an independent variable, and data from the World Development Indicators of the World Bank. IS refers to industrial structure and is represented as a percentage of secondary industry GDP as a part of total GDP. We select it as an independent variable, and data are derived from the ASEAN Statistical Yearbook produced by the Statistics Division of the ASEAN Secretariat. Based on what has been mentioned above, this study will introduce FDI, PCGDP and industrial structure as the main variables for the model. Table 2 provides a brief description of each variable selected for use in this paper.

3 Results and discussion

3.1 Unit root test

To begin, before doing a unit root analysis to test the stability of the variables, we performed descriptive statics for each variable: the results are presented in Table 3. Table 3 shows that CO2 emissions are less volatile than FDI, but more volatile compared to per capita GDP and industrial structure. The volatility of FDI is highest and the volatility of industrial structure is lowest among all variables. Per capita GDP is less volatile than CO2 emissions and FDI, but more volatile than industrial structure. In addition, the correlation coefficients indicate that the probability of the existence of multicollinearity between different dependent variables is small.
Table 2 Description of variables
Variable Symbol Definition Unit Source
Carbon emissions CO2 CO2 emissions from fossil fuels Kiloton World Bank
Foreign direct investment FDI FDI inward stock Millions of dollars UNCTAD
Per capita GDP PCGDP GDP to Population USD World Bank
Industrial structure IS Share of industrial sectors covering GDP % the ASEAN Secretariat

Next, we adopted the augmented Dickey-Fuller (ADF) unit root test with intercept, trend and intercept, and none to determine the stability of the variables. The results are presented in Table 4. Note that the original sequence of all variables does accept the null hypothesis and has a unit root at the 5% significance level, indicating that the sequence is non-stationary. However, after first difference, we found stationary for all variables. This rejects the null hypothesis of having a unit root at significance level within 10%, indicating that sequence is stationary at first difference. The stationary at first difference shows that a long-term equilibrium relationship exists between variables of CO2 emissions, FDI, PCGDP and IS time series. Therefore, we require further verification and adopt the co-integration test.

Table 3 Descriptive statistics of variables (1990-2017)
lnCO2 lnFDI lnPCGDP lnIS
Mean 6.777592 6.422771 6.720911 3.177491
Median 6.927747 6.446374 6.660189 3.175600
Maximum 7.701083 8.788746 7.461870 3.558201
Minimun 5.359836 2.564949 6.135565 2.667228 0.747343 1.634706 0.416619 0.231562
Skewness -0.679633 -0.780247 0.261508 -0.343778
Kurtosis 2.141819 3.060081 1.825126 2.314039
Jarque-Bera 3.014756 2.845212 1.929521 1.100489
Probability 0.221490 0.241085 0.381075 0.576809
Table 4 The results of unit root analysis (1990-2017)
Variables Type
ADF value Critical values Test
1% 5% 10%
lnCO2 (C,0,0) 2.375 -2.653 -1.954 -1.610 unstable
lnFDI (C,0,1) 1.010 -2.657 -1.954 -1.609 unstable
(lnFDI)2 (C,T,1) -2.697 -4.356 -3.595 -3.233 unstable
lnPCGDP (C,T,0) -2.257 -4.339 -3.587 -3.229 unstable
lnIS (C,T,1) -3.084 -4.356 -3.595 -3.233 unstable
DlnCO2 (C,0,0) -4.067 -2.657 -1.954 -1.609 stable
DlnFDI (C,0,0) -1.848 -2.656 -1.844 -1.609 stable
D(lnFDI)2 (C,T,0) -2.868 -3.711 -2.861 -2.629 stable
DlnPCGDP (C,T,0) -4.364 -4.356 -3.595 -3.233 stable
DlnIS (C,T,1) -4.861 -4.374 -3.603 -3.238 stable

Note: The type C,T,K represent constant, trend and the lag length, respectively. The optimal results are determined by consideration of AIC. The results remain valid figures in a unified manner.

