# Protector or Polluter? Environmental Impacts of Remittances

Oussama Ben Atta, Refk Selmi, Farid Makhlouf

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Oussama Ben Atta, Refk Selmi, Farid Makhlouf. Protector or Polluter? Environmental Impacts of Remittances. 2023. hal-04194940

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# Protector or Polluter? Environmental Impacts of Remittances

Oussama Ben ATTA<fnref n="1" />, Refk SELMI<fnref n="2" /> and Farid MAKHLOUF<fnref n="3" />

2023

**Abstract:** As remittances are largely viewed as potential factor of financial development and economic growth, their role in polluting the environment cannot be denied. In this paper, we investigate the environmental effect of migrant remittance in the global south. By using panel data of 37 countries in the southern hemisphere from 1980 to 2014, results show that remittances worsen the environment. We, therefore, support the remittances-led emission hypothesis. Interestingly, we found that the inflows of remittances do not affect CO2 emissions directly, but indirectly through household consumption, private investment, urbanization and importations. Our results deeply suggest that policymakers in the South should (1) consider remittances as a policy instrument to design strategies related to sustainable and responsible investing, and (2) channel remittances into green consumptions and investments.

**Keywords:** Remittances; CO2 emissions; developing countries.

**JEL Codes:** F24; Q56.

<footnote n="1">¹ University of Pau, oussama.benatta@gmail.com</footnote>

<footnote n="2">² ESC Pau Business School, refk.selmi@esc-pau.fr</footnote>

<footnote n="3">³ ESC Pau Business School, farid.makhlouf@esc-pau.fr</footnote>
# 1. Introduction

Remittances are a significant source of financing for the majority of developing countries (see inter alia, Makhlouf, 2013; Makhlouf et al, 2019; Ratha, 2017). Remittances are not only considered as a new financial phenomenon, but they are perceived as a significant source of income owing to its significant economic impact (Meyer and Shera, 2017). According to the World Bank, remittances to low- and middle-income countries reached more than $500 billion in 2019. The economic impact of remittances on countries of origin is relatively well supported. However, the environmental impacts of remittances have devoted much less attention (see, for example, Ahmad et al., 2020; Brown et al., 2020; Sharma et al., 2019), although environmental issues are becoming increasingly important in the economic literature. Climatic changes, energy crises, hikes in energy prices, and the rapidly climbing carbon emission levels have further added fuel to this debate.

The relationship between migration and the environment is traditionally linked to population movement (Findley, 1944; Kane, 1995). The impact of remittances on emigration countries is likely to be complex (Makhlouf, 2013). Remittances can have macroeconomic effects notably on consumption and investment (Bahadir et al, 2018). They can exert a significant impact on education, health and cultural norms of recipient households (Acosta, 2011; Amuedo-Dorantes and Pozo, 2011; Acharya and Leon-Gonzalez, 2014; among others).

The use of remittances will depend on the level of wealth of the household receiving them. In this respect, it can be used for consumption and investment (Bahadir, 2018; Edelbloude et al, 2017). A relatively poor household will spend its money on improving its situation without taking into account the Carbon Loan (for example in the purchase of a vehicle or the installation of electrical machines (purchase of household appliances). In general, a household that is
relatively affluent will use remittances in cost reduction as installation of machines that consume less energy and installation of solar panels or purchase of new and energy-efficient cars.

Davis and Lopez-Carr (2010) find that long-term remittances reduce the size of the households receiving them. This result is interesting in the sense that the reduction of the household size can reduce energy consumption in general. Day and Içduygu (1999) observe that the conspicuous consumption of Turkish households with a migrant is higher than those without a family member abroad. Therefore, this behavior can have a significant effect on energy in general and CO2 emissions in particular. Durand et al, (1996) finds in Latin American Countries that remittances are used mainly for consumption.

