1
The Economics of Carbon Emission Mitigation:
New Variable, New Hypothesis and Revisiting of the Pollution Haven Hypothesis Abstract
Using panel data for 12 emerging markets from 2004 to 2019, we develop and test a new hypothesis about carbon emission (CE) mitigation being an investment opportunity as per the heterodox approach rather than a cost as per the orthodox approach. We also revisit the validity of pollution haven hypothesis (PHH) by augmenting the equation with the new variable, energy transition investments (ETIs).
Moreover, unlike previous research, we differentiate between the direct and indirect effects of FDI, and to measure economic activity, we use total factor productivity (TFP) instead of GDP. The dynamic heterogeneous estimation technique is specified and estimated. Our results validate the ETI-augmented PHH and highlight the indirect effect of FDI on CE through the TFP, ETIs, and energy consumption channels. Furthermore, ETIs are found to have a very significant, albeit small, carbon suppressive effect suggesting the current ETIs' levels are not yet sufficient to secure climate change mitigation in emerging markets. We also find support for our newly developed hypothesis. An important policy implication is that if the identified ETIs patterns were to continue, mitigation would be intensified in emerging markets, offering the opportunity to steer the energy sector onto a more sustainable path.
Highlights
We develop and test new hypothesis on mitigation being an investment opportunity rather than a cost for a sample of emerging countries.
We revisit the PHH by augmenting the equation with the energy transition investment (ETI) variable, and unlike previous research we use total factor productivity (TFP) instead of GDP and differentiate between direct & indirect effects of FDIs.
We found ETIs to affect economic activity positively and negatively on carbon emission (CE), although ETIs' effect is limited due to low levels of ETIs.
The augmented PHH is validated.
If ETIs patterns discovered in this study were to continue, mitigation would be intensified, and ETIs' negative effect on CE may eventually surpass FDI's positive impact on CE.
Keywords
Carbon Emission Mitigation; Energy Transition Investment; Total Factor Productivity; Foreign Direct Investment; Energy Consumption; PHH; SDG7
Abbreviations
CE: carbon emission EC: energy consumption EMs: emerging markets
ETI: energy transition investment FDI: foreign direct investment GDP: gross domestic production GND: green new deal
PHH: pollution haven hypothesis RE: renewable energy
R&D: research and development TFP: total factor productivity
2 1.
Introduction
Global climate change is mainly driven by carbon emission (CE) coming from the combustion of fossil fuels [1]. As the energy sector is by far the largest source of the CE, the energy transition is viewed as the shortest pathway to a zero-emission energy system [2]. Recognizing this fact, and while there has been a long history of economists analyzing factors, processes, and strategies that affect CE mitigation, a long-standing debate persists over the effectiveness of mechanisms intended to ensure mitigation and help achieve the seventh sustainable development goal (SDG7).
Overall, the economic literature on CE mitigation can be classified into two categories. The first category, qualified as orthodox, sees and promotes economic growth as a permanent feature of the economy. It suggests ecological modernization solutions based on market transformation and green growth [3,4] to decouple economic growth from environmental degradation through a range of market- based instruments (MBIs)1 [5,6]. However, a result of this orthodox approach's framing of mitigation as a cost rather than a transformational opportunity [7] fostered resistance to international negotiation on CE mitigation2. The second category, qualified as heterodox, criticizes the unrealistic assumption of 'Homoeconomicus' upon which the orthodox approach is built and frames mitigation as an investment opportunity often dubbed as Green New Deal (GND)3&4.
From the above discussion, a hypothesis that comes out of this heterodox approach is that we would expect the growing trends of investments in the energy transition to affect economic growth positively and negatively affect CE. Thus, in contribution to the orthodox-heterodox debate and using ETIs as a proxy to represent the investment opportunities, we study their double effects on CE and economic activity. More specifically, in this paper, our first objective is to test our newly developed hypothesis in the context of emerging markets (EMs). The choice of EMs as a testing ground is informed by the fact that they are responsible for the lion's share of CE worldwide [8] after experiencing significant increases in inward FDIs related to delocalization.
Furthermore, stylized facts show that EMs have also been experiencing important ETIs. This can be seen in Table 1, which shows the distribution of new ETIs across the world, developed countries and developing countries. Since 2015, ETIs in developing countries have exceeded those in developed countries [9], while their total in developed countries has decreased over the 2015-2020 period, after a decennium (2004-2014) of high ETIs. Furthermore, while ETIs' annual mean in developed countries has gone down over the 2004-2019 period, emerging and developing countries have experienced a more than threefold increase in their annual average ETIs, going from 57.87 during the 2004-2014 period to 193.58 during the 2015-1019 period.
Table 1: Energy transition investments in B$, 2004-2019
World Developed countries Emerging & developing countries 2004-2014 2015-2019 2004-2014 2015-2019 2004-2014 2015-2019
ETI (inflow in B$) 2126.4 1613.5 1489.8 645.6 636.6 967.9
ETI Mean value in B$) 193.3 322.7 152.7 129.12 57.87 193.58
Source: Data sourced from BNEF with authors' calculations
EMs have also been experiencing increasing FDI inflows that helped increase their economic growth and CE, measured in CO2 emissions per capita. As their CE increases, EMs face tremendous pressure from the international community to accelerate the mitigation process [7]. However, another well- established aspect of FDIs inflows into EMs is the technology they bring to host countries [10–13]. To
1 MBIs are defined by OECD (2007) as instruments that "seek to address the market failure of 'environmental externalities' either by incorporating the external cost of production or consumption activities through taxes or charges on processes or products or by creating property rights and facilitating the establishment of a proxy market for the use of environmental services."
2 The withdrawal of the USA, the first CO2 emitter, from the Paris agreement is used as a perfect example of this resistance (Piana, 2010).
3 A GND is a new paradigm that is a systematic, comprehensive, and integrated response framework to the social, economic, and environmental crises. Investments span all areas, including renewable energy sources, zero-emission transportation systems, and sustainable agriculture.
4It is worth noting that ecological economics puts a lot of emphasis on the financial system and its role as a key player in funding GND-type of investments and thus facilitating the transition to the intended zero-carbon economy (Bernardo &
Campiglio, 2014; Daly, 2014; Rezai & Stagl, 2016). For example, Campiglio (2016), Jackson (2009) and Rezai & Stagl (2016) pointed to the importance of harnessing finance for sustainability by directing the volume and composition of investment to address the financing gap for low-carbon technologies.
