The authors declare that they have no conflicts of interest.
This study did not require formal ethics approval.
Data sharing is not applicable to this article.
The authors received no specific funding for this work.
The world is currently confronted with a growing challenge in the form of CO2 and climate change, which pose grave risks to human lives worldwide. This article attempts to investigate the nexus between CO2 emission, FDI, and Globalization for the period of2003 to 2018 in south Asian countries. This study enables policymakers to devise and execute policies to decrease CO2 emissions in the future. We use panel data techniques to investigate the determinants of CO2 emissions over the world. Our finding shows Globalization,GDP, CO2 emissions are influenced positively and significantly by financial development and energy consumption. Trade openness and FDI have a positive relationship with CO2 emission, While social Globalization has a negative impact on carbon emission. This articlehas key policy implications. Policymakers should formulate policies to provide incentives to their citizen to decline CO2 emissions in the world.
Background of Study
Both nationally and internationally, environmental conditions have changed more rapidly in the past half-century. Climate change is a very serious threat; it negatively impacts health, agriculture, and the overall economy of the world. If all pollution from human activities stopped immediately, the climate would still change. However, continued intense, anthropogenic pollution and gas emissions, on the other hand, will lead to global warming, natural ocean action, geological process, and over changing climate patterns. Rising CO2 levels in the air, alongside different gases, warm the planet, causing environmental change.
Carbon dioxide emission has increased during the past half-century with quick economic process and development. The nexus between carbon and globalization area unit is widely studied within the current literature below the environmental economist curve (EKC). EKC hypothesis posits that at the initial stage of the economic process, emission can will increase with financial gain. Once the economy reaches to intensity of financial gain per capita, dioxide emissions begin to say no with financial gain. There's an inverted u formed between dioxide emission and economic process.
For developing and developed countries, foreign direct investment is important. Particularly after the 1980s, in developing countries, it became interesting. In recent decades the policymaker has shown a significant interest in the economic growth in developing and developed countries. How its the potential to realize economic growth without serious pollution? However, is CO2 emission reduced in the progress of economic growth? To answer these queries, foreign direct investment and dioxide emission area unit 2 main factors interacting with economic process. Foreign direct investment has become of the most vital economic forces for the Chinese economy. In developing countries, Chinese FDI lies in polluting industries with high CO2 emissions, which come from the use of fossil fuels. For evaluating the worth of economic growth, CO2 emission should be taken into consideration. On one way, its highest potential is that use of fossils fuels have a positive impact on economic growth since fossil fuels area unit inputs for production processes.
In this article report, we investigate the relations among Globalization, foreign direct investment, and CO2 emission in South Asian countries over the period of 2003 to 2018. The nexus between Globalization, foreign direct investment, and CO2 are an ambiguous one. Some researcher shows the positive linkage between Globalization and CO2 due to climate change and economic growth, while on the other hand, some researcher and scholars have an opposing view for Globalization and CO2 emission. While in the same way, foreign direct investment also has both positive and negative nexus with CO2 emission. The analysis is executed by estimating the theoretical concept of the environmental Kuznets curve (EKC).
Statement of Research Problem
Globalization and foreign direct investment has provided enormous setup to the growth and development of economies of the world. However, the world is presently facing a mounting challenge inside the shape of environmental degradation and international temperature change, as they sit grave risks to human lives everyplace within word; furthermore, the impact of Globalization and foreign direct investment on environmental degradation cannot be overlooked. On the one hand, there is a paucity of research on the link between Globalization and environmental degradation. On the other side, Globalization is measured by trade openness, the inflow of foreign direct investment, worker remittances, and the relationship between Globalization and economic process also as environmental degradation has drawn a lot of interest in recent time because of increase within the awareness of dioxide emission and its impact on air quality. The empirical estimation of many aspects of Globalization is ignored. Further in this literature relationship between FDI and environmental degradation are mixed. it is worth considering that how the inflow of foreign direct investment and globalization influence the environmental quality, growth, and development of world economies. Thus, this article aims to report the environmental outcome of Globalization and FDI to fill the gap within the existing literature.
