Developing states and the green challenge. A dynamic approach

Keywords: CO2 emissions, urbanization, energy efficiency, renewable energy, developing countries, environmental Kuzents curve, GMM panel VAR. JEL Classification: Q01, Q53, Q56, O13

This paper studies the effects of output, urbanization, energy intensity, and renewable energy on aggregated and sector-specific CO2 emissions for a rich sample of developing states. We employ the recently developed GMM panel VAR technique, which allows us to tackle the potential endogeneity issue and capture both the current and future impact of indicators on CO2 via the impulse-response analysis. On the one hand, robust to several alternative specifications, the findings indicate that output, urbanization, and energy intensity increase the aggregated CO2 emissions, while renewable energy exhibits an opposite effect. Moreover, regarding the CO2 responsiveness to output and urbanization shocks, the pattern may suggest that these countries are likely to attain the threshold that would trigger a decline in CO2 emissions. We also reveal heterogeneities related to both countries' economic development and Kyoto Protocol ratification/ascension status. On the other hand, the sectoral analysis unveils that the transportation, buildings, and noncombustion sector tend to contribute more to increasing the future CO2 levels. Overall, our study may provide useful insights concerning environmental sustainability prospects in developing states.

I. Introduction

As a global and stock pollutant with the highest share in greenhouse gasses, carbon dioxide (CO2) emissions are considered the main driving force of environmental degradation.

According to [START_REF] Olivier | Trends in global CO2 and total greenhouse gas emissions: 2017 report[END_REF] report, developing countries such as Indonesia and India have recorded the highest absolute increase in CO2 emissions in 2016 (6.4% and 4.7% respectively), followed closely by Malaysia, Philipines, and Ukraine. Indeed, having the fastest-growing economies, most developing countries experience complex structural changes that reflect in the mix of various socio-economic processes such as industrialization and urbanization. For example, according to the United Nations World Urbanization Prospects (2014)1 , the urban population in 2014 accounted for 54% of the total world population, and it is expected to rise at about 66% by 2050. Additionally, considering the worldwide ongoing urbanization process, Asia and Africa seem to exhibit the fastest rate of urbanization. Overall, these changes strongly connected with the industrialization process also imply an intensification and a shift of economic activities towards urban conglomerates, demanding the use of more energy resources, which in turn may reflect in higher pollution.

Consequently, some of the key factors that can help mitigate pollution include the gradual replacement of classic fossil fuels with more carbon-neutral alternatives, the increase of renewable sources in the energy mix, and the improvements in energy efficiency. In this regard, the renewable energy and energy efficiency projects implemented between 2005 and 2016 in developing economies, and supported at the international level, are expected to reduce greenhouse gas (GHG) emissions by 0.6 gigatons of CO2 per year by 2020 (United Nations Environment Programme, 2017)2 . Likewise, based on the same report, approximately 75 developing or emerging economies have implemented policies or programs that incorporate renewable energy and energy efficiency technologies.

Looking at developing countries' positions vis-à-vis the global environmental challenges and the main related tools designed to address them, they differ in certain features from developed countries. On the one hand, developing states being Non-Annex I parties of the Kyoto Protocol do not have binding commitments to reduce or limit their emissions, compared to their industrialized counterparts. Nonetheless, they may voluntarily comply, and the advanced economies that choose to support them in fighting global warming may also benefit in terms of the fulfillment of their commitments. For example, the Kyoto Protocol's well-known Clean Development Mechanism (CDM) is designed to jointly involve developing and developed economies in fighting climate change through the implementation of various green projects. 3 On the other hand, following the Paris Agreement's adoption under the umbrella of the United Nations Framework Convention on Climate Change (UNFCCC), both developing and developed economies are required to put the efforts and fight together against the imminent threats of climate change. As such, the Paris Agreement may represent one of the most powerful instruments adopted so far concerning developing countries and their active role in combating and mitigating the harmful effects of global warming.

Taking stock of the above mentioned, the goal of this paper is to assess the responsiveness of CO2 emissions following external disturbances to output and urbanization, assuming a transmission channel that incorporates two of the key elements used in mitigating environmental degradation, namely the renewable energy and energy efficiency. In doing so, we employ the recently-developed Generalized Method of Moments (GMM) panel Vector Auto-Regression (VAR) approach of [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF], which allows us to explore the essential dynamics and tackle the potential endogeneity between indicators. The technique is applied for a comprehensive group of developing countries, within a modified Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, which along with the Environmental Kuznets Curve (EKC) hypothesis 4 helps us to provide the necessary economic foundation for the assumed innovations' transmission mechanism required to identify the key structural shocks. Furthermore, opposite to a sizeable empirical strand of literature that independently examines the nexus between CO2 and growth (urbanization) via the classical (urbanization-related) EKC hypothesis, we jointly test these two well-known hypotheses. Indeed, building on the general belief that a vast majority of developing economies have not yet reached the maximum level of growth (urbanization) that would ensure a decrease in pollution, this approach via the computation of impulse response functions (IRFs) allows us to assess whether this will be feasible or not in the future. As well, motivated by the ongoing structural changes that developing countries experience, we focus on aggregated and sector-specific CO2, as they may provide us with complementary insights. 3 As a result of the CDM green projects (i.e., projects aimed at reducing emissions) implemented in developing countries, the Annex I parties can buy Certified Emission Reduction (CER) units, which in turn help them to meet some of their commitments of emission reduction (Carbon Trust, 2009). 4 The classical EKC hypothesis states that the relationship between environmental degradation and economic growth follows a bell-shaped pattern [START_REF] Grossman | Environmental impacts of a North American free trade agreement[END_REF].

Our findings can be summarized as follows. First, although output and urbanization shocks trigger a rise in the current and future levels of CO2, the effect may reverse, and in the long-run, a bell-shaped pattern seems to be at work in terms of the CO2 responsiveness.

Moreover, the green actions that developing countries have taken in the last decades, in particular those related to renewable energy sources, seems to reduce the cumulated CO2 emissions both on the short-and long-term horizon. However, considering that the positive disturbances to energy intensity are associated with an increase in CO2, more attention should be paid to energy efficiency, by attracting and implementing more related projects. Also, while the results confirm the persistence in CO2, a permanent shock to its dynamics causes only a small increase in the future emissions levels.

Second, we examine the robustness of these findings by changing the order of variables into the transmission channel, altering the sample in several ways concerning both N and T dimensions, and controlling for an extensive set of exogenous factors. According to IRFs, the shocks to output, urbanization, energy intensity, and CO2 have the same cumulated increasing effect on CO2, opposite to positive disturbances to renewables that trigger a cumulated decrease in CO2. Likewise, the CO2 response to GDP and urbanization shocks tends to exhibit a bell-shaped pattern in the long-run, indicating that the related EKC hypothesis may be validated.

Third, we find that the results are sensitive to both countries' level of development and the Kyoto Protocol ratification/ascension status. On the one hand, overall, the IRFs show that low income economies might experience a more moderate increase in pollution in the long-run than lower-middle income states. Besides, in low income states, the results seem to be compatible with the EKC hypothesis, especially for urbanization. On the other hand, the countries that ratified or acceded to the Kyoto Protocol before it entered into force may also be those which have been more actively engaged in combating pollution, given that both output and urbanization are more likely to display a threshold effect on CO2 (i.e. validate the EKC hypothesis in the long-run). Indeed, this may also suggest that these states faced the effects of increasing pollution earlier and, thus, decided to become more actively involved in combating climate change sooner than their counterparts.

Finally, despite the positive response of aggregate CO2 to output and urbanization shocks, when the sectoral components of CO2 are taken into account, the findings appear to be much more diverse. In particular, we find opposite results for the other industrial combustion and power industry sector, namely external disturbance to both GDP and urbanization lead to a cumulated decrease in associated CO2. Thus, overall, the disaggregated CO2 analysis indicate that transportation followed by construction and non-combustion sector are more prone to contribute to increasing CO2 pollution in developing economies.

The remainder of the paper is organized as follows. Section 2 reviews the related empirical literature. Section 3 provides the STIRPAT framework, discusses the research methodology, and describes the data used in the analysis. Section 4 presents the baseline empirical findings.

Section 5 examines the robustness of these findings. Section 6 explores their sensitivity.

Section 7 analyzes the sector-specific CO2 dynamics following exogenous shocks to other system variables, and the last section presents the concluding remarks.