Remittances also help to stimulate financial development. Accurately, an increase in remittances inflow prompts an increase in demand for financial services. Moreover, remittances can help small companies by offering capital and also enhance domestic investment which further improves the financial development. Many surveys have been conducted on “the effect of financial development on carbon emission” (for instance, Ayeche et al. 2016; Farhani and Ozturk, 2015; Li et al., 2015; Mugableh, 2015). All these studies indicated that financial development raises carbon emissions. One can cite three potential factors explaining the impact of financial development on environmental deterioration: First, the enhancement of stock markets' performances to cut back on their financial expenses, expand credit channels and operational risks, which accordingly makes companies able to improve finance, set up the latest resources and strengthen their production volume, thus rising CO2 emissions. Second, the development of financial system attracts foreign direct investment so as to improve economic growth and to increase CO2 emissions. Third, efficient financial intermediation is highly conducive to consumer' loan activities, which allow consumers to buy automobiles, houses,
refrigerators, air conditioners, washing machines, among others, emitting more carbon dioxide, threatening the environment. Arguably, Ahmad et al, (2018) found that positive component of financial development has a positive effect on CO2 emissions.

Besides, remittances can lead to additional energy consumption which can increase CO2 emissions. In that context, Alam et al. (2010) show a positive relationship between energy consumption and CO2 emissions. As remittances react to the evolution of oil prices (Makhlouf and Kasmaoui, 2018) and also impact their consumption (Ahmad et al., 2018), and in turn on CO2 emissions, one can expect an indirect significant environmental impact of remittances. In Jamaica for the period 1976-2014 using the environmental Kuznets curve Brown et al. (2020) estimate the impact of remittances on CO2 emissions, and find the same classical results of the Kuznets curve. Their main result shows that the relationship between remittances and CO2 is inverted U-shaped. They find also that in the short run there is an asymmetric response of CO2 to remittances. The relationship between remittances and CO2 emission is not linear (Ahmad et al, 2019; Brown et al., 2020). In the case of Nepal Sharma et al. (2019) find that in the long term there is a negative relationship between remittances and CO2 emissions, while income increases CO2 emissions.

This paper attempts to this gap in the existing literature by empirically investigating the effect of remittances on CO2 emissions in a panel of 37 developing countries. The level of CO2 emissions from developing countries has fast overcome that of developed countries (WDI, 2014). In fact, the amount of CO2 emissions in developing countries from energy consumption is significantly increasing over the last two decades due to the rapidly climbing growth of human populations and the increasing levels of urbanization and industrialization processes. The issue of climate change has not the same resonance in developing countries and in developed countries.
Our sample arises a special interest as the majority of these countries are getting drier with global warming mainly attributed to population growth and rapid urbanization which could eventually climb the level of pollution. Moreover, remittances to developing countries are a major economic and social mainstay. They can amount to as much as around 40 % of GDP (WDI, 2019). Due to the magnitude of these financial flows, governments in developing countries are integrating migrant remittances as part of their economic development agenda.

Importantly, it must be stressed at this stage that a major obstacle to study the causal impacts of remittances inflows on CO2 emissions is that remittances are potentially endogenous to environmental degradation. Omitted variables are a serious concern. To address this concern, we exploit variations in global per capita remittances to instrument for remittances received by each country. The IV results reveal that remittances positively contribute to environmental degradation. More specifically, we find that a one percent increase in the remittance's per capita ratio increase CO2 emissions by 0.3 percent. Next, we document the mechanisms underlying the causal effect of remittances on CO2 emissions. We robustly show that the positive effect channels through household consumption, private investment, urbanization and importations.