Research Objectives
1. To look into how Globalization affects environmental degradation.
2. To assess the effect of Chinese foreign direct investment on environmental degradation.
3. To put forward policy implications for policymakers to improve environmental degradation
Research Questions
1. Does economic growth have on environmental degradation?
2. Does Chinese FDI has an influence on environmental degradation?
Study’s Significance
The world has started to see the dramatic environmental degradation caused by human actions in the name of development. Given the limited research that examines the nexus between Globalization, Chinese foreign direct investment, and environmental degradation, this work will provide an in-depth considerate by taking a large panel of countries from around the world. In the above studies, Globalization is proxy by trade openness, inflow of foreign direct investment, and worker remittances which give an insufficient conclusion on its effect on environmental degradation. In our research report, various aspects of Globalization (economic, social, and political) will give useful perception in relationship with environmental degradation. Chinese foreign direct investment boosts the economy of the world; however, its potential effect on environmental quality is unexplored. Our funding will offer useful policy implications for the policymakers to review and minimize harmful effects of three aspects of Globalization (social, economic, political) as well as Chinese FDI on CO2 emission in the panel countries.
This chapter includes the literature review of positive aspects of globalization and CO2, negative aspects of Globalization and CO2 emissions, positive aspects of FDI and CO2, negative aspects of FDI and CO2, and the last one is a summary of the chapter.
Positive Aspect of Globalization and Co2
Cam et al. (2019) looked at the impact of Globalization on C02 emission exploitation using ARDL data from Vietnam from 1990 to 2016. They ensure that CO2 emissions, Globalization, FDI, export, coal use per capita, and fossil fuel electricity generation are all linked in the short and long run. This outcome indicates that Globalization will increase carbon emissions, whereas export will decrease them. According to this study, FDI had no effect on carbon emission.
Shahbaz et al (2017) estimate impacts of Globalization on CO2 emission for the period1970 to 2014 in japan. They used the ARDL model, which showed that the edge has both positive and negative shocks due to increased carbon emissions as a result of Globalization, whereas energy consumption has positive and carbon emission has a positive and significant effect.
Dinda (2006) used panel data to investigate the influence of globalization on CO2 emissions and revealed the effect of commerce on the climate, pollution intensity, and relative changes of pollution that occur within the world. The relationship of trade and environment is also determined by the Factor endowment and pollution haven hypothesis. Dinda found the effect of Globalization on the environment powerfully depends upon the essential attribute of the country. That results that C02 emission increases with Globalization which is the main reason of global warming.
Shahbaz et al. (2013) estimate the presence of the environment Kuznets curve for CO2 and its relationship with the economic process, energy consumption, and Globalization by using GMM estimator for 18 countries for the year of 1990 to 2010.GMM estimator explain the problem of serial correlation, heteroskedasticity, and endogeneity for some independent variables. There work result that Globalization increases production activity by utilizing domestic resources while energy consumption and CO2 emission have positive nexus. They saw that urbanization and fossil fuel byproducts have an altered U-formed association, implying that urbanization works on ecological quality by bringing down fossil fuel byproducts.
From 1975 to 2014, Khan et al (2019) researched the connection between globalization and CO2 emissions in Pakistan. The ARDL bound test and Johansen co integration were used. CO2 emissions and globalization have a long-term critical link, according to the Johansen co-integration test. According to the ARDL model, increasing globalization components (economic, political, and social globalization) by 1% will result in 0.38, 0.19, and 0.11 percent increases in CO2 emissions, respectively. They also support the inverted U-formed association that exists between globalisation and CO2 emissions.
Kalagci et al. (2018) used panel data estimates for NAFTA countries from 1990 to 2015 to capture out the influence of globalization and trade openness on CO2 emissions. According to their results, economic progress, trade openness, and CO2 emissions all have a positive relationship. Under the EKC, CO2 and economic growth have a positive linear and square relationship.