II. Literature review

In the light of the vast body of empirical literature on the environmental degradation determinants, this section aims to review some of the most recent empirical studies that tackle the impact of output, urbanization, (non-) renewable energy, among other explanatory factors, on environmental degradation. More precisely, we mainly focus on works that explore this nexus for developing5 economies in the context of STIRPAT and/or EKC hypothesis (both the classical and urbanization related ones). As well, given that the output appears in almost all studies as one of the main determinants of environmental degradation, we split the literature into two main parts, namely the (i) output-urbanization-environmental degradation nexus, and (ii) output-(non-)renewables-environmental degradation nexus. 6First, regarding the impact of economic growth and urbanization on environmental pollution, we further split the studies into two sub-categories. Thus, the first strand of literature tackles the papers that extend the baseline STIRPAT equation and/or EKC hypothesis to capture the effects of the urbanization process. In this fashion, researchers such as [START_REF] Lin | Analysis of the environmental impact of China based on STIRPAT model[END_REF], [START_REF] Li | Analysis on influence factors of China's CO2 emissions based on Path-STIRPAT model[END_REF], [START_REF] Wang | Examining the impact factors of energyrelated CO2 emissions using the STIRPAT model in Guangdong Province, China[END_REF], [START_REF] Wang | Assessing CO2 emissions in China's commercial sector: Determinants and reduction strategies[END_REF], among others, using time-series data on China reveal that, overall, both urbanization and economic growth exacerbate the environmental degradation. The authors employ techniques such as ridge regression, partial least-squares regression, or VAR model. Likewise, the findings of [START_REF] Talbi | CO2 emissions reduction in road transport sector in Tunisia[END_REF] for Tunisia and [START_REF] Pata | Renewable energy consumption, urbanization, financial development, income and CO 2 emissions in Turkey: Testing EKC hypothesis with structural breaks[END_REF] for Turkey show that urbanization increases CO2 pollution, while economic growth exhibits a nonlinear effect on CO2, validating the EKC hypothesis.

Furthermore, making use of STIRPAT framework, several works (see e.g. [START_REF] Poumanyvong | Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis[END_REF][START_REF] Liddle | Population, affluence, and environmental impact across development: Evidence from panel cointegration modeling[END_REF]Iwata & Okada, 2014;Sadorsky, 2014a;[START_REF] Wang | The relationship between urbanization, energy use and carbon emissions: evidence from a panel of Association of Southeast Asian Nations (ASEAN) countries[END_REF]among others) examine the effects of growth and urbanization on environmental degradation, using either samples of developing countries or mixed samples comprising both developing and developed economies. However, in most cases, the findings unveil that both variables have a positive effect on environmental degradation. Also, it is worth noted that concerning economic growth, [START_REF] Liddle | Population, affluence, and environmental impact across development: Evidence from panel cointegration modeling[END_REF] and [START_REF] Wang | Is there an Environmental Kuznets Curve for SO2 emissions? A semi-parametric panel data analysis for China[END_REF] find evidence in favor of the EKC hypothesis. Also, scholars such as [START_REF] Li | Environmental Kuznets Curve in China: New evidence from dynamic panel analysis[END_REF], [START_REF] Awad | Climate Changes in Africa: Does Economic Growth Matter? A Semi-parametric Approach[END_REF][START_REF] Joshi | Democracy and carbon dioxide emissions: Assessing the interactions of political and economic freedom and the environmental Kuznets curve[END_REF], among others, study the relationship between pollution and growth in the context of the EKC hypothesis, while controlling for the effects of the urbanization process.

The authors apply either parametric or semi-parametric panel data techniques, and, most frequently, environmental degradation is proxied by CO2 emissions. Overall, the findings seem to be mixed with respect to the EKC hypothesis's validity, whereas urbanization tends to increase the pollution levels.

The second strand of literature focuses on testing the urbanization-related EKC hypothesis, whether or not this is done within the STIRPAT context. In this manner, the findings provided by Martínez-Zarzoso & Maruotti (2011) support the urbanization-pollution EKC for 88 developing states spanning over the period 1975[START_REF] Pesaran | A simple panel unit root test in the presence of cross section dependence[END_REF]. Opposite, Zhu et al. (2012) and, more recently, [START_REF] Wang | Is there an Environmental Kuznets Curve for SO2 emissions? A semi-parametric panel data analysis for China[END_REF] find little evidence in favor of urbanization-CO2 and urbanization-SO2 EKC hypothesis, respectively. Besides, employing the panel VAR analysis, [START_REF] Lin | Energy and carbon intensity in China during the urbanization and industrialization process: A panel VAR approach[END_REF] study the dynamic relationship between industrial structure, urbanization, energy intensity, and carbon intensity for 30 provinces of China spanning over the period 2000-2015. Concerning the carbon intensity response following external shocks to the other variables, the IRFs reveal that both urbanization and industrial structure decrease the carbon intensity, while energy intensity disturbances increase (decrease) on impact (after three years) the carbon intensity. According to twenty years horizon FEVDs' results, only urbanization exhibits a bell-shaped pattern on carbon intensity. Also, the findings provided by [START_REF] Chen | Impact of urbanization on CO2 emissions and energy consumption structure: a panel data analysis for Chinese prefecture-level cities[END_REF] and [START_REF] Xie | Combined nonlinear effects of economic growth and urbanization on CO2 emissions in China: Evidence from a panel data partially linear additive model[END_REF] show that urbanization exhibits nonlinear effects on CO2 for 188 Chinese prefecture-level cities and 30 Chinese provinces, respectively.

Second, the present study is also related to the body of literature that investigates the effects of economic growth, non-renewable energy (especially energy intensity), and/or renewable energy on environmental degradation. In this regard, [START_REF] Shahbaz | Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries[END_REF] investigate for 13 Sub Saharan African states the link between energy intensity and CO2, while additionally test the EKC hypothesis. On the one hand, the long-run panel findings unveil that the energy intensity has a positive impact on CO2, while a bell-shaped pattern characterizes the CO2-GDP nexus. On the other hand, the country-specific long-run results reveal that energy intensity significantly increases CO2 in countries such as Botswana, Congo Republic, Gabon, Ghana, South Africa, Togo, and Zambia. Besides, an inverted Ushaped (U-shaped) relationship between CO2 and GDP is found in South Africa, the Congo Republic, Ethiopia, and Togo (Senegal, Nigeria, and Cameroon). [START_REF] Antonakakis | Energy consumption, CO2 emissions, and economic growth: An ethical dilemma[END_REF] employ the panel VAR approach to investigate the dynamic interrelationship between output, energy consumption (and its subcomponents, namely electricity, oil, renewable, gas, and coal) CO2. In doing so, the authors concentrate on a large panel of 106 states spanning over the period 1971-2011. Overall, for low income group, the findings (based on cumulative generalized IRFs) reveal that CO2 respond significantly and positively only to output and oil consumption shocks. On the contrary, for lower-middle income countries, the CO2 seems to react significantly and positively to output, aggregated energy consumption, electricity consumption, and oil consumption. Likewise, [START_REF] Naminse | Economic Growth, Energy Intensity, and Carbon Dioxide Emissions in China[END_REF] examine for China the link between economic growth, energy intensity (in terms of coal, oil, gas, and electricity), and CO2, over the period 1952-2012. The results based on the IRFs analysis show that coal, electricity, and oil consumption have a positive impact on the future levels of CO2. In contrast, gas consumption seems to decrease future levels of CO2. The regression analysis also indicates an inverted U-shaped relationship between growth and CO2, in line with EKC. Also, Charfeddine & Kahia (2020) investigate the impact of renewable energy and financial development on both CO2 emissions and growth for 24 Middle East and North Africa (MENA) states. The computed IRFs unveil a cumulative negative effect of renewables on CO2, suggesting that renewable energy sources may reduce CO2 pollution.