3.2 Co-integration test

Traditional co-integration testing approaches include the Engle-Granger co-integration test and the Johansen co- integration test. The Engle-Granger co-integration test is usually used to test the co-integration relationship of two variables while Johansen co-integration test is used to examine the co-integration relationship of numerous variables. Therefore, we have applied the Johansen co-integration testing approach to test whether a co-integration relationship is present. The results of the co-integration relationship between CO2, FDI, PCGDP and IS are reported in Table 5. The results presented in Table 5 validate the presence of a co-integration relationship between CO2 emissions and its determinants. This confirms the existence of a long-term relationship between FDI, per capita GDP, industrial structure, and CO2 emissions for Laos over the period 1990- 2017.
Table 5 The results of the Johansen co-integration test
No. of Eqn(s) Eigenvalue Trace statistic 5% critical value P-value
None* 0.820 116.592 69.819 0.000
At most 1* 0.688 71.994 47.856 0.000
At most 2* 0.614 41.682 29.797 0.001
At most 3* 0.435 16.933 15.495 0.030
At most 4 0.077 2.077 3.841 0.150

* Denotes rejection of the hypothesis at the 5% level

3.3 Error-correcting model and analysis

The model with the linear and squared terms of FDI in Eq.(3) shows a long-term equilibrium relationship among the variables, but also shows the existence of significant short-term divergences that can be given an error-correction representation. In order to enhance the accuracy of the model, we introduce the error term in the co-integration regression as the equalization error. Therefore, this paper adopts the error-correcting model to capture the short-term dynamics while making variables consistent with long-term dynamics. Like previous studies by Enger et al. (1987), Arize et al. (1992), Tambi (1999), and Shahbaz et al. (2012), the error-correcting model to be transformed from the Eq. (3) is expressed as
$\begin{align} & \Delta \text{lnC}{{\text{O}}_{2t}}={{\gamma }_{1}}\Delta \text{ln}FD{{I}_{t}}+{{\gamma }_{2}}\Delta {{(\text{ln}FD{{I}_{t}})}^{2}}+{{\gamma }_{3}}\Delta \text{ln}PCGD{{P}_{t}}+ \\ & \ {{\gamma }_{4}}\Delta \text{ln}I{{S}_{t}}+\varphi {{\mu }_{t-1}}+{{\varepsilon }_{t}} \end{align}$
where$\Delta $ is the difference operator, indicating the short-term fluctuation of the variable,$\Delta \text{ ln}FD{{I}_{t}}$is the change in the ‘desired’ equilibrium level, and ${{\mu }_{t-1}}$ is the lagged version of Eq. (3). The error-correcting model is then estimated to include$E{{C}_{t-1}}$as a regressor, which is used to capture the short-term dynamics. In Eq. (4), the fluctuation of the dependent variable can be divided into two parts: one is short- term fluctuation, and the other is long-term equilibrium.
OLS estimates of the regression are used to estimate the coefficient of Eq. (4), and the short-term results of the error correcting model are reported as follows:
$\begin{align} & \Delta \ln \text{C}{{\text{O}}_{2t}}=0.194\Delta \ln FD{{I}_{t}}-0.026\Delta (\ln FD{{I}_{t}})_{{}}^{2}+ \\ & (0.536)(-0.620) \\ & \ 1.210\Delta \ln PCGD{{P}_{t}}+1.765\Delta \ln I{{S}_{t}}- \\ & \ (1.170)\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (4.079) \\ & \ 0.803{{\mu }_{t-1}}+{{\varepsilon }_{t}} \\ & \ (-2.649) \end{align}$
Like Eq. (3) of included a squared term of FDI to explore whether an inverted-U shape or U-shape exists between FDI and CO2 emissions. Based on the coefficients of the Eq. (5), we find that in the short-term, the linear term of FDI has a positive effect and the squared term a negative effect on CO2 emissions at the 1% and 5% significance levels. This shows the presence of an inverted-U shaped curve, thus supporting the environmental Kuznets curve hypothesis to describe the relationship between FDI and CO2 emissions in Laos. The estimate of is -0.803 and has a negative sign and significance at the 5% level, and indicates that changes of FDI, per capita GDP, and industrial structure have a significant effect on the next CO2 emissions in the short-term fluctuation. The study shows that the short-term deviations towards long-term are corrected.
The parameter estimation results show that the elastic coefficients of CO2 emissions in the first-order lag period of FDI, per capita GDP, and industrial structure in the short- term are positive with values of 0.194, 1.120, and 1.765, respectively. That is, every 1% increase in FDI leads to an increase in carbon emissions of 0.194%. A 1% increase in per capita GDP will cause an increase of 1.12% in CO2 emissions, and a 1% increase in industrial structure increases CO2 emissions by 1.765%. It can be concluded that an acceleration of FDI inflows, economic growth, and industrialization in Laos leads to an increase in CO2 emissions.
Although FDI inflows to Laos are increasing, an examination of the coefficients of the variables shows that FDI has less effect on increasing CO2 emissions than per capita GDP growth and industrial structural changes. The main reason for this is that Laos is a resource-rich, low-income country with an economy heavily reliant on mining and hydropower. Foreign investment is focused on sectors like agriculture, social development, transportation, and hydropower, rather than concentrated in CO2 emissions-intensive sectors. For instance, Asian Development Bank (ADB) investment assistance is channeled to four core sectors: agriculture, natural resources, and rural development; education; energy; and water and other municipal infrastructure (ADB, 2018). The Sino-Lao railway and Nam Ngum No. 4 hydropower project are being built by China. A USD 1.2 billion, 410-megawatt hydropower project in the southern panhandle of Laos has been built by a South Korean firm (Freeman, 2019).
Our study has some limitations. A richer set of indicators or variables to examine the factors influencing CO2 emissions should be taken into consideration in future studies. CO2 emissions can be evaluated in the future using per capita CO2 emissions and carbon intensity. The increasing concern for human sustainable development and environmental protection, and green and low-carbon economies will become key topics for future research.