Negative Aspects of Globalization and Co2
Abbas et al. (2018) used CIPS, CADF unit root test, In the experiment of environmental factors, the Westerland cointegration test and the Dumitresca Hurlin Granger causality test were used to assess the impact of energy usage, money development, globalization, economic expansion, and urbanization on CO2 emissions for BRICS countries. They found that data cross-sectionally dependent and heterogeneous while variables are cointegrated, energy use and money development increases CO2 emission, whereas Globalization and urbanization has negative and insignificant linked with CO2 emission. There is bidirectional nexus between economic process, money development, energy use, and square of GDP with CO2 emission, while Globalization and urbanization are unidirectional with CO2 emission.
From 1985 to 2013, You et al. (2018) look at the geographical impacts of economic development on CO2 emissions in 83 developing nations. They used a spatial panel methodology to examine the issue of spatial dependency as well as the results of the comparison between neighboring countries. This shows that indirect results of economic Globalization on greenhouse gas emission are negative to beat the positive direct result that is negative and significant. EKC is inverted U formed between greenhouse gas emission and income.
For the period 1980 to 2017, Ali et al. (2019) investigate the impact of urbanization and Globalization on CO2 emissions in South Africa. They used the single structural break unit tests and the Bai and Perron multiple structural break unit test, and the ARDL cointegration test. The ARDL test result that urbanization produce CO2 emission while in the long term there is significant impact of Globalization. There is a bidirectional connection between CO2 emission and urbanisation, but no nexus between CO2 emission and globalisation, according to the Toda Yamamoto non causality test.
Positive Aspect FDI and Co2
To assess the influence of FDI on carbon intensity, Shao et al. (2017) analysed panel data from 188 countries from 1990 to 2013. According to the GMM estimator, FDI has a negative and substantial influence on carbon intensity, but urbanisation, industrial intensity, and trade openness all have positive and significant impacts on CO2. FDI has a favourable and considerable impact on carbon intensity in high-, middle-, and low-income nations.
The GMM estimator was used by Shahbaz et al. (2019) to decide the link between foreign direct investment and CO2 emissions in the Middle East and North Africa from 1990 to 2015. FDI and CO2 have an N-shaped association, but economic growth and CO2 emissions have an N-shaped and inverted relationship. They employed biomass energy, which resulted in a reduction in carbon emissions. CO2 emissions and biomass energy usage are linked in both directions.
Cong et al. (2019) use victimisation panel data from 19 Asian developing countries from 2002 to 2015 to evaluate how FDI affects air pollution and how institutional quality influences these adverse effects. As a result, FDI inflows increase air pollution at first, while institutional quality improvement aids in air pollution reduction. They also discovered that when institutional quality is considered, the pollution haven and pollution halo hypotheses do not emerge to be reciprocally exclusive.
Kim (2019) looks into the relationship between CO2 emissions, energy use, domestic income, and foreign direct investment in 57 developing nations between 1980 and 2013. The vector error correction model demonstrates that there is no short-run link between FDI and CO2 emissions. While the elasticity of FDI on CO2 emission is modest and statistically significant, this article shows that there is a long-term co-integrated relationship between CO2 emissions, energy use, domestic income, and FDI, which supports the EKC hypothesis.
Negative Aspect FDI and Co2
Shari et al. (2014) optimize panel information for the period of 1992 to 2012 for fifteen developing countries to work out the impact of FDI and economic
process on carbon emission. They used the Johanson cointegration technique to work out the nexus between FDI, economical process, and carbon dioxide emission. FMOLS result that in the long haul, FDI doesn't have any impact on carbon dioxide emission, whereas the economic process has positive nexus with carbon dioxide emission. According to Granger causality estimates, FDI and GDP have no short-term impact on CO2 emissions.
Cam et al. (2019) looked at the impact of Globalization on C02 emission exploitation using ARDL data from Vietnam from 1990 to 2016. They ensure that CO2 emissions, Globalization, FDI, export, coal use per capita, and fossil fuel electricity generation are all linked in the short and long term. This outcome indicates that Globalization will increase carbon emissions, whereas export will decrease them. According to this study, FDI had no effect on carbon emissions.