Moreover, some authors assess the impact of (non-) renewable energy consumption and output on CO2 pollution using the EKC framework for European Union (EU) states. As such, the results of Bölük & Mert (2014) for a sample of 16 EU countries indicate that the consumption of renewables has a positive impact on CO2 emissions, while the EKC hypothesis is not validated. Conversely, based on a sample of 27 developing and developed EU states, the findings of [START_REF] López-Menéndez | Environmental costs and renewable energy: Re-visiting the Environmental Kuznets Curve[END_REF] show, on the one hand, that renewables have a negative effect on greenhouse gas emissions. On the other hand, the results suggest that the EKC hypothesis may be at work for those economies which exhibit high intensity with respect to renewable energy sources. Likewise, for a sample of EU economies, [START_REF] Dogan | Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy[END_REF] show that renewable (non-renewable) energy decreases (increases) the CO2 emission, and the EKC hypothesis is supported. As an empirical methodology, the authors employ panel data techniques robust in the presence of cross-sectional dependence. 7Bearing in mind the present study's objective, we previously review some studies that directly or indirectly tackle the effects of output, urbanization, and (non-) renewable energy, among others, on environmental degradation. However, given that we aim at addressing the potential endogenous behavior between variables and, thus, consistent with the recursive order that we impose among them (see subsection 4.2 for details), the study could also be linked with the strand of research that examines the relationship between (i) output and urbanization (see e.g. [START_REF] Brückner | Economic growth, size of the agricultural sector, and urbanization in Africa[END_REF][START_REF] Bakirtas | The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries[END_REF]; among others), (ii) output and (non-) renewable energy (see e.g. [START_REF] Sadorsky | Renewable energy consumption and income in emerging economies[END_REF][START_REF] Salim | Why do some emerging economies proactively accelerate the adoption of renewable energy[END_REF][START_REF] Liu | The Relationship between Energy Consumption and Output: A Frequency Domain Approach[END_REF][START_REF] Apergis | Renewable Energy, Output, Carbon Dioxide Emissions, and Oil Prices: Evidence from South America[END_REF][START_REF] Doğan | The Energy Consumption and Economic Growth in the E7 Countries: Cointegration in Panel Data with Structural Breaks[END_REF], (ii) urbanization and (non-) renewable energy (see e.g. [START_REF] Shahbaz | Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia[END_REF]Sadorsky, 2014b;[START_REF] Wang | Effects of urbanisation on energy consumption in China[END_REF][START_REF] Kurniawan | Coal consumption, urbanization, and trade openness linkage in Indonesia[END_REF]among others), and as well the papers that focus on efficiency of (non-) renewable energy (see e.g. [START_REF] Aldea | The Renewable Energy Development: A Nonparametric Efficiency Analysis[END_REF][START_REF] Jebali | The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach[END_REF][START_REF] Gökgöz | Energy security and renewable energy efficiency in EU[END_REF]among others).

III. STIRPAT framework, research strategy, and data

STIRPAT framework

STIRPAT is an analytical framework introduced in the literature by [START_REF] Dietz | Rethinking the environmental impact of population, affluence and technology[END_REF][START_REF] Dietz | Effects of population and affluence on CO2 emissions[END_REF] as the stochastic counterpart of IPAT identity proposed by [START_REF] Ehrlich | Impact of population growth[END_REF].

According to the I=PAT accounting equation, the environmental impacts denoted by (I) are determined in a multiplicative way by demographic-economic forces such as population (P), affluence (A), and technology (T). Nonetheless, over the years, to meet the needs of different research questions the baseline IPAT/STIRPAT model has encountered many alternative specifications (see e.g. [START_REF] Kaya | Impact of Carbon Dioxide Emission Control on GNP Growth: Interpretation of Proposed Scenarios[END_REF][START_REF] Schulze | I = PBAT[END_REF][START_REF] Waggoner | A Framework for Sustainability Science: A Renovated IPAT Identity[END_REF][START_REF] Xu | ImPACTS identity of sustainability assessment[END_REF][START_REF] Martínez-Zarzoso | The impact of population on CO2 emissions: evidence from European countries[END_REF][START_REF] Lin | Analysis of the environmental impact of China based on STIRPAT model[END_REF]Shafiei & Salim, 2013; among others).

First, the classical IPAT equation written for panel data with observed countries over the period takes the following for Second, the stochastic counterpart of the above accounting identity is obtained by applying natural logarithm on equation (1). Also, along with this transformation, we approximate the environmental impacts with a well-known global pollutant, namely the CO2 emissions. Likewise, we proxy with the share of the urban population in total population (URB), with the gross domestic product (GDP), while it is captured through both energy intensity (EINT) and the share of renewable energy in total energy consumption (RENG). Subsequently, our modify STIRPAT model can be specified as follows

In the above equation, all the variables are expressed in natural logarithm form, while and captures the potential country-specific fixed effects and the error term, respectively.

Moreover, given that the affluence term is usually expressed via GDP, its square (cubic) term into the equation allows for testing the well-known EKC hypothesis in its traditional (extended form). Indeed, the same holds for any explanatory factor, namely adding higherorder polynomial terms, allows for testing a potential nonlinear effect of the respective variable on environmental degradation (e.g. the urbanization-EKC hypothesis).

Methodology

To explore the CO2 responsiveness to other system variables shocks, we draw upon the novel panel VAR methodology. In this regard, we follow the work of [START_REF] Love | Financial development and dynamic investment behavior: evidence from panel VAR[END_REF] and [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF] and estimate a homogeneous panel VAR model using the GMM approach. Indeed, this technique gives us the possibility to treat all the variables endogenously and also to account for the unobserved individual heterogeneity.

The reduced-form specification of a homogeneous panel VAR with individual fixed effects can be written as follows

Where represents the vector of our four stationary endogenous variables, namely the GDP, URB, EINT, RENG, and CO2, and stands for associated matrix polynomial in the lag operator (i.e. the autoregressive structure). is the vector of constants, while and denotes the vector of unobservables country-specific characteristics and idiosyncratic errors, respectively. The unobservables may capture the cultural, institutional, and historical individual country characteristics that are time-invariant. Likewise, we assume that the vector of idiosyncratic errors possesses the following features: , and , . Put differently, the innovations have zero first moment values, constant variances, and do not exhibit individual serial and cross-sectional correlation (see [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF].

Furthermore, in line with [START_REF] Holtz-Eakin | Estimating vector autoregressions with panel data[END_REF], the panel VAR model described above assumes that the parameters are common across all panel members [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF]. Indeed, this seems to be quite a strong restriction that may not hold when working with a large number of countries, which are prone to exhibits certain particularities. Thus, the country-specific fixed effects are introduced into the model to overcome the parameters'

homogeneity assumption. In this regard, the model may be estimated via the fixed effects or ordinary least squares approach, but the coefficients are likely to suffer from Nickell's bias [START_REF] Nickell | Biases in Dynamic Models with Fixed Effects[END_REF]-when estimating dynamic panels, the fixed-effects are correlated with the regressors, given the lags of endogenous variables [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF]. To alleviate this issue, we use the Helmert procedure described in Arrelano & Bover (1995), and remove the mean of all future available country-time observations, by applying forward meandifferencing (orthogonal deviations). Also, in this manner, we refrain from eliminating the orthogonality between transformed variables and lagged regressors. Consequently, the coefficients are consistently estimated by GMM, using instruments the lags of independent variables [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF].

Data

The study concentrates on 68 countries classified by World Bank (2017) as economies with low and lower-middle income. The list of countries included in the analysis, grouped by geographic region, is displayed in Table 1 in the Supplementary Material. Moreover, the data are annual and cover the period from 1992 to 2015, while the sample is constructed according to data availability and in such a way to omit to deal with missing observations for the main variables. Also, by focusing on this period, we avoid the instabilities triggered by the fall of the Communist Bloc and the end of the Cold War, which may equally distort our analysis.

On the one hand, our primary data source is the World Bank, given that four out of five variables included in the empirical analysis come from World Bank Indicators (WDI, 2018). These variables are the GDP (constant 2011 international $, purchasing power parity), EINT (energy intensity of GDP), URB (urban population as % of the total population), and RENG (renewable energy consumption as % of total final energy consumption).

On the other hand, the data for CO2 emissions (kton per year) are collected from Janssens-Maenhout et al. ( 2017), Emissions Database for Global Atmospheric Research (EDGAR). For the baseline model, we use the aggregate CO2 emissions, computed as the sum of emissions from transport, other industrial combustion, buildings, non-combustion, and power industry sector. Nonetheless, in the heterogeneity section, we estimate the model for each sector separately, using disaggregated CO2 emissions. Furthermore, to capture the overall dynamics' magnitude between variables (especially between CO2 and GDP), we refrain from working with their per capita versions. Indeed, this allows us to further investigate, in the robustness section, if potential changes in the population alter the baseline findings. Also, for modeling purposes, all the variables are express in natural logarithm.

Table 2 and Table 3 in the Supplementary Material illustrates the variables definition and their descriptive statistics before applying any transformation.

IV. Empirical results

Some preliminary data evaluations

Prior to modeling the dynamic relationship between variables, we check some univariate properties of our data, such as the cross-sectional dependence, the critical assumption of stationarity required by a stable VAR model, and the potential cointegration of variables.