The rest of this paper is organized as the following. Section 2 introduces the data and describes the conducted methodology. Section 3 reports and discusses the main findings. Section 4 concludes and provides relevant policy implications.
## 2. Empirical Framework

### 2.1. Data

This paper covers a panel of 37 developing countries over the period running between 1980 and 2014⁴. The variables required to isolate the effect of remittances on CO2 emissions are fairly standard. All data on variables are drawn from the World Bank database. Our dependent variable corresponds to CO2 emissions (per capita). We follow the literature and consider the latter as our environmental degradation indicator. The interest variable, remittances, is defined as the sum of two components: personal transfers and compensation of employees. In an attempt to account for heterogeneity in the demographic size of countries, we standardize our measure of remittances as the amount of remittances per capita. Regarding the control variables, we consider a broad set of variables commonly employed in the literature (in particular, urbanization and trade openness). Descriptive statistics are provided in Table 1. We also show the evolution of CO2 emissions per capita and remittances per capita between 1980 and 2014 in Figures 1 and 2. The increasing trend of CO2 emissions over the whole period is shown in Figure 1. In Figure 2, we observe that per capita remittances diminished between 1980 and 2000⁵, and it is only in the last two decades that the trend is growing.

<footnote n="4">⁴ The classification of countries in terms of income follows the World Bank conventions.</footnote>

<footnote n="5">⁵ The decline in the ratio of remittances per capita between 1980 and 2000 can be explained by the population growth rate exceeding that of remittances. In addition, it should be noted that it is only starting in the 2000s that remittances are measured more precisely (Clemens and Mckenzie, 2014).</footnote>
<table><caption>**Table 1: Descriptive statistics**</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Carbon emissions</td><td>1,225</td><td>-.4481825</td><td>1.267061</td><td>-3.110603</td><td>2.300529</td></tr><tr><td>Remittanes per capita</td><td>1,083</td><td>.5781621</td><td>2.046612</td><td>-6.446126</td><td>4.583524</td></tr><tr><td>Urbanization</td><td>1,225</td><td>3.675015</td><td>.5153034</td><td>1.552021</td><td>4.514994</td></tr><tr><td>Trade openness</td><td>1,225</td><td>3.913902</td><td>.5108327</td><td>2.212206</td><td>5.006985</td></tr><tr><td>HH consumption</td><td>1,061</td><td>24.02346</td><td>1.635102</td><td>20.7084</td><td>28.05421</td></tr><tr><td>Investment</td><td>1,189</td><td>2.984225</td><td>.3297559</td><td>1.711844</td><td>4.492966</td></tr><tr><td>Imports</td><td>1,225</td><td>3.313065</td><td>.5371134</td><td>1.108484</td><td>4.590428</td></tr><tr><td>Financial development</td><td>1,216</td><td>3.156237</td><td>.7498105</td><td>.4796638</td><td>5.11502</td></tr><tr><td>Remittances per capita (World)</td><td>1,084</td><td>3.088194</td><td>.7725199</td><td>1.987674</td><td>4.535076</td></tr></tbody></table>

Note: All variables are in log (Source: WDI and authors’ calculations)

<figure><img src="image_3.png" /><figcaption>**Figure 1: CO2 emissions (metric tons per capita) trend in developing countries**</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2 : Migrant remittances (per capita) evolution in developing countries</figcaption></figure>

## 2.2. Empirical model

To investigate the effect of remittance flows on CO2 emissions, we rely on the following regression model:

$$Y_{i,t} = \alpha_{it} + \beta X_{i,t} + \omega Z_{i,t} + \varepsilon_{i,t} \quad (1)$$

Where $Y_{i,t}$ is the log of the per capita CO2 emissions, $X_{i,t}$ is the log of remittances per capita, $\beta$, our coefficient of interest, captures the effect of remittances on CO2 emissions. $Z_{i,t}$ represents the set of control variables previously reported which are urbanization and trade openness (all expressed in logs). Urbanization accounts for the effect of rural/urban migration and trade openness considers the role of trade in environmental pollution. $\alpha_{it}$ is a country-specific
intercept and $\varepsilon_{i,t}$ is an error term that is normally and identically distributed (iid). The subscripts $i$ and $t$ are a country and a time index, respectively.