3
study the effects of the above-noted changes, we use the pollution haven hypothesis (PHH) framework, considered one of the most influential and persuasive theories in explaining the FDI-environment nexus.
PHH argues that new investment projects in advanced countries, which are restricted for environmental reasons, look for opportunities in developing countries with lax ecological policies [14]. This shifts the international production allocation from developed countries (with high-pollution levels) to developing countries (with low-pollution levels) with the dual effect of promoting industrialization in the host countries and using more efficient global available environmental resources. The PHH is also explained by the fact that environmental factors are considered factors of production. So production cost increases due to the stringency of environmental regulation in developed countries [15]. Accordingly, countries with lax environmental laws will have a comparative advantage in attracting more FDIs and with the possibility of declining environmental sustainability [16]; what [12] Zarsky (1999) described as "a race to the bottom" in environmental standards.
Empirically, however, and despite the large body of research testing the validity of PHH, there still is no agreement on whether FDIs are to blame for the rising CO2 levels in the EMs. So while some studies have validated the PHH in some EMs, such as [17] that found a positive impact of FDI-attributable climate change in Sub-Saharan Africa; [18] and [19] that reported strong PHH evidence for China; [20], which found a positive relation between FDI, energy consumption (EC) and CE in the BRICS; and [21]
that studied 17 countries in the southern and southern east of Asia, other studies have failed to validate the hypothesis including [22] that tested 5 ASEAN countries; [23] that studied China; and [24], which used GCC sample. A more appropriate model is required to investigate the validity of the PHH. Our second objective is to test the PHH by augmenting the model with a new variable that has the potential to affect the impact of FDI on the environment in the context of EMs.
The theoretical literature provides general support for the positive effect of ETIs on climate change mitigation. For example [25] and [26] highlight an interaction effect between the GND and climate change mitigation. However, we are unaware of any study that augments the PHH equation by the ETI variable to consider the simultaneous impacts of FDI and ETI on economic activity, EC and consequently CE. It is worth stressing that it makes more sense to augment the PHH equation with ETIs than with renewable energy (RE) alone, as done previously by studies such as [27]. This is because ETIs include investment figures for a more comprehensive range of transition areas that contribute to mitigation instead of renewables alone. ETIs comprise energy storage, carbon capture and storage, hydrogen, and electrified vehicles and heating [8].
Our third objective, and unlike previous studies, is to use total factor productivity (TFP) as an economic growth measure instead of GDP to estimate the PHH model. This is because TFP has a relationship with energy [28] and considers efficiency in the use of energy, technology, and resources. Furthermore, according to [29], growth economists should concentrate on TFP and its drivers instead of factor accumulation. Finally, in the case of EMs, using TFP implies a better measure of changes in world technology and the opportunities offered by globalization.
Putting together the different strands of literature thus far in a schematic form, we get Figure 1, which depicts the traditional FDI-environment nexus augmented by the new ETI variable and replacing GDP with TFP. While there have been studies investigating the impact of FDIs on CE, little to nothing has been done on exploring the existence of the indirect effects separately from the direct ones [30]. The failure to consider the indirect effects may result in under/over estimating FDIs' impact on CE. As a result, our fourth objective is to test for the existence of FDIs' indirect effects on CE to avoid misestimating their actual impact on CE. Figure 1 shows how FDIs indirectly affect CE in at least two different ways. Firstly, an increase in FDI positively affects TFP, thanks to more efficient use of energy and other factors of production, and consequently, it reduces CE [28]. Secondly, FDI has a negative indirect effect on CE via the ETI channel. With spillovers from R&D and innovations accompanying FDIs, the latter leads to a decline in costs of RE assets [9]. This, coupled with the increase in the consequent viability of RE projects [31], motivates an increase in ETIs, which would lead to an accelerating transition and an increase in the deployment of clean and energy-efficient technologies and an overall reduction in CE [32].
4
Figure 1: FDI effects on CE in an ETI changing landscape
This paper aims to understand the FDI, TFP, ETI, EC, and CE nexus in 12 emerging countries. Using the dynamic heterogeneous estimation technique, these relationships will be tested within the PHH framework. The analysis confirms the cointegration relationship, validates the PHH in the sample countries although, and due to time lag effect, the impact of FDI on economic growth and the corresponding increase in CE is not immediate. Furthermore, due to the efficient use of energy resources, TFP is found to play a determinant role in reducing EC. However, reducing EC, using MBIs as recommended by the orthodox approach to climate change, can negatively disrupt economic growth and discourage firms from innovating. Our recommendation is to further increase ETIs, in line with the heterodox view of mitigation as an opportunity. This would replace the dirty energy with RE without decreasing EC, and hence double objectives of economic growth and CE mitigation are met.
Regarding ETI, we found its effect on CE to be negative but limited. This limited effect is due to, among other things, the low level of ETIs that would allow for environmental protection in the long run. We also found ETI's impact on TFP to be significant in the long run, and we expect this trend to increase.
This is because as R&D in RE increases, the viability (costs) of RE projects assets increases (decreases).
This, in turn, encourages more ETIs in emerging countries. Overall, and considering the positive role of ETI on CE mitigation, our recommendation for EMs is to promote further transition investments exploiting the declining costs and the increasing performance of renewables opportunities. Promotion can be active mobilization of energy transition finance locally and globally.
Finally, another noteworthy finding of our study is that if the ETI patterns revealed in this work continue, mitigation will be stepped up, and ETIs' negative effect on CE may eventually surpass FDI's positive impact on CE.
The remainder of the paper is organized as follows. Section 2 presents the data, methodology, and details about the econometric modelling. Section 3 reports the results, and finally, section 4 presents the discussions, policy recommendations, and future studies.
2. Dataset description and empirical methodology 2.1 Data
All data used in this study are annual observations obtained from many sources, as detailed in Table 2 (below). The period covered is from 2004 to 2019, which was imposed by data availability, particularly ETIs. The observations are for a panel of 12 emerging countries, for which data is available, with different levels of energy transition readiness [33]. These countries are China, India, Brazil, Mexico, Chile, Peru, Malaysia, Thailand, Indonesia, Poland, Philippines, and Hungary.
Our dependent variable, the CE, is used as an environmental indicator and proxy for climate change.