Zafer et al (2016) employ the cointegration approach and Granger causality analysis to investigate the nexus among energy usage, wages, FDI inflows, and CO2 emissions in Turkey from 1974 to 2014. According to the cointegration results, the pollution halo hypothesis (FDI) has a beneficial influence on the climate in the short term; however, in the long run, there is a bidirectional causation link between FDI inflows and CO2 emissions, as well as a negative coefficient of FDI. The Granger causality test has been applied to explain the unilateral causation nexus between energy usage and economic growth.
Nuno (2018) used ARMIA model, OLS, ARCH regression, VAR, Granger causality for the years 1980 to 2013 for Portugal to study climate change. The impact of income per capita and CO2 emission have positive nexus and negative nexus between square income of per capita on carbon emission. This paper also explained that trade openness and FDI have a negative relationship with CO2 emission.
The model specification and econometrics methods used for empirical analysis are discussed in this chapter. Following that, we presented our research report, which included model selection, data source descriptions, and variables used.
Model Specification
To estimate results, the following model will be used:
0CO2it=?0+ ?1xit + ?2zit+?t +?i+vit
Where 0CO2it denotes carbon emissions in the country I at time "t," and it denotes the dunning's two variables for 0CO2. Zit displays a set of control variables (financial development, energy consumption, GDP, trade openness, and urban population). ?i depicts country effects that are not observed but persist over time. ?t is the unobserved amount of impact that is the same across countries, whereas it is the part whose square measure varies across countries and time.
Based on the above-mentioned theoretical framework and the structure of Pakistan and South Asian countries,
For analyzing the impact of globalisation and Chinese FDI on CO2 emissions, we used the model below.
Ln0CO2=?0+ ?1 ln(FDI) + ?2 ln(economic globalization) + ?3 ln(social globalization) + ?4 ln(political globalization ) + ?5 ln( trade openness) + ?6 ln( urban population) + ?7 ln(GDP) + ?8 ln(energy use) + ?9 ln(financial advancement )……..(1)
Where
0CO2denotes CO2 emission in metric tons per capita
FDI stands for foreign direct investment from China.
Globalization denotes economic, social, political Globalization
Openness of trade in the host country (trade as a percentage of GDP)
Urban population the proxy use is (annual %)
GDP stands for Gross Domestic Product (constant=2010).
Energy use the proxy use is (kg of oil equivalent per capita)
Financial advancement Domestic credit provided by banks to the local sector serves as a proxy (trade percent of GDP),
Theoretical Framework
In the field of social sciences research panel, data technique and methodology are vastly popular form which is used for longitudinal data analysis. In the panel data method, entities of country, firms, or group of peoples are analyzed cross-sections wise and periodically over the specific time span. Panel data allows you to control for variables you cannot observe (culture factors). The ability to overcome the problem of heterogeneity, i.e., to regulate unobserved individual or time-specific heterogeneity, is one of the most valuable benefits of using panel data. (Hausman and Taylor, 1981). When the time series and cross-sections dimensions are combined, the data's standard and quantity can be improved in ways that would be impossible if only one of these two dimensions was used (Gujarati, 2003). Panel data technique has several advantages and benefits. The estimated parameters provide more information, accuracy, and precision with less chance of collinearity between the variables (Hsiao,2003, Baltagi, 2008, Greene,2005)
Globalization has facilitated a deeper integration of developing and developed countries around the world by encouraging investment in new technologies and innovations. Globalization would modify each group of economies to grow however a high price to a natural setting.
For developing and developed countries, foreign direct investment are important. Especially after the 1980s, in developing countries, it became important. In recent decades the policymaker has shown a major interest to the economic growth in developing and developed countries. How its possible to gain economic growth without the serious a threat of pollution? How co2 emissions can be a decline in the progress of economic growth? To answer these questions, foreign direct investment and dioxide emission area unit 2 main factors interacting with economic process. On one way its highly possible that the use of fossils fuels have a positive impact on economic growth since fossil fuels are inputs for production output. China has been the largest destination for foreign direct investment among all developing countries for a number of years. Chinese foreign direct investment boost the economy of the world; however, its potential effect on environmental quality is unexplored. The relationship between FDI and CO2 emission are studied by pollution halo and pollution haven hypothesis.