As a starting exercise, we estimate Eq. 1 using an ordinary least squares (OLS). In fact, under the assumption that remittances are exogenous, an OLS model would give unbiased estimate of the remittances effect. However, since the amount of remittances may be linked to unobserved country-level confounders or encountered a reverse causality and measurement error issues, we expect that the estimates of the coefficient of interest, $\beta$, will be skewed using standard regression models and can no longer be given a causal interpretation. We effectively address this issue in the following.

## 2.3. Identification strategy

The identification of the remittances impact on CO2 emissions is plagued by the endogeneity of remittances. First of all, we may encounter the unobservable heterogeneity issue. There may be unobservable country-level factors that may affect jointly remittances and CO2 emissions. This can arise because it is likely that high-carbon emitting countries may receive large amounts of remittances. Second, there exists a reverse causality concern. Precisely, while remittances might impact CO2 emissions, carbon emissions might also determine the level of remittances. In fact, it was recently documented in the literature that climatic factors affect migration (Beine and Parsons, 2015; Burzynski et al., 2018; Cattaneo et al., 2019). Climate change may lead to a reduction in income and therefore lead to an increase in migration flows. That said, increasing migration flows normally results in an overall increase of remittances. As a conclusion, CO2 emissions may affect remittances. Finally, the third endogeneity source refers to measurement errors. The remittances considered in our study are those of the World Bank and only include remittances made through official - formal - channels. Hence, in such a case, the magnitude of
remittances may be underestimated. The issue of unobservable heterogeneity may be appropriately solved by including country fixed effects. However, fixed effects cannot tackle the two other possible causes of endogeneity.

Our strategy consists, therefore, of estimating Eq. (1) by instrumentalizing the remittances variable. To do so, we follow Gapen et al., (2009) and consider the amount of remittances to the rest of the world as an exogenous variation of remittances received by each country. The underlying intuition is that an overall increase in remittances worldwide provides an explanation for the change in remittances within each country. In fact, as outlined by Gapen et al. (2009), this instrument permits capturing the effects of lower transaction costs of remitting and further systematic changes in the microeconomic determinants of remittances, as such changes are expected to increase remittances flows worldwide. As our variable of interest corresponds to remittances per capita, we employ more narrowly as an instrument the global remittances as a ratio of world GDP.

# 3. Results

## 3.1. *Baseline estimates*

This section presents the preliminary results when using OLS estimator. Table 2 reports the correlation between remittances per capita and CO2 emissions. The first column links remittances per capita and CO2 emissions without any control variables. It shows that remittances may drive an increase in carbon emissions. The second column of Table 2 relates CO2 emissions to remittances per capita, urbanization and trade openness. The coefficient of the remittance variable is positive and statistically significant meaning that as remittances per capita ratio increase, carbon emissions may increase. A 10% increase in remittances per capita is associated with a 0.2% increase in CO2 emissions. The third model (column 3) adds country fixed
effects as an additional control to limit unobservable heterogeneity. We prefer this specification given that it increases the explanatory power of the model. The variable of interest remains significant, supporting the positive correlation between remittances and CO2 emissions. We note that the environmental impact of remittances seems stronger in this case. An increase by about 10% in remittances per capita leads to a rise in CO2 emissions by about 1%. Lastly, the coefficients associated with control variables have the expected sign. Specifically, urbanization and trade openness are positively correlated to CO2 emissions.

<table><caption>Table 2: Baseline estimates</caption><thead><tr><th>VARIABLES</th><th>(1)<br>CO2 emissions</th><th>(2)<br>CO2 emissions</th><th>(3)<br>CO2 emissions</th></tr></thead><tbody><tr><td>Remittances per capita</td><td>0.115***<br>(0.0188)</td><td>0.0220*<br>(0.0132)</td><td>0.102***<br>(0.00730)</td></tr><tr><td>Urbanization</td><td></td><td>1.907***<br>(0.0469)</td><td>0.338***<br>(0.0501)</td></tr><tr><td>Trade Openness</td><td></td><td>-0.127**<br>(0.0534)</td><td>0.284***<br>(0.0340)</td></tr><tr><td>Constant</td><td>-0.583***<br>(0.0399)</td><td>-7.016***<br>(0.248)</td><td>-1.450***<br>(0.205)</td></tr><tr><td>Country FE</td><td>NO</td><td>NO</td><td>YES</td></tr><tr><td>Observations</td><td>1,083</td><td>1,083</td><td>1,083</td></tr><tr><td>R-squared</td><td>0.034</td><td>0.620</td><td>0.963</td></tr></tbody></table>