FDI measures the direct investment from cross-border, re-investment of earnings, equity capital, and other capital [34]. FDI is a valuable indicator for assessing technological transfer, tracking the implementation of the Sustainable Development Goals, private sector growth, and investment climate, especially in developing countries [17]. EC comprises commercially traded fuels, including modern renewables used to generate electricity [35]. ETIs include investments in projects such as energy storage,
5
renewable power, hydrogen production, Electric Vehicle (EV) charging infrastructure, Carbon capture and storage projects and end-user purchases of low-carbon energy devices, like small scale solar systems, heat pumps, and zero-emission vehicles [9]. ETI5 is expected to affect environmental degradation negatively.
Table 2: Variables used in the study:
Variable Meaning Unit Source
CE Carbon emissions per capita metric tons per capita EDGAR database
TFP Total factor productivity6 Constant national price USA Federal Reserve Economic Database FDI Foreign direct investment inflows in % GDP % GDP World Bank
EC Primary Energy consumption per capita Gigajoule per capita Bp-statistics Database
ETI Energy transition investment7 flow in %
GDP % GDP BloombergNEF and authors calculation
GDP Gross domestic product per capita Current US$ per capita World Bank
2.2 Model and empirical methodology
In this paper, the empirical investigation tests the relationship between CE, the dependent variable, and TFP, FDI, EC, and ETIs, as the independent variables. Because of the expected time lag effect from ETIs and FDI to CE and economic growth variables, the relationships need to be tested by distinguishing between the short and long terms8.
The Hausman test is used to choose between the pooled mean group (PMG) and the mean group (MG) estimators. The results show that the null hypothesis is accepted, so PMG is favoured over MG. As per [36,37], the panel ARDL is expressed as follows:
ΔCEit = αi + β1iCEit− 1 + β2iTFPit− 1 + β3i ETIit− 1 + β4iFDIit− 1 + β5iECit− 1 + ∑𝑝−1𝑗=1δ1jΔCEit−j +
∑𝑞−1𝑗=0δ2jΔTFPit−j + ∑𝑞−1𝑗=0δ3jΔETIit−j +∑𝑞−1𝑗=0δ4jΔ𝐹𝐷𝐼it−j + ∑𝑞−1𝑗=0δ5jΔ𝐸𝐶it−j+εit, Eq. (1)
ΔTFPit = αi + β1iTFPit− 1 + β2iCEit− 1 + β3i ETIit− 1 + β4iFDIit− 1 + β5iECit− 1 + ∑𝑝−1𝑗=0δ1jΔTFPit−j +
∑𝑞−1𝑗=0δ2jΔ𝐶𝐸it−j + ∑𝑞−1𝑗=0δ3jΔETIit−j+ ∑𝑞−1𝑗=0δ4jΔ𝐹𝐷𝐼it−j + ∑𝑞−1𝑗=0δ5jΔ𝐸𝐶it−j +εit, Eq. (2)
Δit ECit = αi + β1iECit− 1 + β2iTFPit− 1 + β3i ETIit− 1 + β4iFDIit− 1 + β5iCEit− 1 + ∑𝑝−1𝑗=1δ1jΔECit−j
+∑𝑞−1𝑗=0δ2jΔ𝑇𝐹𝑃it−j + ∑𝑞−1𝑗=0δ3jΔETIit−j+ ∑𝑞−1𝑗=0δ4jΔ𝐹𝐷𝐼it−j + ∑𝑞−1𝑗=0δ5jΔ𝐶𝐸it−j +εit, Eq. (3)
ΔFDIit = αi + β1iFDIit− 1 + β2iTFPit− 1 + β3i ETIit− 1 + β4iCEit− 1 + β5iECit− 1 +∑𝑝−1𝑗=1δ1jΔFDIit−j
+∑𝑞−1𝑗=0δ2jΔ𝑇𝐹𝑃it−j + ∑𝑞−1𝑗=0δ3jΔETIit−j+ ∑𝑞−1𝑗=0δ4jΔ𝐶𝐸it−j + ∑𝑞−1𝑗=0δ5jΔ𝐸𝐶it−j +εit, Eq.(4)
5We calculated the time series of the ETI flow variable by cumulating the observations of the stock variable ETI with the initial observation of ETI for 2004 being close to zero. This is because data is available only for 2004 onwards. However this shouldn’t affect much our estimations as ETIs prior to 2004 are negligible in the emerging markets. Furthermore, ETIs are capital investments and the ETI flow variable is divided by GDP. 𝐸𝑇𝐼𝑡= ∑𝑡 𝑒𝑡𝑖𝑖
𝑖=2004 with 𝑒𝑡𝑖𝑖 is ETI stock in the year 𝑖 and
6 TFP is the Hicks natural TFP.
7Although some ETIs are included in FDIs, the amount is negligible so there is no issue of double accounting. Also, as per the correlation analysis below, the between FDI and ETI is very negligible.
8 Although CE is imputed from EC, we regress CE on the EC and EC on CE. This is because the energy use variable includes both RE and dirty energy and our panel is constituted of a heterogeneous sample of countries in terms of level of energy transition readiness (ETR) (World Economic Forum, 2020). In one hand as CE increases in the emerging markets, which are mostly energy deficient, we expect countries with good ETR to try to reduce CE by increasing the use of RE. in this case RE is used as a complementary to, and not a substitute to, the non-renewable energy. However, and as per the pollution heaven hypothesis, we expect emerging markets with low ETR and high CE to attract pollutant investments projects, which are high- energy users and consequently their CE increases. This overall explanation is consistent with previous empirical studies including Asungo et al (2016, P. 6569), which found a substantial evidence that the variable CE has long run effect on EC, and also observed the reciprocal path with as much evidence for a sample of 24 African countries. Our explanation for investigating.
6
ΔETIit = αi +β1iETIit− 1 +β2iTFPit− 1 +β3i CEit− 1 +β4iFDIit− 1 + β5iECit− 1 + ∑𝑝−1𝑗=1δ1jΔETIit−j
+∑𝑞−1𝑗=0δ2jΔ𝑇𝐹𝑃it−j + ∑𝑞−1𝑗=0δ3jΔ𝐶𝐸it−j+ ∑𝑞−1𝑗=0δ4jΔ𝐹𝐷𝐼it−j + ∑𝑞−1𝑗=0δ5jΔ𝐸𝐶it−j +εit, Eq.(5)
The level terms reflect long-run dynamics, while the first-difference terms reflect short-run effects. The error term is represented by εit, and Δ represents the first-difference operator. "p" is the lag for the dependent variable, and "q" is the lag for regressors. The number of cross-section countries is N=12, and the number of years is T= 16.