The panel data techniques/methods are used in this article report to try to determine the impact of globalization and Chinese FDI on CO2 emissions. Panel data models can be used in three ways: (a) Common constant (b) Fixed effects (c) Random effects. To make compression between random and fixed effects that which method is best. Thus, we run the Hausman test, if the value of the probability is less than 0.5 percent we use fixed-effect method.
Pooled regression has many limitations. One limitation is that it assumes homogeneity for all countries that doesn't allow management of the consequences of the particular country. The correlation between independent variables and unobservable shocks may lead to bias estimates (Cheng and wall, 1999, Bevan and Danbolt, 2004).
Variables Study
In this research report we use different variables. Carbon dioxide is our predicated variable; however, the proxies used for carbon dioxide is carbon emission (in metric tons per capita), and data are collected from WDI. The explanatory or control variable are FDI, financial advancement, Globalization, GDP, Openness to trade, energy use, and the urban population WORLD DEVELOPMENT INDICATORS provides annual data on financial development, GDP, trade openness, energy use, and urban population (WDI).
Globalization
In this study, we are looking at globalisation as an independent variable. The economic, social, and political aspects of globalisation are all important. The KOF Index of Globalization was used to collect data.
Trade and financial globalization are examples of economic globalisation proxies.
Interpersonal, informational, and cultural globalization are social globalisation proxies. Political globalization functions as a proxy for political globalisation.
Carbon Dioxide
Carbon dioxide emission is used as dependent variable.. The proxies used for C02 is carbon emission (in metric tons per capita), and data are collected from WDI.
FDI
Our independent variable is FDI stock from Chinese companies. The "2012 statistical bulletin of China's outward Foreign direct investment" was used to compile the annual data on Chinese FDI stock.
Financial Advancement
The availability of credit for investment in the host country is represented by financial development. Domestic credit to the local sector by banks (trade percent of GDP) is used as a proxy, and data is gathered from the World Development Indicators.
Trade Openness
International trade provides a path to the global market through economies of scale; thus, international trade provides opportunities for foreign investors. The nexus among trade openness and CO2 emissions is undeniably of great concern to economists. The host country's trade openness proxy (trade as a percentage of GDP) is used, and data is gathered from the WDI.
GDP
GDP is our independent variable. Countries which have high GDP per capita are considered more developed. Some researcher found unidirectional nexus between GDP and CO2 emission. GDP proxy is GDP of the country (constant=2010), and data are collected form WDI
Urban Population
Urban population is our independent variable. The urban population is the population inhabiting areas that have a larger population volume than rural areas. It is the population living in cities. The proxy use is (annual %), and data are collected from WDI.
Energy use
Energy use is our independent variable. Energy use and CO2 emission have positive and significant relationship. Energy use is a major contributor to CO2 emissions. The proxy use is (kg of oil equivalent per capita) and data are collected form WDI
Table 2 displays the descriptive statistics for the
selected data set. The existence and distribution of data can be revealed using descriptive statistics. Variables should be included in regressions if the standard deviation value is positive. The descriptive statistics explain the mean value, standard deviation, min, and maximum value.
| | | | | | |
| lnco2 | 84 | -.6063933 | 1.058527 | -3.259436 | 1.121147 |
| lnfd | 109 | 3.346659 | .7424752 | 1.182563 | 4.464143 |
| lneu | 64 | 5.997152 | .413746 | 5.074517 | 6.793564 |
| LDP | 112 | 7.179326 | .9012436 | 5.303793 | 8.991354 |
| lnup | 112 | .9865353 | .6785241 | -3.066444 | 1.916329 |
| lneg | 105 | 3.667446 | .2616309 | 3.054001 | 4.135099 |
| lntg | 105 | 3.707323 | .2330212 | 3.015535 | 4.266396 |
| lnsg | 105 | 3.734196 | .3072252 | 2.309403 | 4.242764 |
| lnpg | 105 | 4.041614 | .5442473 | 2.694627 | 4.532599 |
| India | 102 | 4.606409 | 2.476537 | -.9162907 | 8.650996 |
Table 3 shows the observed covariance results for the selected data set. It's worth noting that the highest correlation between economic globalisation and other variables is 0.881, which is statistically significant at the 5% level of significance. Despite the fact that the correlation matrix shows a significant relationship between some variables, none of these correlations is strong enough to cause the multicollinearity problem.
