Green aid, aid fragmentation and carbon emissions

Green aid, aid fragmentation and carbon emissions Mehmet Pinar Business School, Edge Hill University, Ormskirk, Lancashire L39 4QP, UK Departamento de Análisis Económico y Economía Política, Universidad de Sevilla, Avda. Ramón y Cajal, 1, 41018 Sevilla, Spain HIGHLIGHTS GRAPHICAL ABSTRACT •This paper examines green aid effectiveness in reducing carbon dioxide emissions. •We use panel data of 92 countries covering the period between 2002 and 2018. •The green aid fragmentation reduces the effectiveness of green aid flows. •We use of different fragmentation measures, and results remain robust. •Donors should reduce fragmentation to increase the effectiveness of the green aid. ABSTRACTARTICLE INFO Editor: Kuishuang Feng Keywords: Green aid Carbon emissions Fragmentation Institutional quality Climate change The existing studies that examined the green aid effectiveness in reducing carbon dioxide (CO2) emissions found mixed results; however, neither of these studies considered the role of aid fragmentation in the effectiveness of green aid flows. By using the dynamic panel generalized method of moments (GMM) methodology to panel data of 92 countries covering the period between 2002 and 2018, this study examines the impact of green aid fragmentation on the effectiveness of green aid flows in reducing CO2 emissions. Using different fragmentation measures, this paper finds that green aid fragmentation is detrimental to the effectiveness of green aid flows in reducing CO2 emissions per capita. The findings also highlight that the damaging impact of green aid fragmentation is lower in countries with stronger institutions. This paper highlights the need for better coordination of the green aid flows by having less fragmented green aid flows. 1. Introduction Climate change has become one of the primary concerns of the world as it led to extreme events, such as reduced crop yields (Lobell et al., 2011;Ray et al., 2015, 2019), intensified the frequency and size of forest fires (Flannigan et al., 2000;Seidl et al., 2017;Michetti and Pinar, 2019), deteriorated health of many citizens of the world (Neira et al., 2014) and low economic growth (Dell et al., 2012;Dellink et al., 2019), among many other negative consequences. To combat climate change, an Intergovernmental Panel on Climate Change report (IPCC, 2018) pointed out that global temperature rise could be kept below 1.5 °C by achieving a zero-emissions target by 2050 and reducing carbon emissions. Furthermore, the United Nations' Sustainable Development Goals (SDGs), the 2015 Paris Agreement, and the recent COP21 meeting in Glasgow urged countries to reduce carbon emissions to mitigate the adverse effects of climate change. As a result, many nations signed the Paris agreement, which was adopted in 2015 under the United Nations Framework Science of the Total Environment 870 (2023) 161922 E-mail address: [email protected]. http://dx.doi.org/10.1016/j.scitotenv.2023.161922 Received 26 October 2022; Received in revised form 23 January 2023; Accepted 27 January 2023 Available online 1 February 2023 0048-9697/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Convention on Climate Change (UNFCCC), to reduce their carbon emissions (UNFCCC, 2015). Furthermore, developed countries have been allocating some part of the foreign aid that targets the objectives of the Rio Conventions, and climate finance reached 79.6 billion U.S. dollars in 2019 (OECD, 2021). Recent studies have investigated the effectiveness of green aid flows in reducing carbon emissions in developing countries and found mixed findings (see e.g., Bhatnagar and Sharma (2022) for a recent review of green finance). Even though most of the existing studies found that the green aid flows reduced carbon emissions in developing countries (Carfora and Scandurra, 2019;Farooq, 2022;Ikegami and Wang, 2021;Sharma et al., 2019;Wu et al., 2021), some studies found that green aid flows increased carbon emissions (Mahalik et al., 2021), or had no significant impact on carbon reductions (Bhattacharyya et al., 2018). The recipient country's institutional quality was an essential factor in the effectiveness of green aid flows (Li et al., 2021;Ren et al., 2022). However, neither existing studies accounted for nor controlled for aid fragmentation, yet aid fragmentation is a vital characteristic of the effectiveness of aid flows. This paper aims to fill this gap and examine the role of aid fragmentation in the effectiveness of green aid flows in reducing carbon emissions. The aid fragmentation has been found to be detrimental to the effectiveness of foreign aid (see e.g., Djankov et al., 2009)asfragmentedaidleadsto increased transaction costs (Anderson, 2012) and puts additional pressure on the limited administrative capacity in developing countries (Knack and Rahman, 2007). The existing studies examined the role of foreign aid in the economic growth of the recipient country, and they found that foreign aid is lesseffectiveifitismorefragmented(Kimura et al., 2012). However, recent studies also found that donor concentration (lower fragmentation) in different sectors may be detrimental to economic growth (Gehring et al., 2017). Given that aid fragmentation may have a differing impact in various sectors, this paper aims to contribute to the green aid effectiveness literature by examining the role of aid fragmentation in reducing carbon emissions in recipient countries by using panel data of 92 recipient countries over the 2002–2018 period. Toourknowledge,thisisthefirst attempt to examine the role of aid fragmentation in assessing the green aid effectiveness in reducing carbon emissions. The remainder paper is organized as follows. Section 2 provides a literature review on the effectiveness of green aid, the role of aid fragmentation in foreign assistance, and other factors affecting carbon emissions. Section 3 provides the details of the data and methodology. Section 4 provides the empirical findings, and finally, Section 5 concludes and provides policy recommendations. 