Furthermore, the model can be specified as an error correction model, yielding the following:
ΔCEit = γi +∑𝑝−1𝑗=1γ1𝑖𝑗ΔCE𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ2𝑖𝑗ΔTFP𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ3𝑖𝑗ΔETI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ4𝑖𝑗ΔFDI𝑖𝑡−𝑗
+ ∑𝑞−1𝑗=0γ5𝑖𝑗ΔEC𝑖𝑡−𝑗 +δ1ECTit− 1 +Ωit, Eq.(1’)
ΔTFPit = γi + ∑𝑝−1𝑗=1γ1𝑖𝑗ΔTFP𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ2𝑖𝑗ΔCE𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ3𝑖𝑗ΔETI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ4𝑖𝑗ΔFDI𝑖𝑡−𝑗
+ ∑𝑞−1𝑗=0γ5𝑖𝑗ΔEC𝑖𝑡−𝑗 + δ2ECTit− 1 +Ωit, Eq.(2’)
ΔECit = γi + ∑𝑝−1𝑗=1γ1𝑖𝑗ΔEC𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ2𝑖𝑗ΔTFP𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ3𝑖𝑗ΔETI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ4𝑖𝑗ΔFDI𝑖𝑡−𝑗
+ ∑𝑞−1𝑗=0γ5𝑖𝑗ΔCE𝑖𝑡−𝑗 + δ3ECTit− 1 +Ωit, Eq.(3’)
ΔFDIit = γi + ∑𝑝−1𝑗=1γ1𝑖𝑗ΔFDI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ2𝑖𝑗ΔTFP𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ3𝑖𝑗ΔETI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ4𝑖𝑗ΔCE𝑖𝑡−𝑗
+ ∑𝑞−1𝑗=0γ5𝑖𝑗ΔEC𝑖𝑡−𝑗+ δ4ECTit− 1 +Ωit, Eq.(4’)
ΔETIit = γi + ∑𝑝−1𝑗=1γ1𝑖𝑗ΔETI𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ2𝑖𝑗ΔTFP𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ3𝑖𝑗ΔCE𝑖𝑡−𝑗 + ∑𝑞−1𝑗=0γ4𝑖𝑗ΔFDI𝑖𝑡−𝑗
+ ∑𝑞−1𝑗=0γ5𝑖𝑗ΔEC𝑖𝑡−𝑗 + δ5ECTit− 1 +Ωit, Eq.(5’)
Where γlij are the short-run coefficients for l=1, 2, 3, 4, 5. The value of δ, the parameter of the error correction term (ECT), which is the error-Correction speed of the adjustment term of the model in equilibrium, is supposed to be negative, significant, and between 0 and 1 in absolute value.
The choice of the lagged variable (p, q) is determined according to the Akaike information criterion or Schwarz-Bayesian information criterion. Using the Wald test, the presence of a cointegration relationship is tested by the following hypothesis:
H0: β1i = β2i = β3i = β4i = 0 (absence of a long−run relationship), H1: β1i ≠ β2i≠ β3i ≠ β4i ≠ 0 (presence of a long−run relationship).
3. Results and discussion
The characteristics of the data series are presented in Tables 3 and 4 (below), descriptive statistics, and the correlation matrix, respectively, for the raw data variables.
Table 3: Descriptive Statistics
TFP ETIPERGDP FDIPERGDP EC CE
Mean 0.963936 1.587406 4.324692 62.81173 3.983763
Median 0.980885 1.067147 2.965113 63.09216 3.770275
Maximum 1.151638 7.899194 54.23906 137.3662 8.724242
Minimum 0.758335 0.000802 0.152586 12.40286 0.835943
Std. Dev. 0.084347 1.492490 6.662942 35.47806 2.499773 Skewness -0.299506 1.524484 5.823014 0.213012 0.530569 Kurtosis 2.652505 5.644052 40.08914 1.940399 1.934107 Jarque-Bera 3.696670 130.2977 11649.10 10.05361 17.43733 Probability 0.157499 0.000000 0.000000 0.006560 0.000164
Sum 178.3281 304.7820 800.0680 11620.17 736.9962
Sum Sq. Dev. 1.309038 425.4573 8168.643 231599.4 1149.791
The average ETI in percentage of GDP is around 1.587%, indicating a good investment level in the energy transition sector. Furthermore, ETIs across the sample countries gained momentum over the sampling period, as can be seen for China and Malaysia. The highest ETI flow per GDP is recorded in the China (7.899) in 2019, going up from 0.117 in 2014, and the lowest flow is recorded in Malaysia (0.000802) in 2014, going up to 1.045 in 2019. Interestingly, the EC standard deviation is relatively high, confirming the heterogeneity of the EC distribution across the emerging countries. Maximum TFP is registered in Brazil (1.15), which indicates a high level of economic growth and technological change
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in Brazil over the sampling period. At the same time, the maximum FDI as a percentage of GDP was registered in 2016 in Hungary at 54.3%. Finally, Kurtosis and skewness show that, apart from TFP, all variables are not normally distributed. The Jarque-Bera statistic further confirms this result.
Furthermore, since variables are not normally distributed, we use Spearman’s Rho to examine the relationships between all variables. Based on the relation matrix in Table 4, the results show no excessive correlation is detected between the study variables, suggesting multicollinearity is highly unlikely in the analysis.
Table 4: Correlation matrix
CE EC ETI FDI TFP
CE 1 0.954 0.03608 0.01415 0.0449
EC 0.954 1 -0.0519 0.04535 0.135
ETI 0.03608 -0.05193 1 -0.023611 0.0476
FDI 0.014153 0.04535 -0.02361 1 0.1867
TFP 0.044966 0.135762 0.04761 0.18678 1
3.1 Panel Unit Root Test
Using several panel unit root tests, including Augmented Dickey-Fuller (ADF) [38], Im et al (IPS) [39], the Levin et al. (LLC) [40] and Phillips & Perron (PP) [41], Table 5 presents the tests' results. The assumption is of a homogeneous autoregressive unit root under the alternative hypothesis. The IPS, notably the homogeneous character of the unit root under the alternative hypothesis, corrects the LLC hypothesis. [40] propose the ADF test, which assumes that all panel units' autoregressive coefficient dynamics are homogeneous. The non-parametric adaptation of Dickey-Fuller test is the PP test. The presence of a unit root is the null hypothesis in all tests.