First, we check the presence of cross-sectional dependence by employing the Breusch-Pagan (1980) LM, [START_REF] Pesaran | General diagnostic tests for cross section dependence in panels[END_REF] 1 show that all variables are characterized by cross-sectional dependence. Second, taking into account the presence of cross-sectional dependence and the large dimension of N (i.e., N=68 and T=24), we employ the [START_REF] Harris | Inference for unit roots in dynamic panels where the time dimension is fixed[END_REF] panel unit root test. In particular, this test allows us to alleviate the effects of cross-sectional dependence by subtracting the cross-sectional means from the variables, while imposing small-sample adjustment to T. Besides, bearing in mind that unit root/stationarity tests are usually sensitive to the number of lags included in the equation, we also consider the [START_REF] Pesaran | A simple panel unit root test in the presence of cross section dependence[END_REF] CADF test by augmenting the equation with one and two lags, respectively. Also, for both tests, we include in the equation a constant and a trend for the variables in levels, whereas only the constant for their first difference. Table 2 andTable 3 show the associated results. Overall, we can observe that all variables are stationary on their first difference and integrated of order one in levels, with the notable exception of URB and CO2 for [START_REF] Pesaran | A simple panel unit root test in the presence of cross section dependence[END_REF] test augmented by one lag. Third, given that the stationarity analysis suggests mixed results, especially for URB and CO2 variable, and to be sure that variables do not exhibit a long-term relationship, we check for a potential cointegration between variables. Indeed, if the variables are cointegrated in levels, only taking their first difference to satisfy the stationarity condition of the VAR model would eventually bias the estimates. To this end, we employ the error-based panel cointegration tests of [START_REF] Westerlund | Testing for error correction in panel data[END_REF], which allow us to control for the presence of crosssectional dependence via the bootstrap procedure. The findings depicted in Table 4 show that the null hypothesis of no cointegration is strongly accepted across all four tests when using both 100 and 800 replications for the bootstrap procedure. Consequently, estimating the panel VAR by differencing the data seems to be the most appropriate decision in our case, since the model will be consistent, and the inference will hold. Besides, taking the first difference of the log-transformed data facilitate the modeling between variables by allowing us to work with their growth rates.

Identification and estimation of the structural panel VAR model

Identification

A crucial aspect of the VAR approach involves the assumptions imposed to estimate the associated system of simultaneous equations consistently. Indeed, converting the classical VAR into a structural VAR (SVAR) approach by setting specific restrictions, allow us to achieve the necessary causal inference, and have a meaningful economic interpretation of the parameters. In other words, the identification in SVAR of all structural parameters requires that some theory-based economic restrictions are imposed. In doing so, we draw upon a recursive panel SVAR model, meaning that we do not impose any restriction on the matrix that captures the impact effects8 , i.e. we use exclusion restrictions. Effectively, this can be done by imposing a particular causal order between variables, which plays a vital role in the computation of both the Cholesky decomposition of the innovations' variance-covariance matrix and the IRFs [START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF]. Correspondingly, we further detail the rationale behind the causal ordering we impose on the systems' variables.

First, according to the EKC hypothesis and STIRPAT framework, we argue that the GDP exhibits the highest levels of exogeneity, while CO2 the highest level of endogeneity.

More specifically, we consider that CO2, namely the variable ordered last into our transmission channel, responds more quickly following exogenous shocks to economic activity. Thus, the exogenous structural disturbances to output have both a contemporaneously and lagged impact on the CO2. Opposite, the GDP being ordered first into the system may have only a delayed response to any exogenous shocks to CO2 (i.e. is restricted to respond within the period).

Second, the three remaining variables, namely the urbanization, energy intensity, and renewable energy, enter the transmission channel at the right-(left) side of the GDP (CO2).

The reasoning for this choice is straightforward. On the one hand, as previously mentioned, the related literature ranks these factors among the most important determinants of CO2 emission. On the other hand, regarding the sample's particularities (discussed more is detailed in the Introduction section), they may easily explain the ongoing urbanization process, along with the efforts made by developing economies to combat climate change. In this manner, for example, the active involvement in the CDM of the Kyoto Protocol may mirror some of the countries' efforts aiming to reduce environmental degradation. However, what remains ambiguous so far, is the causal ordering of these factors in the transmission channel, given that it may influence our results. Indeed, we may have less information than the underlying economic foundation of CO2-GDP nexus, but the economic intuition could equally help us in this regard.

Subsequently, we assume that any exogenous shocks to output may impact the urbanization degree, which may further influence the energy intensity, renewable energy share, and the CO2. The same logic is preserved for the other variables, namely the external disturbances to energy intensity may affect renewables, which in turn may reflect on the CO2 emissions levels. Thus, the CO2 emissions are ultimately allowed to react within the period to any exogenous shocks to the other system's variables. In contrast, all the variables respond within the period following exogenous shocks to output.

Taken collectively, our previous economic rationale may be linked with the fast growth of developing economies, which may impact the scale of the urbanization process. As a result, we expect that the intensification of economic processes to increase the energy use, but at the same time, at least from a sustainability perspective, to foster the advance in energy efficiency and renewables. Notably, we postulate that efforts to promote energy efficiency and the renewable energy share are a by-product of the pressures caused by urbanization and, in any case, economic activity. Indeed, these efforts may also suggest the countries' willingness to get involved in pollution mitigation activities.

Nonetheless, the [START_REF] Granger | Investigating causal relations by econometric models and cross-spectral methods[END_REF] causality Wald test can also help us verify the underlying economic reasoning. In this regard, we note that the associated results depicted in Table 5 in the Supplementary Material overwhelmingly endorse the assumed transmission channel between variables. Specifically, the findings show that each factor separately Granger-causes the CO2 (except the renewable energy), while all four variables jointly Granger-cause the CO2. Besides, GDP, along with all the excluded variables taken together, Granger-cause the equation variable. Also, as a counterfactual, the causality towards the GDP runs only from the renewable energy share, but its statistical significance is considerably low.

Estimation

A key primary step in estimating the panel SVAR involves setting the optimal lag length of the model. Therefore, we choose the appropriate order of our panel SVAR, according to moment and model selection criteria (MMSC) proposed by [START_REF] Andrews | Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models[END_REF] based on [START_REF] Hansen | Large sample properties of generalized method of moments estimators[END_REF] J statistic. Table 6 in the Supplementary Material presents the associated results. Overall, the MMSC statistics indicate that the first-order panel SVAR is the most suitable, compared with the other two alternatives, namely the second-and third-order specifications.9

Accordingly, we estimate the first-order panel SVAR model through the GMM estimator. The results displayed in Table 8 in the Supplementary Material show the following.10 On the one hand, the output has a significant positive one-lag impact on itself, urbanization, and CO2, while a negative one on the energy intensity and renewable energy.

On the other hand, urbanization, renewable energy, and CO2 respond positively and significantly to a one-lag impact of urbanization. Moreover, the energy intensity seems to have a significant increasing delayed effect only on CO2 emissions. Also, given that renewable energy displays a significant negative one-lag impact on GDP, there is a negative feedback effect at work between the indicators.

The first-order panel SVAR-GMM findings give us an original resolution on the dynamic behavior between variables. Indeed, it also represents the leading basis for the crucial IRFs and forecast-error variance decompositions (FEVDs), which may be retrieved following its multivariate estimation. As such, being mainly interested in the CO2 response following shocks to other system variables, let us now discuss the associated orthogonalized cumulative IRFs11 and FEVDs, both generated based on 1000 Monte Carlo simulations, and depicted in Figure 1 and Table 5, respectively.

First, the IRFs indicate that one standard deviation exogenous positive shock to GDP triggers a persistent increase in CO2 emissions, both immediately and cumulated over the twenty years horizon. More specifically, the CO2 increases with about two percentage points (pp) on impact, following a positive shock to output. Although it shows a smooth evolution over time, the upward trend seems to be slightly bent to the right. Likewise, its magnitude almost triples in the long-run, reaching and even exceeding five pp. From an economic perspective, these findings suggest that developing countries under examination are situated on the EKC's growing side. However, depending on their economic context, the results may suggest that they are likely to reach the crucial GDP turning point in the long-run sooner or later. Overall, these findings are expected, considering that the developing countries exhibit among the highest GDP growth rates, which are often incompatible with lower levels of environmental pollution. For example, a positive exogenous technology shock may induce the well-known phenomena of "catch-up growth" and, thus, trigger the intensification of industrial processes, which would eventually reflect at first in higher pollution. Indeed, as the nations' economic welfare grows, they can more easily acquire advanced green technologies, which, along with the increase in household income, may equally promote environmental sustainability. Thus, over time these may help in flattening the pollution curve. In this fashion, judging from the perspective of a future potential validity of EKC, our findings may complement the work of Liddle ( 2013 Second, one standard deviation permanent positive shock to urbanization triggers an increase in CO2, which may attain almost five pp after twenty years from impact. Also, we note that the cumulated effect becomes statistically significant only after two years. This may imply that the adverse effects of the urbanization process are not reflected immediately on the environment, but rather with a delay. Additionally, the overall pattern of the CO2 response seems to mirror to a certain extent the CO2 response to GDP shocks, suggesting that states will be able to reach the urbanization threshold that would lead to a decrease in CO2 in the future. In this regard, the results are similar to studies that unveil a bell-shaped pattern between urbanization and environmental degradation (see e.g. [START_REF] Martínez-Zarzoso | The impact of urbanization on CO2 emissions: Evidence from developing countries[END_REF][START_REF] Lin | Energy and carbon intensity in China during the urbanization and industrialization process: A panel VAR approach[END_REF][START_REF] Chen | Impact of urbanization on CO2 emissions and energy consumption structure: a panel data analysis for Chinese prefecture-level cities[END_REF].