Standard errors in parentheses

***p<0.01, **p<0.05, *p<0.1

Model (1) links CO2 emissions to remittances per capita without any controls. Model (2) includes controls.

Model (3) adds country FE.

## 3.2. IV estimates

The estimates we show in the last sub-section cannot be interpreted as causal, as we discuss in sub-section 2.2. The estimated association between remittances and CO2 emissions may in some ways reflect an independent “third” variable effect. A further concern we discuss is the reverse causality between remittances per capita and carbon emissions as well as the measurement error
of remittances variable. In this sub-section, we display the estimates when addressing these issues, by using instrumental variable strategy. We'll first discuss the first-stage results and subsequently the second stage estimates.

## First-stage results

Table 3 columns (1) and (3) report the results for the first-stage regression. The estimated effects of our instrument on remittances per capita are positive and highly significant, regardless of the specification (with or without controls). This implies that remittances at the global level accounts for the variation in remittances received for each country, mainly through cost reduction and transfer facility. Lastly, the first-stage F-statistic for excluded instrument is higher than ten indicating that the instrument is different from zero and therefore satisfy the instrument relevance<fnref n="6" />.

## Second-stage results

Table 3 columns (2) and (4) present the second-stage estimates. The coefficients reported in the first row represent the second-stage estimates of the effect of remittances per capita on carbon emissions. The estimates document a positive and significant effect of remittances on CO2 emissions. In particular, we find that a one percent increase in remittances per capita increase

<footnote n="6">⁶ We may believe that remittances at the global level affect CO2 emissions through other channels, in particular worldwide interactions among countries. This may imply that our instrument does not meet the exclusion criterion restriction. Two points let us discard this potential threat. First, we have generated remittances at the global level by excluding remittances received by each country. Second, if the instrument affects CO2 emissions, it will pass through the exchange of trade between countries. As we control in our model with trade openness, we capture the degree of exposure of countries to the rest of the world</footnote>
CO2 emissions by 0.3 percent. It is worth nothing that the instrumental variable estimates are substantially larger than the FE-OLS estimates reported in Table 2. Thus, without addressing the endogeneity bias, the true effects of remittances per capita on carbon emissions are underestimated.

<table><caption>Table 3: IV results</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Remittances per capita</th><th>CO2 emissions</th><th>Remittances per capita</th><th>CO2 emissions</th></tr></thead><tbody><tr><td>Remittances per capita</td><td></td><td>0.380***<br>(0.0294)</td><td></td><td>0.260***<br>(0.0295)</td></tr><tr><td>Global remittances per capita</td><td>0.380***<br>(0.029)</td><td></td><td>0.716***<br>(0.070)</td><td></td></tr><tr><td>Urbanization</td><td></td><td></td><td>-1.527***<br>(.295)</td><td>0.237***<br>(0.0859)</td></tr><tr><td>Trade Openness</td><td></td><td></td><td>0.410*<br>(0.236)</td><td>0.130**<br>(0.0524)</td></tr><tr><td>Constant</td><td></td><td>0.838***<br>(0.0922)</td><td></td><td>-0.546<br>(0.365)</td></tr><tr><td>Country FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>First-stage F-stat</td><td>145,21</td><td></td><td>103,98</td><td></td></tr><tr><td>R-squared</td><td></td><td>0.911</td><td></td><td>0.946</td></tr><tr><td>Observations</td><td>1,083</td><td>1,083</td><td>1,083</td><td>1,083</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Model 1 (columns 1 and 2) links CO2 emissions to remittances per capita with country FE and without controls. Model 2 (columns 3 and 4) adds controls.
### 3.3. Mechanisms

The estimates discussed earlier support a positive and significant impact of remittances on carbon emissions. However, what are the channels through which the relationship occurs? We provide evidence for four potential channels through which the level of remittances may contribute to more carbon emissions and in turn harm more the environment.