Table 5: Panel Unit root test
Unit root test LNCE LNETI LNTFP LNEC LNGDP
LLC
Level
intercept -3.726 (0.0001)
-5,818 (0.000)
-2.2714 (0.0116)
-3.8138 (0.000)
-6.372 (0.000)
trend -0.413 (0.339)
-7.67 (0.0000)
2.9668 (0.0015)
-1.008 (0.56)
-3.855 (0.000) First
difference
Intercept -4.327 (0.0000)
-8.96 (0.000)
-6.8984 (0.000)
-4.22 (0.000)
-5.779 (0.000) Trend -4.578
(0.0000)
-4.137 (0.000)
-6.8782 (0.0000)
-4.324 (0.0000)
-7.0826 (0.000)
IPS
Level
Intercept -0.122 (0.4515)
-5.687 (0.000)
0.8470 (0.8015)
0.1814 (0.572)
-3.227 (0.0006) Trend 2.373
(0.991)
-5.6873 (0.000)
-0.4003 (0.3445)
1.28 (0.901)
0.1113 (0.544) First
difference
Intercept -3.558 (0.0002)
-9.235 (0.0000)
-4.0165 (0.0000)
-3.206 (0.0007)
-3.296 (0.0005) Trend -3.0305
(0.0012)
-7.237 (0.0001)
-2.8968 ( 0.0019)
-2.37 (0.0089)
-3.253 (0.0006)
ADF
Level
Intercept 28.413 (0.4427)
78.569 (0.000)
28.8125 (0.4221)
21.47 (0.7169)
47.118 (0.0032) Trend 15.155
(0.976)
57.198 (0.00006)
28.7965 (0.4229)
20.57 (0.763)
20.238 (0.683) First
difference
Intercept 58.473 (0.0006)
109.555 (0.0000)
62.4873 (0.0002)
50.407 (0.0028)
49.605 (0.0016) Trend 55.072
(0.0017)
87.269 (0.000)
51.3985 (0.0045)
44.955 (0.011)
49.79 (0.0015)
PP
Level
Intercept 61.703 (0.0002)
199.12 (0.0000)
66.744 (0.001)
44.458 (0.0135)
122.731 (0.000) Trend 12.999
(0.9929)
136.341 (0.0000)
28.5702 (0.4346)
18.866 (0.842)
29.133 (0.215) First
difference
Intercept 96.411 (0.0000)
175.551 (0.000)
94.7964 (0.0000)
85.1235 (0.000)
75.714 (0.0000) Trend 102.881
(0.0000)
170.655 (0.0000)
96.3966 (0.0000)
87.326 (0.000)
92.742 (0.000) Notes: the values in brackets are the corresponding p-values
8
Overall, we conclude that CE, TFP, and EC are integrated into the first order since we tested the stationarity of the variables using various panel unit roots tests. ETI and GDP, on the other hand, are level stationary. Based on these findings, the implementation of the ARDL model is justified.
3.2 Results and discussion:
This section empirically examines to what extent the ETI-FDI-environment nexus have contributed to the CE mitigation in the EMs in the short and long runs. Table 6 reports the results for the long-term elasticity of CE in terms of FDI, TFP, ETI, and EC. The coefficients of the ECTs are significant. This implies that the model converges to equilibrium over the long term. Hence, the ECT helps in adjusting and partially restoring the cointegration relationship. For Wald tests, p-values=0; then, we reject the null hypothesis of no cointegration, i.e., a long-run relationship exists.
3.2.1 PMG Long Run Estimates
The estimated results of the PMG ARDL are presented in table 6 and are discussed following the order of our objectives reported in the introduction section.
3.2.1.1 Objective 1: testing the new hypothesis: the dual Effects of ETI on CE and TFP As per column 1, concerning the effect of ETIs on CE reduction, the results show a very significant (1%) although weak negative impact (-0.0055). Our interpretation of this result is that the sample countries are on the right transition path, but the level of ETIs has not yet reached a sufficient level to reduce their CE to a sizeable effect. This is in line with [42] latest report. To further decrease CE, emerging countries should harness more finance for ETIs, and focus their investment volume and composition on closing the finance gap in low-carbon technologies. Similar results were found with RE by the REN21 [43], which concluded that the revolution in the power sector is driving rapid change towards a RE future. However, the overall transition is not advancing at the required speed.
Column 2 shows that ETI's impact on TFP is significant in the long run, with a 1% increase in ETI leading to a rise of 0.0136% in TFP. Although R&D in RE technologies is one of the aims of ETI, its positive impact is not limited to technologies only but also economic activity through energy efficiency enhancement. So we expect the positive size-effect of ETI on TFP to increase in EMs. This is because as R&D in RE increases, the costs (viability) of assets of RE projects decrease (increases), which in turn fosters further ETIs in EMs. As per IRENA (2021) report [42], this is happening.
The above results validate our newly developed hypothesis, with ETIs having a significant dual impact positively affecting economic growth and negatively on CE.
3.2.1.2 Objective 2: PHH augmented equation test
As per the base model (column 1, Table 6), FDI has a significant direct positive impact on CE. In the long run, it shows that CE will increase by 0.0098% for every 1% increase in FDI at a 5% significance level. These results suggest that the positive impact brought by FDI (dirty technology → increasing CE) outweighs the negative effect (clean technology→increasing the deployment of clean and energy- efficient technology) in EMs. Consequently, the augmented PHH is validated in the case of EMs. This result is in line with [44], that found 'CE increase due to FDI' to be greater than 'CE decrease due to clean technology that accompanied FDI' in China during the 1980-2012 period.
As per Table 4, FDI has a significant positive impact on TFP, with a 1% increase in FDI leading to a rise in TFP by 0.0633%. The improvement in TFP results from intangible factors such as technological change, R&D and education that accompany FDI inflows. This shows that FDI's effect on TFP improves the emerging countries' energy transition readiness. In fact, as per column 5, a 1% increase in FDI leads to a significant rise in ETI by 0.129. Moreover, with a 1% increase in TFP, FDI goes up by 2.508%, showing that FDIs are attracted by efficient use of resources and new technology.