Third, a positive one standard deviation shock to energy intensity raises the CO2 by about three pp on impact. As well, the cumulate CO2 response exhibits a sharp increase over roughly the first one year and a half, then stabilizes and very slowly increases until it reaches nearly four pp following a permanent shock to energy intensity. This result is in line with the study of Sadorsky (2014a), [START_REF] Shahbaz | Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries[END_REF], and [START_REF] Naminse | Economic Growth, Energy Intensity, and Carbon Dioxide Emissions in China[END_REF], but opposes the one of Martínez-Zarzoso & Maruotti (2011).

Fourth, in terms of the overall pattern displayed, the response of CO2 following a positive exogenous shock to renewables seems quite similar to the cumulate effect produced by an exogenous shock to energy intensity. However, one standard deviation positive shock to RENG induces an opposite effect, namely a decrease of about two pp in CO2 at the moment of the impact. Moreover, the cumulate magnitude of the negative response diminishes significantly after the initial impact, and then stabilize and gravitate around the same value for the rest of the period. We note that the permanent shock, projected twenty years ahead, still causes a drop in CO2, even if the magnitude is slightly lower (i.e. around one and a half pp). These findings corroborate the ones of López-Menéndez et al. ( 2014 Finally, the CO2 increases at about nine pp in the aftermath of a permanent exogenous positive shock to itself. However, the increasing of the cumulative response in the long-run is almost imperceptible, pointing out a low magnitude of CO2 persistence (see the top-left plot in Figure 1). Overall, this finding supports the one of Martínez-Zarzoso & Maruotti (2011), Sadorsky (2014a), and Acheampong (2018), among others, who find persistence effects in CO2 emissions.

Concerning FEVDs, on the one hand, as expected, the largest share of the variables' variation is explained by their dynamics (see the principal diagonal of Table 3). Furthermore, energy intensity seems to explain, twenty years ahead, about 9.99% of the variation in CO2, followed by renewable energy (4.06%), output (3.99%), and urbanization (2.01%). Indeed, the findings seem to uphold the energy as the primary contributor of CO2 in our group of developing economies. Also, the results indicate that the renewables have a more significant long-term contribution to CO2, compared to output and urbanization. Overall, this is a quite exciting and promising result, which may suggest, yet again, that these states have made substantial efforts to switch towards more environmentally friendly energy sources, and, among others, that the CDM related projects have had the desired outcomes. Likewise, this result is also supported by the large share of renewable energy variation, following a shock to energy intensity.

On the other hand, we remark that the external shocks to output explain, twenty years ahead, a large share of variation in the other macro factors. These findings are also not unexpected, considering that the exogenous disturbances propagate first through output an then to its related macro components. Besides, it seems that any exogenous shocks to the remaining column variables, do not exhibit a large magnitude in explaining the fluctuations in the row variables.

V. Robustness

We assess the robustness of our baseline SVAR specification in several ways. Also, we focus on reporting the associated findings with respect to the crucial IRFs, retrieved after running the panel SVAR-GMM model.

Alternative ordering

Considering that we use a recursive ordering strategy to achieve identification in our SVAR, we check the stability of the underlying economic rationale by implementing alternative transmission schemes. On the one hand, we check the soundness of our economic intuition behind the ordering of the factors positioned at the right-side (left-side) of GDP (CO2) within the transmission scheme, namely URB, EINT, and RENG. In this manner, we switch their initial position by running a five-time rotation between them, until we consider all available options. On the other hand, we order each of these three factors at the top of the transmission channel, namely before the output. Therefore, we can observe if changing the variable which exhibits the highest level of exogeneity alters the baseline findings. It should be mentioned that these new restrictions imposed among the system variables are even more consistent with the literature that positions urbanization, energy intensity, and renewables as the main determinants of CO2.

As shown by Figure 3 in the Supplementary Material, using distinct ordering scenarios does not qualitatively alter the baseline findings. Indeed, as expected, small changes in the magnitude of the responses are present. In this fashion, we note that a more visible difference is at work for CO2 response following external shocks to EINT, especially in the model where EINT is ordered first into the transmission channel. In particular, the cumulative response of CO2 due to a positive shock to EINT seems to follow a downward trend after reaching the peak, that is, approximately after one year and a half after the impact. One possible explanation could be related to the fact that manifesting the highest level of exogeneity, the effect of energy intensity on CO2, does not also include the effect of disturbances to GDP and urbanization. As such, the energy intensity may appear much lower, thus, having a lower impact on CO2.

Altering the sample

To check whether our baseline findings are robust under certain economic or political distress conditions, we account for some well-known related events which can be seen in relation to both T and N dimensions of our sample. First, to control for the potential (delayed) effects of the global financial crisis, we restrict the period of analysis to (1992-2010) 1992-2008. Furthermore, the exclusion of the period following 2008 coincides with the starting point of the Kyoto Protocol's first commitment phase (i.e. 2008-2012). Thus, if there were specific changes in the environmental behavior of developed countries as a result of potential pressures to achieve their binding targets, we would expect them to reflect on developing countries as well. Second, we drop the period immediately following the end of the Cold War, namely 1992-1996, since the economies affected by this quite prominent geopolitical distress could have encountered difficulties in terms of economic recovery. Indeed, if our assumption hold, the fluctuations in their primary macro aggregates may alter the baseline findings. Third, having in mind the Arab Spring, which involves several developing states, we also check whether its effects reflected on our results. In doing so, we drop from the sample all the economies affected to some extend by this major political unrest episode.

Finally, it is generally recognized that the petroleum industry has major implications on the environment. In this fashion we exclude all states ranked by the Central Intelligence Agency (CIA)12 among the top thirty economies regarding the crude oil exports. Overall, the associated cumulative IFRs, depicted in Figure 4 in the Supplementary Material, shows that independent of the restriction imposed on the sample, the baseline results are preserved both in terms of pattern and statistical significance.

Exogenous control factors

We exogenously introduce, along with the main SVAR endogenous variables, several additional explanatory factors into the model to control for a potential bias caused by omitted variables. These variables are related to changes in the size of the economy 13 (population), sectoral output composition (agriculture, industry, and services as % of GDP), trade (trade as % of GDP), environmental prospects (forest rents as % of GDP), external financing (remittances in % GDP), and private sector financial conditions (domestic credit to the private sector as % GDP) (see Table 2 andTable 3 in the Supplementary Material for variables definition and descriptive statistics, respectively). Also, to maintain the stability conditions of the SVAR model and consistency between variables, we transformed them into growth rates, by taking the first difference of their log-transformed values. Overall, the cumulative IRFs illustrated by Figure 5 in the Supplementary Material indicate that the findings are comparable with those of the baseline model, especially judging based on the significance and long-term trajectory of CO2 response due to different innovations shocks.

IV. Heterogeneity

This section explores the sensitivity of CO2 responses following external shocks to other factors, depending on the income level group and the ratification or ascension date of states to the Kyoto Protocol.

The level of economic development

The economic development stages that a country crosses imply that different effects such as scale, structural, or technological, are at work during different periods and may cause substantial fluctuations in environmental conditions (see e.g. [START_REF] Grossman | Environmental impacts of a North American free trade agreement[END_REF]. Thus, to explore the possible difference of CO2 responses with respect to countries' income level, we construct two sub-samples of low and lower-middle income economies, based on the World Bank classification (2017) (see Table 4 in the Supplementary Material for summary statistics). Panel (a) and panel (b) in Figure 6 in the Supplementary Material depicts the cumulative IRFs for both income sub-samples. First, as expected, following external shocks, the GDP exhibits a positive effect on CO2 but with higher magnitude in lowermiddle income economies. Moreover, the cumulated CO2 response over the first two years displays a sharp increase in lower-middle income states, compared to the low income ones.

Likewise, the increasing long-run trajectory seems to be more accentuated in wealthier countries.

capita terms, computed as the difference of log-transformed values). As shown by the panel (e)-(f) in Figure 4 in the Supplementary Material, the cumulative IRFs are almost identical to those revealed by the baseline model.

Second, CO2 emissions significantly and positively react due to innovations shocks to urbanization only in low income countries, and with a delay of around four years. Besides, the CO2 response path tends to display a bell-shaped pattern in the long-term, supporting the urbanization-EKC hypothesis. Conversely, the lack of significance in the lower-middle income countries may suggest that the urbanization process is at a more advanced stage, leading to a more abundant flow of sophisticated ecological practices that help in combating pollution.