3.4 Policy suggestions

According to the results of our analysis, it is evident that there is a positive relationship between FDI and CO2 emissions in Laos. Such a finding is valuable to Laotian policy makers who are tasked with attracting FDI and to foreign investors, especially Chinese investors, who are investing heavily in Laos.
Currently, the Laotian government is promoting policies to transform Laos from a land-locked to a land-linked country, and is aiming to advance out of least developed country status by 2020. As a part of its development strategy, Laos has pursued a reform agenda in order to attract foreign investment that enriches domestic capital sources to fuel economic growth. Expanding domestic investment and attracting foreign investment will continue to be the primary tasks of Laotian policymakers well into the future. For example, the Laotian government recently announced that it expects domestic and foreign investment to rise in 2019 to more than USD 2.7 billion, equivalent to 14% of the country’s GDP (Freeman, 2019). Key for Laos is an approach to economic development that that attracts foreign investment and allows CO2 emissions growth to be curtailed as economic growth occurs. Economic growth must be coordinated to attract foreign investment and protect the ecological environment. Our policy suggestions include: (1) Laos should continue to focus on the introduction of FDI, and encourage both domestic and foreign-invested companies to implement high-quality, low-carbon, green investment projects; (2) Laos should enhance environmental standards and strengthen environmental supervision of foreign-invested companies and projects to prevent them from pursuing short-term economic growth while ignoring the eco-environmental consequences. For example, we suggest levying a carbon tax on high-pollution FDI-invested projects, and enacting corresponding laws and regulations for governing carbon emissions.
Recently, China has become one of the most important investors in Laos (Fig. 2). The largest FDI project in Laos is a USD 6.7 billion north-south railway line that China is constructing, intended to create a direct route between China and the Southeast Asian peninsula, and to promote the social and economic development of Laos. Domestically, the Chinese government has launched a new “ecological civilization” policy paradigm that promotes “green shift” development (Tracy et al., 2017). For its dealings with other countries, China has proposed a new “Green Silk Roads” initiative (Liu, 2019). Laos stands at the crossroads of the Belt and Road initiative, making it crucial to the joint construction of “Green Silk Roads”. Therefore, Chinese investors must attach importance to ecological environmental protection in their current and future investment in Laos, and aim at reducing environmental risks so as to avoid mega-investment project failures like that of the Myitsone hydropower project in Myanmar (Kiik, 2016). In addition, Chinese investors and enterprises should raise the monitoring level of pollutant emissions and focus on the eco- environmental effects of investment projects in Laos.
Fig. 2 FDI inward flows in Laos by source countries, 2014- 2017 (The ASEAN Secretariat, 2018).

4 Conclusions

Based on time series data for Laos over the period 1990- 2017, this paper assesses the impact of FDI on CO2 emissions in Laos. The analysis is based on a series of equations using Johansen co-integration and error-correction representation procedures, and reaches the following conclusions:
(1) We apply the Johansen co-integration testing approach to investigate the presence of co-integration, and the empirical findings validate the presence of a long-term co-integration relationship among the variables.
(2) We employ an error-correcting model to examine the short-term dynamic effect of FDI on CO2 emissions, and the empirical results show that FDI has a significant short-term dynamic effect on changes in CO2 emissions, indicating that the relationship between FDI and CO2 emissions is an inverted U-shaped curve, supporting the EKC hypothesis. The first-order lag period of FDI, per capita GDP, and industrial structure in the short-run has a positive effect on CO2 emissions and leads to increase in CO2 emissions.
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