9
Table 6: Results of estimation
The long-run dynamics of EC and CE are significant, with a 1% increase in EC leading to an increase of 0.797% in CE. This shows that the increase in EC, associated with more FDIs and mostly from dirty sources, is a source of CE in the EMs. This demonstrates the first indirect effect of FDI on CE. To better understand the overall environmental effects of FDI, the other indirect effects on CE should also be considered [30], including TFP and ETI.
3.2.1.3 Objective 3: investigating the indirect effects of FDI on CE
While Column 2, Table 6, shows FDI has a significant positive impact on TFP, with a 1% increase of FDI leading to a rise in TFP by 0.0633%, column 1 shows an increase of TFP by 1% decreases CE by 0.1414%. This shows that TFP in our panel countries reached the threshold level that allows environmental protection in the long run. The two results combined show the indirect impact of FDI on CE via the TFP channel in the long run. Furthermore, column 3, Table 6, shows that a 1% increase in TFP significantly decreases EC by 0.295%, owing to new technologies10 that allow for more efficient use of energy and other factors of production. This demonstrates the indirect negative effect of TFP on
9The literature clarifies that CE may affect EC and TFP for a plethora of reasons, inter alia, (i) policymaking bodies formulating policies to decrease CE, limit the consumption of certain types of energy, which in turn negatively affects TFP. (ii) Furthermore, to limit the deterioration of the health of the population, workers, in particular, ultimately affect EC patterns or habits and the productivity of workers within the economy (Asongu et al. 2016). (iii) From intuition, EC is directly associated with CE and TFP growth.
10Which comes with FDI.
Column 1 Column 2 Column 3 Column 4 Column 5
Dependent variable: CE TFP9 EC9 FDI ETI
Panel A: Long run
CE - -0.3599**
(-3.1072)
0.86***
(65.75)
-0.893***
(-7.247)
-0.594***
(-7.095)
TFP -0.1414***
(-3.0938) - -0.295***
(-8.054)
2.508**
(2.66)
5.791***
(8.619)
EC 0.7970***
(22.149)
0.1574**
(2.1314) - 0.499***
(11.16)
0.2302***
(5.916)
FDI 0.0098**
(2.5955)
0.0633***
(6.7247)
0.027***
(8.189) - 0.129*
(1.937)
ETI -0.0055***
(-1.8987)
0.0136**
(2.2589)
0.008***
(8.1680)
0.011
(0.6915) -
Panel B: Short run
ECT -0.6317***
(-3.5377)
-0.1006 (-1.4839)
-0.295 (-2.045)
-0.72 (-5.700)
-0.3306***
(-4.827)
Δ(CE) - 0.1578
(0.0099)
0.238*
(1.8616)
2.55 (0.78)
-2.903*
(-2.07)
Δ(TFP) -0.26
(-0.834) - 0.359
(0.0257)
5.139 (1.005)
-4.786*
(-1.96)
Δ(EC) 0.258
(1.36)
-0.08
(-1.099) - -1.297
(-0.502)
6.588*
(3.37)
Δ(FDI) 0.006
(0.8304)
-0.003 (-1.099)
-0.0014
(-0.398) - 0.0004
(0.009)
Δ(ETI) -0.004
(-0.508)
-0.002 (-0.539)
-0.004 (-0.582)
0.409*
(2.199) -
C -1.275**
(-3.514)
-0.026 (-0.88)
0.828*
(2.1078) - -
TREND 0.001
(0.473) - - - -
Cointegration tests
Wald ꭓ2 1564.6 11.455 3650.3 346.8 122.408
Prob sup ꭓ2 0.000 0.0219 0.000 0.000 0.000
Notes:
All variables are in logarithm
*, **, *** denote the rejection of the null hypothesis at 1, 5, and 10% significance level; t-stat is in [ ]
10
CE via the EC channel. Overall, the previous three results combined show the indirect effects of FDI on CE via the TFP and EC channels. These results support [30], who found negative indirect effects of FDI on CE in China and [45], which also found significant indirect effects on environmental quality while testing PHH for developing countries.
The other indirect effect of FDI on CE happens thru the ETI channel via the following three steps. First, the very significant and positive effect of FDI on TFP (0.0633***) as mentioned above. Second, as per column 5, the very significant and sizeable effect of TFP on ETI (5.791***). This shows TFP to be a very significant driver of ETIs in EMs. Finally, as per column 1, ETI has a very significant negative effect on CE (-0.0055***). With spillovers from R&D and innovations accompanying FDIs, the latter leads to a decline in costs of RE assets [9]. This, coupled with the increase in the consequent viability of RE projects [31], motivates an increase in ETIs, which would lead to an accelerating transition and an increase in the deployment of clean and energy-efficient technologies and an overall reduction in CE [32].
It is worth noting that in the context of transition, an increase of ETI by 1% does not only increase EC (0.008%) in the long run, as per column 3, but it also improves people's access to energy in EMs. This is because ETIs provide new energy sources, such as RE, that are included in the variable EC11. However, because EMs are energy deficient, these newly created energy sources are used to fill the energy gap, not to replace fossil fuel-based energy. On the other hand, a 1% increase in EC increases ETI by 0.23% (column 5, Table 6). This shows that increasing EC's needs are drivers for further ETIs in emerging countries.
3.2.2 PMG Short Run Estimates
This section presents short-run estimations and outlines the error correction model corresponding to long-run equilibriums or cointegration relationships.
The feedback coefficients for the cointegrating vectors for CE, TFP, EC, FDI, and ETI are presented in Panel B, Table 6. We first notice that, apart from TFP, intervals of ECT's parameters and signs are consistent with theory. Moreover, the parameters δ1, δ2, δ3, δ4, and δ5 indicate the speed of adjustment to the equilibrium level, and they are obtained by having recourse to the PMG method. ECT's parameters corresponding to the variables are significant for all variables except TFP as a dependent variable. This implies that in the presence of a shock, CE, EC, FDI, and ETI can significantly be restored to their long- run equilibrium with a speed of adjustment of 63%, 29.5%, 72%, and 33.06%, respectively. This is a good scenario because the fundamentals of all ECTs (Table 6 panel B) are mostly weakly exogenous with a slight exception for Δ(CE) column 3, Δ(ETI) column 4 and Δ(CE), Δ(TFP), and Δ(EC) column 5. The highlighted fundamentals are significant and display strong exogeneity relative to the corresponding ECT. The speed of 72% for FDI indicates that about 72% of disequilibrium caused by previous years' choices will be corrected in the current year and converges back to long-run equilibrium.