First, remittances may serve to relieve the household budget and enhance consumption (Combes and Ebeke, 2011; De and Ratha, 2012). Column 1 of Table 4 reports a positive association between remittances and household private consumption. The fact that household consumption increases suggest a rise in aggregate demand and hence additional carbon emissions (Ahmad et al., 2019). This is shown in Column 2.

Next, the second transmission channel helping to explain the relationship between remittances and CO2 emissions is private investment. Remittances can in fact be allocated to investment purposes (Arif, 1999; Woodruff and Zenteno, 2007; Jena, 2018). Migrants may remit to invest in their origin country; To further explore this mechanism, we regress private investment on remittances. The results are reported in Column 3 and indicate a positive relationship linking remittances and private investment. Moreover, the latter appears to cause greater carbon emissions (Column 4).

Third, the relationship between remittances and pollution can be linked via imports. As remittances can be targeted to meet the consumption needs of recipient households, the latter may demand more goods. If domestic production cannot satisfy agents' domestic demand, the overall supply/demand balance may be achieved by means of imports. In our case, we found a positive association between remittances and imports (Column 5). Also, previous empirical studies show that trade openness has an influence on CO2 emissions (Hossain, 2011; Ren et al., 2014). We
provide support to this evidence and find a positive and significant connection between imports, one of the components of trade openness, and carbon emissions (Column 6).

Lastly, urbanization may be one mechanism that accounts for the positive effect of
remittances on environmental degradation. In fact, it is quite reasonable to believe that
remittances allow rural households to migrate to urban areas (Column 7). It is also clear that
urbanization contributes to environmental degradation with greater carbon emissions (Martinez-
Zarzoso and Maruotti, 2011; Zhu et al., 2012; Sardosky, 2014). Column 8 of Table 4 indicates a
positive association between urbanization and CO2 emissions.
<table><caption>Table 4: Mechanisms results (FE-OLS estimates)</caption><thead><tr><th>VARIABLES</th><th>(1)<br>HH<br>consumption</th><th>(2)<br>CO2<br>emissions</th><th>(3)<br>Privateinvestment</th><th>(4)<br>CO2<br>emissions</th><th>(5)<br>Imports</th><th>(6)<br>CO2<br>emissions</th><th>(7)<br>Urbanization</th><th>(8)<br>CO2<br>emissions</th></tr></thead><tbody><tr><td>Remittances per capita</td><td>0.129***<br>(0.0124)</td><td></td><td>0.0311***<br>(0.00713)</td><td></td><td>0.0667***<br>(0.0107)</td><td></td><td>0.0281***<br>(0.00522)</td><td></td></tr><tr><td>HH consumption</td><td></td><td>0.512***<br>(0.0246)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Privateinvestment</td><td></td><td></td><td></td><td>0.287***<br>(0.0390)</td><td></td><td></td><td></td><td></td></tr><tr><td>Imports</td><td></td><td></td><td></td><td></td><td></td><td>0.430***<br>(0.0422)</td><td></td><td></td></tr><tr><td>Urbanization</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.782***<br>(0.0859)</td></tr><tr><td>Constant</td><td>24.34***<br>(0.0735)</td><td>11.36***<br>(0.600)</td><td>3.398***<br>(0.0304)</td><td>0.163<br>(0.131)</td><td>3.238***<br>(0.0352)</td><td>-0.273**<br>(0.139)</td><td>4.004***<br>(0.0414)</td><td>2.037***<br>(0.349)</td></tr><tr><td>Country FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Observations</td><td>948</td><td>1,061</td><td>1,053</td><td>1,189</td><td>1,083</td><td>1,225</td><td>1,083</td><td>1,225</td></tr><tr><td>R-squared</td><td>0.960</td><td>0.969</td><td>0.535</td><td>0.948</td><td>0.803</td><td>0.945</td><td>0.905</td><td>0.949</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
### 3.4. Robustness checks