Third, following a positive shock to energy intensity (renewables), the CO2 emissions respond in a positive (negative) way in both income groups. As well, the cumulated effect shows a sharp increase after the impact in both sub-samples (except CO2 response following renewables shocks in low income states, where the increase seems to be smoother and lower in magnitude). However, starting approximately with the second year, the IRFs indicate that the cumulated effect stabilizes and preserves its positive linear trajectory up to twenty years in low income states. In contrast, it follows a monotonically increasing pattern in lowermiddle income ones. On the whole, this may confirm that in countries where the industrialization process is more pronounced, it also becomes more challenging to maintain low levels of pollution.

Finally, an exogenous positive shock to CO2 leads to an increase in its levels, and the magnitude of impact seems to be comparable in both groups. Nonetheless, in low income countries, the cumulated response starts to decline after the impact, and then quickly readjust (after about two years) to a linear path that remains stable in the long-run. Opposite, in lowermiddle income economies, the cumulated response keeps an increasing trajectory over the twenty years horizon.

The Kyoto Protocol status

We split the main sample taking into account the date of the ratification/accession of individual states to the Kyoto Protocol based on the United Nations Treaty Collection14 . Thus, the first sub-sample (Kyoto Protocol group A) comprises the nations which ratified or acceded before the year in which it entered into force (i.e. 2005), while in the second group (Kyoto Protocol group B) we include the remaining countries for which the ratification/accession date is 2005 onwards (see Table 4 in the Supplementary Material for

VII. Sectoral CO2 emissions

To have a more in-depth look at the potential changes in pollution dynamics in the relationship with our macro indicators, we substitute aggregated CO2 with its sector-specific counterparts (see Table 2 andTable 3 in the Supplementary Material for variables definition and summary statistics, respectively). 15 In doing so, we estimate the GMM-SVAR model considering the CO2 related to each of the following sectors: transport, buildings, other industrial combustion, non-combustion, and power industry. Figure 2 displays the CO2 sector-specific cumulative orthogonalized IRFs.

First, considering the presumed differences in the magnitude, an external shock to output and urbanization has a cumulative significant positive effect on CO2 from transport, buildings, and non-combustion sector-with the notable exception of CO2 from buildings which do not significantly respond to urbanization disturbances. Besides, the significant positive cumulated paths over the twenty-year horizon suggest that the related EKC hypothesis may be at work in the very long-run, both for output and urbanization. Also, in line with the baseline findings, the CO2 emissions respond with a delay of about two years following urbanization shocks. On this last point, given that the construction industry has a substantial contribution to the urbanization process, the lack of significance of the buildingsrelated CO2 response following external shocks to urbanization may indicate that a substantial number of green projects are implemented in this sector, thus, helping to reduce the associated pollution.

Second, an exogenous increase in output and urbanization reduce the CO2 from other industrial combustion and power industry sector both on impact and cumulated over twenty years. However, industrial combustion-and power industry-related CO2 emissions do not respond immediately to output shocks, but rather with roughly ten and eighteen years of delay. Moreover, regarding the disturbances to urbanization, they seem to cause a U-shaped pattern in cumulative CO2 emissions' evolution, opposite to the bell-shaped pattern postulated by the traditional EKC hypothesis.

Third, the CO2 related to each of the five sectors react positively (negatively) to one standard deviation energy intensity (renewables) shocks, both on impact and cumulated over the twenty years, thus, backing up the baseline findings. However, the effect of renewables 15 We test the cross-sectional dependence and stationary properties of CO2 sector variables using the same tests as for the baseline model. The findings confirm the presence of cross-sectional dependence, while the unit root test shows that variables are stationary in levels (see Supplementary Material Table 9-11). However, in empirical analysis, we use the first difference of the variables in order to work with growth rates and have all the variables in the system at the same level. on CO2 from non-combustion and power industry is not statistically significant. Indeed, these two similar results may go hand in hand, given that access to energy in developing countries is a significant issue, mainly alleviated, among others, by the transition to off-grid renewable energy systems [International Renewable Energy Agency (IRENA), 2015). More precisely, the off-grid renewables technologies (e.g. solar, micro-hydro, wind, biomass, among others), whose leading market is concentrated in developing economies, represent the more environmentally-friendly and cost-effective alternative to classical non-renewable energy sources, such as the fossil fuels used for electricity generation via combustion processes [see e.g. IRENA, 2015; Renewable Energy Policy Network for the 21st Century (REN21), 2015].

Additionally, the results also corroborate with the negative effect of output and urbanization on power industry-related CO2.

Fourth, an external increase in the sector-specific CO2 emissions triggers a statistically significant increase in its levels. At the same time, the magnitude at the moment of impact ranges from about 14.5 pp (CO2 from transport) to 30 pp (CO2 from other industrial combustion). Furthermore, the cumulated effect starts to decay immediately after the impact (except CO2 associated with other industrial combustion), and then quickly stabilizes and follows an almost linear path until the end of the analyzed period. In particular, the results may highlight, yet again, the inertial behavior of CO2 pollution levels.

Overall, the findings illustrate, on the one hand, the complexity of the relationship between sector-specific CO2 and the several related key economic aggregates, highlighting which sector is more likely to be associated in the future with higher pollution levels. On the other hand, the results strengthen the vital role of non-combustion energy sources and energy efficiency projects (e.g. the rapidly growing off-grid renewable systems, the use of sustainable technologies in the construction industry, among many others) in promoting green growth and urbanization, and ultimately in reducing the environmental degradation.

VIII. Conclusion and policy implications

This paper explored the impact of external changes in output, urbanization, energy intensity, and renewable energy on aggregated and sector-specific CO2, within a modified STIRPAT analytical framework. To this end, motivated by the potential endogenous behavior between variables, we employed the novel panel GMM-VAR technique for a rich sample of 68 developing states over 1992-2015.

The results showed, on the one hand, that an exogenous increase in output, urbanization, energy intensity and CO2 led to a significant increase in CO2, both on impact and cumulated over the twenty years horizon. Besides, the CO2 response following disturbances to output and urbanization, suggest that a threshold effect, compatible with the classical and urbanization EKC hypothesis, might be at work in the long-run for our group of countries. Conversely, we found that a positive shock to renewables cumulatively and significantly decreases the current and future levels of CO2. In sum, these findings may imply that in the context of rapid industrialization and urbanization, renewable energy is one of the most powerful tools in mitigating environmental degradation. Nonetheless, more considerable attention must also be paid to energy efficiency, especially as increasing it can further enhance the beneficial effects of renewable energy on the environment. These results are supported by several robustness tests, including an alternative Cholesky ordering of variables, when altering the sample, and controlling for a series of exogenous factors. On the other hand, the findings are found to be are sensitive concerning both countries' level of development and their Kyoto Protocol ratification/ascension status. Besides, the disaggregated CO2 analysis unveiled essential differences regarding the contribution of various sectors to the overall CO2 pollution. In particular, the results may suggest that the CO2 emissions related to transportation, construction, and non-combustion sector are more likely to increase in the future, compared to other industrial combustion and power industry sector.

The findings could be transposed in some valuable policy recommendations. First, developing countries should pay more attention to the implications that the process of urbanization, as well as the growth-promoting policies, have on CO2 pollution. Moreover, the urban planning and development policy requires an appropriate design to accommodate better any potential negative impacts on the quality of the environment. Second, although countries make outstanding efforts to invest as much as possible in renewable energy sources and minimize energy dependency, these investments should be continuously adapted to cope with the dynamics of their particular economic environment. Likewise, adequate monitoring during project implementation may increase their efficiency and signal beforehand any potential nonconformities. Third, to counterbalance and mitigate the overall pollution, additional efforts should be directed towards the sectors where CO2 emissions are more likely to increase. Finally, the ongoing international cooperation and assistance from developed nations may represent a central pillar in ensuring environmental sustainability in developing economies. Future work could consider a more detailed breakdown of energy sources in assessing their impact on CO2 (see e.g.

  ),[START_REF] Shahbaz | Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries[END_REF],[START_REF] Dogan | Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy[END_REF],[START_REF] Li | Environmental Kuznets Curve in China: New evidence from dynamic panel analysis[END_REF],[START_REF] Wang | Is there an Environmental Kuznets Curve for SO2 emissions? A semi-parametric panel data analysis for China[END_REF],[START_REF] Talbi | CO2 emissions reduction in road transport sector in Tunisia[END_REF],[START_REF] Naminse | Economic Growth, Energy Intensity, and Carbon Dioxide Emissions in China[END_REF],[START_REF] Pata | Renewable energy consumption, urbanization, financial development, income and CO 2 emissions in Turkey: Testing EKC hypothesis with structural breaks[END_REF], among others.

  ),[START_REF] Dogan | Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy[END_REF], and Charfeddine & Kahia (2020) while contrasting those ofBölük & Mert (2014).