The ETI flow displays a strong exogeneity relative to FDI's ECT. The adjustment of imbalance to restore a long-term relationship depends on ETI.
4. Conclusion and policy implications
Using the ARDL model on annual data of 16 years for a sample of 12 EMs, in this paper, we develop and test a new hypothesis about mitigation being an investment opportunity as per the heterodox approach to CE mitigation. We also investigate the validity of PHH by augmenting the PHH equation with the new variable, Energy transition investments (ETIs). Moreover, unlike previous research, we differentiate between the direct and indirect effects of FDI, and to measure economic activity, we use total factor productivity (TFP) instead of GDP.
In what follows, we present the key findings and discuss the implications and policy recommendations.
11Please see EC definition
11
First, our empirical results validate our newly developed hypothesis. ETI has a significant dual effect, positively affecting economic growth and negatively CE, albeit limited. The limited effect on CE stems from the fact that:
i. EMs have expanded their ETIs in their energy investment portfolio. However, without a matching reduction of fossil fuel and coal energy, as [46] showed, they will not have much impact on environmental sustainability. In fact, unlike in the developed countries where ETIs are driven by environmental sustainability and energy security concerns, which are found to reduce CE, ETIs in EMs are motivated by international community pressure, the decreasing cost of RE technology [42], and the increasing viability of the RE projects. We believe that this difference in ETIs' drivers contributes to the positive, although limited, effects of ETIs on CE in the EMs our results have shown.
ii. EMs' ETIs have not reached the threshold level to allow environmental protection in the long run.
Thus, further ETIs would increase RE capacity and energy efficiency and accelerate EMs' energy transition readiness. Many studies have supported this, including [47] and [48] new reports. In fact, although global annual investment in the RE sector has increased steadily over time, averaging USD 322 billion annually during the 2015-2019 period as per Table 1, [47] maintained that the current investment levels are not nearly enough to put the world on a climate-compatible pathway.
Furthermore, global annual RE investment needs to almost triple between now and 2050.
Considering the positive contribution of ETI to CE mitigation, our recommendation for policy and other decision-makers in EMs is to seize the opportunity and take robust decisions to promote further transition investments exploiting the declining costs and the increasing performance of renewables.
Promotion can be in the form of actively facilitating the mobilization of energy transition finance, which currently we believe to be at its best for at least two reasons. One, private finance is well-positioned to play a significant role in the transition with the increasing viability and decreasing costs of RE projects [49]. Two, crises are reshaping the energy system and the energy investors' choices. For example, the 2009 great recession helped slash the carbon market by half due to reduced production output, leaving investors looking for alternative investment opportunities other than the pollutant ones. This represented a golden opportunity for clean energy investment projects to find funding among investors willing to invest in green bonds and equity funds [49]. Furthermore, COVID-19 represents another big shock to the energy system and equally presents an opportunity to steer the energy sector onto a more sustainable path [50].
Another important implication of our research is that if the patterns of ETIs identified in this paper continued, mitigation would intensify. ETIs' negative impact on CE might outweigh the positive effect of FDI on CE. Having said that though, it is imperative to remember that this mitigation intensification goal will not be easily achieved if ETIs are developed without parallel changes to political-economic structures, which are announced in the national development contributions (NDCs) pledges [49].
Second, our empirical results validate the augmented PHH for our sample countries. With ETIs included in the PHH equation, FDIs inflows into EMs still neglect environmental standards and bring in more dirty technologies than clean ones. The recommendation for EMs to continue reviewing the quality of their FDI requirements encouraging clean technology FDIs, and discouraging their dirty technology counterparts to improve energy use efficiency and decrease CE. However, as per ETIs' negative impact on CE noted above, the ETI-augmented PHH can be reversed once ETIs reach the necessary threshold.
In the current research, we are unable to give an estimation of this threshold, and we call for future research to answer this question. Further research is also proposed to test the ETI-augmented PHH once that ETI threshold is reached.
Third, since we found FDIs to have a significant and indirect effect on CE via the ETIs, TFP and EC channels, we conclude that to evaluate the total effects of FDI properly, it is not enough to estimate its direct effects done through testing the PHH hypothesis. It is imperative for future research to include the FDI indirect effects thru all potential channels.
Fourth, as discussed in the introduction, previous research shows that to reduce EC, the orthodox approach to climate change recommends using MBIs, such as taxes and subsidies. However, this can negatively disrupt economic growth, discourage firms from innovating or reducing their CE [6].
However, a policy that aims to reduce EC to mitigate CE should be formulated with much attention paid to economic development and environmental protection. As our analysis shows that ETI has a dual effect of increasing TFP (which reduces EC) and decreasing CE, to resolve the dilemmatic situation and in line with the heterodox approach of viewing mitigation as an opportunity, a further increase in ETIs is highly recommended. This would replace the dirty energy with RE without decreasing EC, so both economic growth and CE mitigation objectives are met.
12
Finally, some limitations should be acknowledged while evaluating the empirical findings of this investigation. One of them is the impact of the sampling on the outcomes. Another one has to do with differences between sample countries in terms of things like levels of energy transition readiness and amount of ETIs and institutional factors.
References
1. IPCC. Climate Change 2014: Synthesis report [Internet]. Geneva: IPCC; 2015. 151 p. Available from: http://www.ipcc.ch/report/ar5/syr/
2. IEA. Implications – Global Energy Review 2020 – Analysis [Internet]. IEA. 2020 [cited 2021 Jul 17]. Available from: https://www.iea.org/reports/global-energy-review-2020/implications 3. OECD. Toward green growth: a summary for policy makers. 2011.
4. UNEP. Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication - A Synthesis for Policy Makers [Internet]. 2011. Available from:
www.unep.org/greeneconomy
5. Barker T, Ekins P, Junankar S, Pollitt H, Summerton P. The competitiveness effects of European environmental fiscal reforms. 2009;3(1):33.
6. Rademaekers K, Laan J van der, Smith M, Breugel C van, Pollitt H. The role of market-based instruments in achieving a resource efficient economy. ECORYS; 2011. Report No.:
ENV.G.1/FRA/2006/0073.
7. Piana V. Bringing heterodox recipes to policymakers: the case of “Innovative economic policies for climate change mitigation.” In 2010.