| | | | | | | | | | | |
| lnco2 | 1.0000 | |||||||||
| lnfd | -0.0719 | 1.0000 | ||||||||
| lneu | 0.5604 | 0.0445 | 1.0000 | |||||||
| LDP | 0.6443 | -0.0473 | 0.5487 | 1.0000 | ||||||
| lnup | -0.2219 | -0.0031 | -0.4518 | -0.7905 | 1.0000 | |||||
| lneg | 0.7879 | -0.0419 | 0.5924 | 0.8804 | -0.6043 | 1.0000 | ||||
| lntg | 0.6997 | 0.1337 | 0.6440 | 0.7925 | -0.5455 | 0.9142 | 1.0000 | |||
| lnsg | 0.3945 | 0.3385 | 0.4068 | 0.7000 | -0.5751 | 0.5763 | 0.7325 | 1.0000 | ||
| lnpg | 0.9585 | -0.1070 | 0.4782 | 0.4537 | -0.0156 | 0.6454 | 0.5575 | 0.2321 | 1.0000 | |
| lnfdi | 0.6159 | -0.1233 | 0.3479 | 0.2738 | 0.0287 | 0.3166 | 0.3677 | 0.4148 | 0.6286 | 1.0000 |
The Variance inflation test (VIF) for the selected data set is shown in Table 4 The correlation test is supported by the variance inflation test in the table above. The correlation between one independent variable and other independent variables is explained by the variance inflation test. The 1/tolerance variance inflation factor is always greater than or equal to 1. According to (Neter, Wasserman & Kutner, 1985), the threshold level for multicollinearity is 10 and the value of VIF exceeding from 10 is regarded as a multicollinearity problem. The VIF results in Table 4.3 show that the highest value is 8.164 which is well below the threshold level. This suggests that our regression results do not suffer from the problem of multicollinearity. Furthermore, cluster standard errors that are correlated at the country level are used to overcome the possibility of serial correlation in our static analysis. These techniques minimize error and bias in our model as much as possible (Mottaleb & Kalirajan, 2010).
| | | |
|---|---|---|
| lneg | 7.22 | 0.036731 |
| lntg | 8.64 | 0.053642 |
| lngdp | 3.89 | 0.072004 |
| lnsg | 8.17 | 0.122332 |
| lnup | 5.16 | 0.193732 |
| lnpg | 5.18 | 0.196194 |
| lnfdi | 3.09 | 0.323359 |
| lneu | 2.39 | 0.419118 |
| lnfd | 1.76 | 0.563674 |
The selection of 2003 to 2018 as the time period for analysis is appropriate and justified. CO2 emissions have been steadily increasing since the early 2000s, and this trend is expected to continue; thus, an examination of CO2 emissions is appropriate for those years.. The independent variables in the panel analysis model show little variation over time because the data contains few entities and few periods. The Hausman (1978) specification test is used to determine whether or not the model has fixed and random effects. This test shows that the random effect is better than the fixed-effect model, with a P-value of 0.1, indicating that individual effects are uncorrelated with regressors. The POLS results are presented in Table 5 for comparison, with pooled OLS assuming homogeneity for all countries.