  Figure 1Cumulative orthogonalized IRFs

  Figure 2 Cumulative orthogonalized IRFs: sectoral CO2 emissions (a) CO2 from transport (b) CO2 from buildings

Figure 1

 1 Figure 1Inverted roots of AR characteristics polynomial (GMM-SVAR)

  five-variable one lag panel SVAR is estimated by GMM, using first four lags of the variables as instruments. The country-specific fixed effects are removed during estimation via the Helmert transformation. Reported numbers display the coefficients of regressing the row variables on first lag of the column variables. Standard errors robust to heteroskedasticity and serial correlation in brackets. ***, **, *, denotes significance at the 1%, 5% and 10% level, respectively.

  Figure 2Orthogonalized IRFs

  Figure 3 Cumulative orthogonalized IRFs: robustenss (alternative ordering) (a) (b)

  Figure 4 Cumulative orthogonalized IRFs: altering the sample & variables in per capita terms (a) period: 1992-2008 (b) period: 1992-2010

  Figure 5 (Continued) (a) trade (b) forest rents

  Figure 6 Cumulative orthogonalized IRFs: level of income and the Kyoto Protocol status (a) low income economies (b) lower-middle income economies

Table 1 Cross-sectional dependence tests

 1 The[START_REF] Breusch | The Lagrange multiplier test and its applications to model specification in econometrics[END_REF] LM,[START_REF] Pesaran | General diagnostic tests for cross section dependence in panels[END_REF] scaled LM,Pesaran (2004) CD, and[START_REF] Baltagi | A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model[END_REF] Bias-Corrected (BC) scaled LM test. H0 is ''no cross-section dependence (correlation)''. P-values in brackets. ***, **, *, denotes significance at the 1%, 5% and 10% level, respectively.

	Test/Variable	CO2	GDP	EINT	RENG	URB
	Breusch-Pagan LM	34483.67*** 44405.60***	19907.45*** 18059.67***	43539.82***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	Pesaran scaled LM	477.134***	624.129***	261.184***	233.808***	611.303***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	Pesaran CD	141.464***	202.439***	55.523***	59.380***	110.153***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	BC scaled LM	475.655***	622.651***	259.705***	232.330***	609.824***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	Notes:

  We remove cross-sectional means and apply small sample adjustment to T. H0 is ''panels contain unit roots''. P-values in brackets. ***, **, *, denotes significance at the 1%, 5% and 10% level, respectively.

	Table 2
		Stationarity analysis I
	Test/		Harris-Tzavalis test
	Variable	Level (cons & trend)	Δ (cons)
		rho	p-value	rho	p-value
	GDP	0.748	(0.983)	0.235***	(0.000)
	URB	0.853	(1.000)	0.839**	(0.033)
	EINT	0.685	(0.258)	-0.007***	(0.000)
	RENG	0.675	(0.145)	-0.056***	(0.000)
	CO2	0.686	(0.271)	0.004***	(0.000)
	Notes:

Table 3 Stationarity analysis II

 3

	Test/
	Variable

Table 4 Panel cointegration tests

 4 H0 is ''no cointegration''. The equation includes the constant term, one lag, and one lead. The width of the Bartlett kernel window is set to three. ***, **, *, denotes significance at the 1%, 5% and 10% level, respectively.

			Westerlund (2007)
	Statistic	Z-value	Robust p-value	Z-value	Robust p-value
		bootstrap with 100 replications	bootstrap with 800 replications
	Gt	0.669	0.271	0.669	0.271
	Ga	13.271	1.000	13.271	1.000
	Pt	10.714	0.591	10.714	0.591
	Pa	9.670	0.684	9.670	0.684
	Notes:

Table 5 Twenty years horizon forecast-error variance decompositions

 5

	Response variables			Impluse variables
		GDP	URB	EINT	RENG	CO2
	GDP	99.36	0.21	0.19	0.19	0.02
	URB	12.31	87.54	0.02	0.01	0.09
	EINT	21.59	0.14	78.14	0.04	0.07
	RENG	0.89	0.37	8.17	90.47	0.08
	CO2	3.99	2.01	9.99	4.06	79.92
	Notes: The numbers (in percentages) show the variation in the row variable that is explained by
	the column variables.

  denotes that the respective country belongs to the low income group.

	Notes: EINTc	Energy intensity level of primary energy per capita computed as total primary energy supply over GDP measured in constant 2011 US dollars at PPP (MJ/$2011 PPP GDP)	Zambia Zimbabwe ⸙ Authors' computation based on the World Bank Indicators data (energy intensity level of primary energy and population: https://databank.worldbank.org/data/home.aspx)
		Antonakakis et al., 2017; Naminse &
	Zhuang, 2018). However, this is strictly conditioned by data availability, especially for this
	group of developing economies. As well, an analysis of the impact of various types of crises
	on CO2, by making use of complementary techniques such as the local projection method
	(see e.g. Jalles, 2019), could provide additional insights regarding the future behavior of CO2
	emissions.

⸙

Table 3 Descriptive statistics (full-sample)

 3

	Variable/Statistic	Mean	Std. dev	Median	Min	Max	Observations
			baseline analysis
	GDP	1.54e+11	5.49e+11	2.52e+10	1.43e+08	7.54e+12 1632
	URB	36.97067	15.85659	35.3225	6.288	83.679	1632
	EINT	8.732009	6.978321	6.149098	1.91032	57.98816 1632
	RENG	59.00502	29.2848	63.37218	0.600592	98.34261 1632
	CO2	45506.02	185879.9	4161.842	20.6217	2419637	1632
			robustness analysis
	GDPc	2815.427	2185.772	2085.198	180.4062	12152.17 1632
	CO2c	0.0008181 0.0012382 0.0003974 0.0000311 0.012404 1632
	POP	4.17e+07	1.38e+08	1.03e+07	74769	1.31e+09 1632
	AGRI	23.93044	12.40996	22.88478	2.706677	79.04237 1559
	IND	24.93198	10.76102	24.15961	2.073173	77.41367 1540
	SERVI	45.03406	9.706143	45.77348	12.43525	77.02007 1485
	TRADE	72.59632	32.94223	67.26805	0.1674176 311.3553 1565
	FRENTS	4.593966	6.052195	2.429323	0.0000	40.42677 1616
	REM	2072.407	6065.007	204.5626	0.0095628 70388.64 1340
	CREDIT	21.92812	18.57499	15.66249	0.4103563 114.7235 1521
			CO2 sector-specific analysis
	CO2_T	7117.286	20936.74	1244.581	15.26836	257301.2 1632
	CO2_B	5187.247	17222.53	561.6689	0.407881	180733.1 1632
	CO2_NC	5724.411	18873.11	476.8578	0.046156	206595.6 1632
	CO2_OIC	10202.23	42005.12	636.099	0.0000	529105.3 1608
	CO2_PI	17424.87	91486.77	608.0745	0.0000	1245902	1632

Table 4 Descriptive statistics (sub-samples)

 4

	Variable/Statistic	Mean	Std. dev	Median	Min	Max	Observations
			low income economies
	GDP	1.97e+10	2.16e+10	1.36e+10	5.13e+08	1.53e+11	576
	URB	29.17297	11.52414	30.2355	6.288	59.632	576
	EINT	11.83926	8.449231	9.401523	1.91032	57.98816	576
	RENG	81.83385	14.57414	86.66449	40.46676	98.34261	576
	CO2	2391.314	2914.064	1306.94	45.36593	18988.19	576
			lower-middle income economies
	GDP	2.27e+11	6.71e+11	4.27e+10	1.43e+08	7.54e+12	1056
	URB	41.22395	16.2794	40.0925	12.977	83.679	1056
	EINT	7.037145	5.31305	5.425794	1.992982	38.33533	1056
	RENG	46.55294	27.75466	51.01571	.600592	95.85808	1056
	CO2	69023.13	227689	8254.396	20.6217	2419637	1056
			Kyoto Protocol status group A
	GDP	1.90e+11	6.53e+11	2.88e+10	1.43e+08	7.54e+12	1104
	URB	38.34527	17.04287	36.327	6.288	83.679	1104
	EINT	8.284522	6.528194	5.964056	1.91032	57.98816	1104
	RENG	53.47162	30.03119	57.21445	.600592	97.29142	1104
	CO2	59523.13	222856.2	5533.39	20.6217	2419637	1104
			Kyoto Protocol status group B
	GDP	7.91e+10	1.77e+11	2.00e+10	6.37e+08	9.47e+11	528
	URB	34.09649	12.56938	33.7425	9.585	65.526	528
	EINT	9.667664	7.759983	6.990818	2.056564	50.13474	528
	RENG	70.57486	23.83922	78.07493	5.554171	98.34261	528
	CO2	16197.52	41380.69	2583.84	43.01915	227542	528

Table 5 Panel SVAR-Granger causality Wald test

 5 Excluded variable does not Granger-cause equation variable'', while according to H1 ''Excluded variable Granger-causes equation variable. ***, **, *, denotes significance at the 1%, 5% and 10% level, respectively.