8. Mathis, Will, Rathi, Akshat. Low Clean-Energy Spending in Poor Countries Puts Climate Goals in Peril. Bloomberg.com [Internet]. 2021 Jun 9 [cited 2021 Jul 18]; Available from:
https://www.bloomberg.com/news/articles/2021-06-09/low-clean-energy-spending-in-poor- countries-puts-climate-goals-in-peril
9. Bloomberg NEF. Energy Transition Investment Trends 2021 [Internet]. BloombergNEF. 2021 [cited 2021 Jul 17]. Available from: https://about.bnef.com/energy-transition-investment/
10. Lee JW. The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth. Energy Policy. 2013 Apr 1;55:483–9.
11. Birdsall N, Wheeler D. Trade Policy and Industrial Pollution in Latin America: Where Are the Pollution Havens? The Journal of Environment & Development. 1993 Jan 1;2:137–49.
12. Zarsky L. Havens, halos and spaghetti : untangling the evidence about foreign direct investment and the environment. Foreign direct investment and the environment : [papers presented at the OECD Conference on Foreign Direct Investment and the Environment held in January 1999 in The Hague]. 1999;
13. Sandborke & Metha. OECD Global Forum on International Investment : Foreign Direct Investment and the Environment. 2002;229.
14. Walter I, Ugelow JL. Environmental Policies in Developing Countries. Ambio. 1979;8(2/3):102–9.
15. Stavropoulos S, Wall R, Xu Y. Environmental regulations and industrial competitiveness:
evidence from China. null. 2018 Mar 9;50(12):1378–94.
16. Siebert H. Environmental Quality And The Gains From Trade. Kyklos. 1977;30(4):657–73.
17. Sarkodie SA, Strezov V. Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Science of The Total Environment. 2019 Jan 1;646:862–71.
18. Bu M, Lin CT, Zhai S. Comparative Environmental Regulation and Foreign Investment Inflows: Is China a Pollution Haven? Frontiers of Economics and Globalization. 2014 Nov 13;14:1–20.
19. Sun C, Zhang F, Xu M. Investigation of pollution haven hypothesis for China: An ARDL approach with breakpoint unit root tests. Journal of Cleaner Production [Internet]. 2017 Sep 10 [cited 2021 Jul 17];161:153–64. Available from:
https://www.sciencedirect.com/science/article/pii/S0959652617310594
13
20. Ghouali YZ, Belmokaddem M, Sahraoui M, Guellil M. Factors Affecting CO2 Emissions in the BRICS Countries : A Panel Data Analysis. Procedia Economics and Finance. 2015 Oct 17;26:114–
25.
21. Behera SR, Dash DP. The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region.
Renewable and Sustainable Energy Reviews [Internet]. 2017 Apr 1 [cited 2022 Jan 23];70:96–
106. Available from: https://www.sciencedirect.com/science/article/pii/S1364032116309546 22. Zhu H, Duan L, Guo Y, Yu K. The effects of FDI, economic growth and energy consumption on
carbon emissions in ASEAN-5: Evidence from panel quantile regression. Economic Modelling [Internet]. 2016 Nov 1 [cited 2022 Jan 23];58:237–48. Available from:
https://www.sciencedirect.com/science/article/pii/S0264999316301262
23. Zhang & Zhu. Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China - ScienceDirect [Internet]. 2016 [cited 2022 Jan 23]. Available from:
https://www.sciencedirect.com/science/article/abs/pii/S1364032115016093
24. Al-mulali U, Foon Tang C. Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy [Internet]. 2013 Sep 1 [cited 2021 Jul 8];60:813–9. Available from:
https://www.sciencedirect.com/science/article/pii/S0301421513003911
25. Galvin,N., Healy, R. R. The Green New Deal Is More Relevant Than Ever [Internet]. Scientific American Blog Network. 2020 [cited 2021 Dec 9]. Available from:
https://blogs.scientificamerican.com/observations/the-green-new-deal-is-more-relevant-than- ever/
26. Boyle AD, Leggat G, Morikawa L, Pappas Y, Stephens JC. Green New Deal proposals: Comparing emerging transformational climate policies at multiple scales. Energy Research & Social Science [Internet]. 2021 Nov 1 [cited 2021 Dec 9];81:102259. Available from:
https://www.sciencedirect.com/science/article/pii/S2214629621003522
27. York R, McGee JA. Does Renewable Energy Development Decouple Economic Growth from CO2 Emissions? Socius [Internet]. 2017 Jan 1 [cited 2021 Jul 17];3:2378023116689098. Available from: https://doi.org/10.1177/2378023116689098
28. Ladu, MG, Meleddu, M. Is there any relationship between energy and TFP (total factor
productivity)? A panel cointegration approach for Italian regions [Internet]. 2014 [cited 2021 Jul 18]. Available from: https://ideas.repec.org/a/eee/energy/v75y2014icp560-567.html
29. Easterly W, Levine R. It’s Not Factor Accumulation: Stylized Facts and Growth Models. The World Bank Economic Review. 2001;15(2):177–219.
30. Huang & al. The Heterogeneous Effects of FDI and Foreign Trade on CO2 Emissions: Evidence from China [Internet]. 2019 [cited 2021 Jul 17]. Available from:
https://www.hindawi.com/journals/mpe/2019/9612492/
31. Rogoff K. The case for a World Carbon Bank. Business Standard India [Internet]. 2019 Jul 12 [cited 2022 Apr 23]; Available from: https://www.business-standard.com/article/opinion/the- case-for-a-world-carbon-bank-119071200057_1.html
32. Jaffe A, Newell R, Stavins R. Environmental Policy and Technological Change. Environmental &
Resource Economics. 2002 Feb 1;22:41–70.
33. World Economic Forum. Fostering Effective Energy Transition: 2020 Edition | Inter American Dialogue [Internet]. 2020 [cited 2021 Jul 17]. Available from:
http://globaltrends.thedialogue.org/publication/fostering-effective-energy-transition-2020- edition/
34. World Bank. World Development Indicators | DataBank [Internet]. 2021 [cited 2021 Jul 18].
Available from: https://databank.worldbank.org/source/world-development-indicators 35. BP Statistics. Statistical Review of World Energy | Energy economics | Home [Internet]. bp
global. 2021 [cited 2022 Apr 23]. Available from:
https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world- energy.html