| | | | | | | |
| lnfd | .0968651 | .0661609 | 1.46 | 0.149 | -.0360229 | .2297532 |
| lneu | .0150451 | .0674213 | 0.22 | 0.824 | -.1203746 | .1504648 |
| lngdp | .2929257 | .1129377 | 2.59 | 0.012 | .0660836 | .5197677 |
| lnup | .0056217 | .0490945 | 0.11 | 0.909 | -.0929875 | .104231 |
| lneg | .3689204 | .367312 | 1.00 | 0.320 | -.3688476 | 1.106688 |
| lntg | .0169995 | .2856569 | 0.06 | 0.953 | -.5567594 | .5907583 |
| lnsg | -.1100324 | .2049445 | -0.54 | 0.594 | -.5216756 | .3016108 |
| lnpg | 4.679793 | .2946236 | 15.88 | 0.000 | 4.088024 | 5.271562 |
| lnfdi | .0217651 | .0136318 | 1.60 | 0.117 | -.0056151 | .0491453 |
| _coms | -24.499 | .9968972 | -24.58 | 0.000 | -26.50133 | -22.49668 |
We considered a random effect over a fixed effect based on the Hausman test while p-value (0.1). This specific test with p-value (0.1) shows that random effects are a better choice than fixed effects, indicating that individual effects are uncorrelated with repressors, as shown in table 6. if there is 1% increase in GDP so carbon emission will increase by 0.292%. Among the three proxies used for Globalization, economic aspects produce a significant and positive nexus with carbon emission. 1% rise in economic Globalization enhances CO2 emissions by 0.368% and social Globalization, enhances CO2 emissions by decreases by -0.32%, respectively. Among the other variable of interest is Chinese FDI which produces positive and significant association with carbon emission. 1% surge in China's FDI increases CO2 emissions by 0.096%. We find that the urban population has a positive relationship with carbon emission if there increase in the urban population so carbon emission will increase by 0 .005%. Trade openness have also positive relationship with carbon emission, if there is 1% rise in trade openness it will increases by 0.016
| | | | | | | |
|---|---|---|---|---|---|---|
| lnfd | .0968651 | .0661609 | 1.46 | 0.143 | -.0328078 | .2265381 |
| lneu | .0150451 | .0674213 | 0.22 | 0.823 | -.1170983 | .1471884 |
| Ingdp | .2929257 | .1129377 | 2.59 | 0.009 | .0715719 | .5142794 |
| lnup | .0056217 | .0490945 | 0.11 | 0.909 | -.0906017 | .1018452 |
| lneg | .3689204 | .367312 | 1.00 | 0.315 | -.350998 | 1.088839 |
| lntg | .0169995 | .2856569 | 0.06 | 0.953 | -.5428778 | .5768768 |
| lnsg | -.1100324 | .2049445 | -0.54 | 0.591 | -.5117163 | .2916515 |
| lrpg | 4.679793 | .2946236 | 15.88 | 0.000 | 4.102342 | 5.257245 |
| Infdi | .0217651 | .0136318 | 1.60 | 0.110 | -.0049527 | .0484828 |
| cons | -24.499 | .9968972 | 24.58 | 0.000 | -26.45289 | -22.54512 |
| Sigma_u | 0 | fraction of variance due to u_i | ||||
| Sigma_e | .09913782 | |||||
| rho | 0 | |||||
Conclusion
The world is currently confronted with a growing challenge in the form of CO2 and climate change, which pose grave risks to human lives worldwide. CO2 emissions are rapidly increasing around the world. China is primarily responsible for CO2 emissions. Using panel data from south Asian countries from 2003 to 2018, this paper empirically determines the determinants and pattern of CO2 emissions. Stylized macroeconomic variables such as GDP, trade openness, FDI, Globalization, urban population, energy use, and financial development were used to determine the effects of CO2 emissions in these countries. CO2 emissions have a positive and significant relationship with GDP and financial development; as GDP rises, so do CO2 emissions. On a similar direction, urbanization and energy use have also positive relationship with CO2 emissions. These variables led to a high rapid increase in CO2 emissions, however. These findings corroborate previous research.
Policy Implications
This study offers the following policy implications based on our findings.
1. Economic growth and Globalization, in general, will increase environmental issues and grow rapidly with energy consumption. Such industrialization and development process hugely contribute to CO2 emission. The government should enact laws, regulations, and fiscal policies to encourage energy efficiency and utilizing renewable energy sources
2. Chinese FDI has a positive influence on the environment, which authenticates the "pollution halo hypothesis". The host countries must strive to attract further Chinese FDI i.e. exchange of green/clean technology for their development and growth as it is less harmful to environmental quality.
The authors declare that they have no conflicts of interest.
This study did not require formal ethics approval.
Data sharing is not applicable to this article.