We now investigate how various econometric specifications and sample data may change our estimates, which has a reflection on the robustness of the results. First, we replicate the analysis by excluding oil-producing countries, presumably the most polluting. The results are displayed in column 1 of Table 5. The estimates are unchanged. In column 2, we exclude India, the second largest emitter of CO2 in 2017. The results show a positive and significant impact of remittances on CO2 emissions.

Second, we re-estimate Eq. (1) by excluding countries that receive large amounts of remittances (with an average share of remittances in GDP above 5% and 10% respectively for the entire period examined). The results are shown in columns 3 and 4 and indicate a positive and significant association between remittances and carbon emissions.

Thirdly, it is plausible to assume that the relationship between remittances and carbon emissions is driven by country-level observable factors, like economic growth and financial development. If so, the effect we identify may be the effect of such factors omitted from Eq. (1). To look into this issue, we included GDP per capita and financial development as additional relevant control variables in our main specification. The results are shown in column 5 and support the previous estimates as well. In column 6, we also include the quadratic term of GDP per capita according to the Kuznets environmental curve. The results remain relatively stable.

Lastly, since the results discussed earlier are based on the identification using global remittances as a source of exogenous variation for remittances within each country, we use lagged remittances as instruments. In column 1, we employ as instruments the two and three period lagged values of per capita remittances. The results also demonstrate a positive effect of remittances on CO2 emissions. Subsequently, in column 2, we examine the relationship using
remittances with a lag of 4 to 5 periods as instruments. The results remain consistent<sup>7</sup>. We also examine the instruments validity using the Hansen test of overidentifying restriction (J test). In both specifications, the Hansen test indicate that we cannot reject the validity of the instruments (p-values higher than 5%). A potential problem that may arise for these estimates is that the instruments may be correlated with unobserved confounders in the second stage. We assume that this is not the case in our regressions as we include country fixed effects to capture the country-level unobservable factors that may affect CO2 emissions.

<table><caption>Table 5: Robustness estimates - IV second-stage results</caption><thead><tr><th>VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><td></td><td colspan="6" style="text-align: center;">CO2 emissions</td></tr></thead><tbody><tr><td>Remittances per capita</td><td>0.337***<br>(0.0477)</td><td>0.263***<br>(0.0302)</td><td>0.271***<br>(0.0398)</td><td>0.258***<br>(0.0299)</td><td>0.139***<br>(0.0397)</td><td>0.172***<br>(0.0438)</td></tr><tr><td>Urbanization</td><td>0.141<br>(0.0984)</td><td>0.225**<br>(0.0874)</td><td>0.150*<br>(0.0838)</td><td>0.144*<br>(0.0816)</td><td>0.0143<br>(0.0669)</td><td>0.0149<br>(0.0690)</td></tr><tr><td>Trade Openness</td><td>0.0874<br>(0.0925)</td><td>0.177***<br>(0.0522)</td><td>0.106<br>(0.0652)</td><td>0.135**<br>(0.0535)</td><td>0.0617<br>(0.0376)</td><td>0.0589<br>(0.0412)</td></tr><tr><td>Financial Development</td><td></td><td></td><td></td><td></td><td>0.0482<br>(0.0294)</td><td>0.0302<br>(0.0326)</td></tr><tr><td>GDP per capita</td><td></td><td></td><td></td><td></td><td>0.597***<br>(0.104)</td><td>1.358***<br>(0.428)</td></tr><tr><td>GDP per capita SQ</td><td></td><td></td><td></td><td></td><td></td><td>-0.0564*<br>(0.0293)</td></tr><tr><td>Constant</td><td>-0.930<br>(0.601)</td><td>-0.689**<br>(0.351)</td><td>-0.108<br>(0.375)</td><td>-0.191<br>(0.343)</td><td>-4.402***<br>(0.820)</td><td>-6.789***<br>(1.626)</td></tr><tr><td>Country FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Observations</td><td>877</td><td>1,048</td><td>849</td><td>984</td><td>1,074</td><td>1,074</td></tr><tr><td>R-squared</td><td>0.927</td><td>0.947</td><td>0.954</td><td>0.952</td><td>0.969</td><td>0.965</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