	[Equation] \ Excluded variable	chi2	df	prob > chi2
	[GDP]
	URB	1.083	1	(0.298)
	EINT	1.531	1	(0.216)
	RENG	2.812*	1	(0.094)
	CO2	0.469	1	(0.493)
	ALL	5.921	4	(0.205)
	[URB]
	GDP	10.608***	1	(0.001)
	EINT	0.167	1	(0.683)
	RENG	2.247	1	(0.134)
	CO2	1.989	1	(0.158)
	ALL	16.152***	4	(0.003)
	[EINT]
	GDP	22.044***	1	(0.000)
	URB	0.149	1	(0.699)
	RENG	0.563	1	(0.453)
	CO2	0.805	1	(0.370)
	ALL	23.092***	4	(0.000)
	[RENG]
	GDP	11.287***	1	(0.001)
	URB	5.934**	1	(0.015)
	EINT	2.131	1	(0.144)
	CO2	1.273	1	(0.259)
	ALL	23.727***	4	(0.000)
	[CO2]
	GDP	4.828**	1	(0.028)
	URB	15.787***	1	(0.000)
	EINT	6.179**	1	(0.013)
	RENG	1.313	1	(0.252)
	ALL	22.787***	4	(0.000)
	Notes: H0 is ''

Table 6 Panel SVAR selection order criteria

 6 Model and moment selection criteria are computed using the first four lags of variables.

	Lag	CD	J	J p-value	MBIC	MAIC	MQIC
	1	0.988	70.449	0.019	-270.824	-25.550	-117.853
	2	0.988	48.099	0.033	-179.417	-15.900	-77.435
	3	0.988	17.601	0.345	-96.156	-14.398	-45.166
	Observations 1224
	Panels	68
	Notes:

Table 7 Model stability condition

 7

	Eigenvalue
	Real	Imaginary	Modulus
	0.940	0	0.940
	0.372	0	0.372
	-0.081	0	0.081
	-0.045	0	0.045
	0.022	0	0.022

Notes: All the eigenvalues lie inside the unit circle. The GMM panel SVAR model satisfies stability condition.

Table 8 First-order panel SVAR-GMM estimates

 8

		Response to
	Response of	GDPt-1	URBt-1	EINTt-1	RENGt-1	CO2t-1
	GDP	0.3746***	-0.2343	0.0190	-0.0292*	0.0083
		(0.0436)	(0.2251)	(0.0154)	(0.0174)	(0.0122)
	URB	0.0066***	0.9398***	0.0002	0.0010	0.0011
		(0.0020)	(0.0241)	(0.0007)	(0.0007)	(0.0007)
	EINT	-0.2868***	0.1219	-0.0517	0.0271	0.0246
		(0.0610)	(0.3158)	(0.0427)	(0.0362)	(0.0274)
	RENG	-0.1375***	0.4409**	-0.0481	-0.0618	-0.0263
		(0.0409)	(0.1810)	(0.0329)	(0.0497)	(0.0233)
	CO2	0.1628**	1.9181***	0.1078**	0.0536	0.0072
		(0.0741)	(0.4827)	(0.0433)	(0.0468)	(0.

https://population.un.org/wup/publications/files/wup2014-highlights.pdf

https://wedocs.unep.org/bitstream/handle/20.500.11822/22149/1_Gigaton_Third%20Report_EN.pdf?sequence =1&isAllowed=y

The term "developing" is used with double connotation, meaning that it refers to both developing and emerging countries.

We note that the studies that comprise, along with the output, both urbanization and (non-) renewable energy as explanatory factors of environmental degradation, are included in the category that we consider most suitable.

These findings are also partially supported by the more recent study of[START_REF] Inglesi-Lotz | The role of renewable versus non-renewable energy to the level of CO 2 emissions a panel analysis of sub-Saharan Africa's Βig 10 electricity generators[END_REF] for a sample that comprises the top 10 electricity generators states from Sub-Saharan Africa. Specifically, the results show that renewable (non-renewable) decreases (increases) the CO2, but the validity of the EKC hypothesis over the period 1980-2011 is not supported.

The matrix of impact effects or impact multipliers matrix, stands for the matrix that contains the immediate responses of the variables following a structural shock.

Along with MMSC (i.e. Bayesian, Akaike, and Hannan-Quinn information criterion) also the overall coefficient of determination (CD) which shows the share of the variation explained by the model and[START_REF] Hansen | Large sample properties of generalized method of moments estimators[END_REF] J statistic of over-identifying restrictions are reported (see[START_REF] Abrigo | Estimation of Panel Vector Autoregression in Stata[END_REF]. However, we rely predominantly on MMSC in choosing the optimal lag length, given that the[START_REF] Hansen | Large sample properties of generalized method of moments estimators[END_REF] J statistic has no correction for the degrees of freedom and, thus, may provide biased results. Also, we mention that the chosen model accepts Hansen's overidentification restriction at a 1% level of significance.

Post estimation, we examine the stability condition of the panel SVAR-GMM model. As such, we note that all eigenvalues lie inside the unit root circle, proving that the model is correctly specified and exhibits a high accuracy (see Table7and Figure1in the Supplementary Material).

We also recovered the simple orthogonalized IRFs, given that they are useful in evaluating the overall stability of our model. In this regard, Figure2in the Supplementary Material shows that the CO2 responses move towards zero over time, supporting both the variables' stationarity condition and the overall stability of the model.

For more details, see https://www.cia.gov/library/publications/the-world-factbook/fields/262rank.html

With respect to possible changes in countries' population, we estimate two alternative models using (i) GDP and CO2 in per capita terms, and (ii) GDP, EINT, and CO2 in per capita terms (i.e. their growth rates in per

https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-a&chapter=27&clang=_en.

I am indebted to the Editor (Corina Saman), two anonymous referees, participants at "The Thirteen International Conference Sustainable Growth in The Current Socio-Political Context (SGSP2018)

summary statistics). The cumulative IRFs are illustrated by panels (c) and (d) in Figure 6 in the Supplementary Material.

On the one hand, the findings indicate that for the states which ratified or acceded to the Kyoto Protocol before 2005, the evolution of cumulated CO2 response following output and urbanization shocks seems to switch its increasing trend in the long-run. In particular, this suggests that this group of countries may attain the peak in CO2 more rapidly and for lower levels of GDP and URB, compared to the economies which ratified /acceded to the Protocol after it entered into force. As such, the traditional and urbanization-EKC hypothesis seems to be more realistic for the Kyoto Protocol group A. Moreover, for the Kyoto Protocol group A states, the urbanization exhibits a delayed cumulated effect on CO2. In contrast, for the members of group B, the effect loses its significance in the long-term.

On the other hand, an exogenous increase in energy intensity (renewables) triggers a cumulated positive (negative) effect on CO2 in both groups of economies. However, at the moment of the impact, the magnitude of CO2 response is higher due to energy intensity (renewable energy) disturbances for the states which ratified/acceded to the Kyoto Protocol before (after) it entered into force. Also, in the next two years after the impact, the cumulated magnitude of CO2 response following both energy intensity and renewables shocks increases sharply, but then stabilizes and raises very slowly for the group A economies. For the group B states, the cumulated response of CO2 (i) raises abruptly after the impact due to EINT disturbances, but then stabilizes to a new high and follows a linear path until the end of the period, (ii) remains roughly at the same level recorded at the time of the impact following renewable energy shocks. Besides, a positive one standard deviation shock to CO2 has a positive effect on its levels for both groups. However, the cumulated effect increases (decreases) slowly over the years across the states of the Kyoto Protocol group A (B).

Overall, the findings may suggest that the states which ratified or acceded to the Protocol before 2005 are the ones that have undergone significant changes in their economic development (e.g. have experienced a more intense process of industrialization and urbanization, among others). Thus, they were committing much faster in actions to counteract the potential adverse effects on the environment. [START_REF] Pesaran | A simple panel unit root test in the presence of cross section dependence[END_REF] H0 is ''all series are non-stationary''. For the unbalanced panels, namely for CO2_OIC and CO2_PI, we report the standarized t-bar statistic. P-values in brackets. ***, **, * denote statistical significance at the 1%, 5%, and 10% level, respectively.

Supplementary Material

Additional References for the Supplementary Material

Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Olivier, J.G.J., Peters, J.A.H.W. and Schure, K.M., 2017. Fossil CO2 and GHG emissions of all world countries. EUR 28766 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-79-73207-2, doi:10.2760/709792, JRC107877.