<footnote n="7">7 The first-stage results associated to these two specifications are available upon request.</footnote>
<table><caption>Table 6: Lags as instruments</caption><thead><tr><th>VARIABLES</th><th>(1)</th><th>(2)</th></tr><tr><td></td><td colspan="2">CO2 emissions</td></tr></thead><tbody><tr><td>Remittances per capita</td><td>0.132***<br>(0.0133)</td><td>0.135***<br>(0.0216)</td></tr><tr><td>Urbanization</td><td>0.244***<br>(0.0908)</td><td>0.204**<br>(0.0951)</td></tr><tr><td>Trade Openness</td><td>0.266***<br>(0.0403)</td><td>0.261***<br>(0.0441)</td></tr><tr><td>Constant</td><td>-0.960***<br>(0.341)</td><td>-0.733*<br>(0.391)</td></tr><tr><td>Country FE</td><td>YES</td><td>YES</td></tr><tr><td>First-stage F-stat</td><td>203.32</td><td>47.04</td></tr><tr><td>Hansen test, p-value</td><td>0.2693</td><td>0.6365</td></tr><tr><td>Observations</td><td>959</td><td>884</td></tr><tr><td>R-squared</td><td>0.966</td><td>0.970</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

4. Conclusions and some policy implications

The objective of this paper was to study the impact of remittances on CO2 emissions. Using historical data for a panel of developing countries we found that, in general, remittances have an adverse effect on the environment. This result seems to be robust to changes in the number of countries and to the specifications considered. Remittances are rather used in energy-intensive goods. Governments of countries of emigration should put in place incentive policies to channel remittances into the consumption of environmentally sustainable goods. Remittances have an important development potential if they are oriented towards green projects and consumption of environmental goods. In other words, the policymakers should channel remittances for appropriate use and more interestingly should focus on responsible, sustainable
and environmentally friendly financial expansion to prevent the detrimental consequences of remittances on carbon emissions. Our results also provide compelling evidence that the countries under study could gain from international trade but this depends on the level of integration in the world trade. In general, the more trade circumstances liberalize, the more are the efforts to undertake international environmental standards for international trade and competition aims (Galeotti and Lanza, 1999). The trade liberalization reinforces the need for environmental cooperation and encourages the importation of less polluted techniques to promote the clean production. Last but not least, remittances are widely perceived as one of the potential factors of financial development. Given this, government should formulate lending policy in such a way that the financial institutions should lend money to firms that effectively follow the environmentally friendly production processes. In such a context, the harmful impact of remittances can be mitigated. Moreover, government should support the financial sectors in order to encourage investing in renewable energy sectors so that dependency on adverse sources of fossil fuels will be lessened.

Overall, the results of this study are expected to be useful for providing a better understanding of the current environmental challenges and for identifying the ways in which the adverse impacts of CO2 emissions can be reduced. They may offer a new basis for discussion on the effective design and implementation of environmental and energy policies for developing countries.

Despite the relevance of our findings, this research has some limitations. Most potentially, the access to data was limited, and only annual data was available. The performed technique may offer distinct findings if the data set is expanded to reflect quarterly or monthly evolution. Also,
while this paper brings interesting insights on the effects of remittances on pollution, however,

micro-economic studies on the effects of remittances can supplement this work.
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