2. Literature review The recent literature has investigated the role of green aid flows in reducing carbon emissions in developing countries. One stream of literature found that the green aid effectively reduced CO2 emissions. Using the autoregressive distributed lag (ARDL) model, Mahalik et al. (2021) found that total foreign aid effectively reduced CO2 emissions in India between 1978 and 2014. Similarly, Sharma et al. (2019) also demonstrated that foreign aid effectively reduced CO2 emissions in Nepal by using the ARDL model. Carfora and Scandurra (2019) used the propensity score matching method, which included83countriesthatreceivedgreenaidand66countriesthatdidnotreceive green aid, and found that countries that received green aid experiencedareductionintheirCO2emissions.Inarelatedpaper,Wu et al. (2021) used fixed effect and mediation analyses and found that green aid reduces CO2 emissions directly and indirectly by lowering biofuel consumption. Using annual data for 49 Asian countries between 2001 and 2019, Farooq (2022) also showed that foreign aid effectively reduced CO2 emissions. Another stream of literature found that green aid was only effective if countries had stronger institutions. For example, using data from 86 aid recipient countries over the period 2003–2014, Li et al. (2021) found that green aid increased CO2 emissions in countries with poor institutions but reduced CO2 emissions in countries with lower levels of corruption. Similarly, analyzing panel data of 30 Chinese provinces for the 2006–2017 period, Ren et al. (2022) also found that environmental aid significantly reduces carbon emissions if property rights are protected. On the other hand, Bhattacharyya et al. (2018) examined the effect of energy-related aid flows in 128 countries between 1971 and 2011 and found that energy-related aid flows did not significantly reduce CO2 emissions except for the European and Central Asian countries. Finally, Mahalik et al. (2021) found that energy-related aid flows increased CO2 emissions in India. The above literature examined the effectiveness of green aid in reducing carbon emissions; however, neither controlled for aid fragmentation. This paperaimstocontributetotheliterature by examining the role of green aid fragmentation in the effectiveness of green aid flows to reduce CO2 emissions. Extensive literature demonstrated that the aid is less effective if the aid is more fragmented (Djankov et al., 2009). Fragmented aid increases direct and indirect transaction costs (Acharya et al., 2006;Anderson, 2012), deteriorating the bureaucratic quality in the recipient country (Knack and Rahman, 2007). Most existing studies found that aid fragmentation hinders economic growth (Annen and Kosempel, 2009;Kimura et al., 2012; Gehring et al., 2017). However, recent studies also show that the aid is more effective if fragmented. Using data on 106 recipient countries from 1970 to 2008, Gutting and Steinwand (2017) demonstrated that aid fragmentation reduced the risk of political instability related to aid shocks. Similarly, Gehring et al. (2017) found that donor concentration is detrimental to education-related aid flows, and countries with a better bureaucratic quality benefit more from fragmented assistance. Beyond the foreign aid flows as a determinant of carbon emissions, other factors are found to be important for environmental pollution. The so-called environmental Kuznets curve (EKC) hypothesis highlights that the initial economic growth leads to environmental degradation up to a given development level; however, economic growth after a given development level improves environmental conditions (see e.g., Grossman and Krueger, 1991, 1995;Panayotou, 1993). The EKC hypothesis has been widely examined and most of the existing studies confirm the EKC hypothesis (Bekun et al., 2021;Ike et al., 2020;Karahasan and Pinar, 2022;Lau et al., 2019;Sinha and Shahbaz, 2018;Yao et al., 2019), but also some studies do not support the EKC hypothesis (Halliru et al., 2020;Inglesi-Lotz and Dogan, 2018;Zoundi, 2017). It has been found that foreign direct investment (FDI) and trade also affect carbon emissions. The existing studies also found that FDI and trade may either increase or decrease pollution levels. The so-called pollution haven hypothesis argues that the FDI flows to developing countries in which the environmental regulations are weaker, and FDI activities lead to increased CO2 emissions.The existing studies confirm the existence of the pollution haven hypothesis (Behera and Dash, 2017;Hanif et al., 2019;Mulatu, 2017;Sapkota and Bastola, 2017;Singhania and Saini, 2021). On the other hand, some other studies found that FDI flows reduced CO2 emissions as FDI flows allow host countries to access better technology and management systems (Adebayo et al., 2023;Huang et al., 2017;Nathaniel et al., 2020;Salehnia et al., 2020). In comparison, some other studies found that foreign direct investment had no significant or varying effect on CO2 emissions based on the investing country or recipient region (see e.g., Ahmad et al., 2021a;Apergis et al., 2022;Tan et al., 2021). Similarly, the existing studies also found that trade openness impacts CO2 emissions per capita (see e.g., Adebayo et al., 2022;Cai et al., 2018;Chen et al., 2022;Dauda et al., 2021;Dou et al., 2021;Essandoh et al., 2020;Kolcava et al., 2019). Finally, recent studies demonstrated that urbanization also plays a significant role in pollution levels (Ahmad et al., 2021b;Dong et al., 2020;Hussain et al., 2022; Murshed et al., 2021;Nadeem et al., 2020;Nathaniel and Adeleye, 2021; Sufyanullah et al., 2022;Wang et al., 2020;Wang et al., 2021). Therefore, while examining aid fragmentation's role in the effectiveness of environmental aid flows in reducing carbon emissions, we also control for various additional factors that were essential determinants of carbon emissions. 3. Data and empirical strategy 3.1. Data The data for the green aid flows are obtained from the OECD's Environment and Rio markers online database (OECD, 2022). The green M. Pinar Science of the Total Environment 870 (2023) 161922 2 aid flows data set is classified into various dimensions: climate change mitigation and adaptation, desertification, and biodiversity. Since this paper aims to investigate the effect of aid flows on climate change mitigation outcome (i.e., carbon emissions), we use the total aid flows allocated to the recipient country in the category of climate change mitigation. We obtained the green aid flows for 92 countries between 2002 and 2018, and then divided the total green aid flows with the total population to get green aid flows per capita (AID) received by each country. We use three proxies to measure green aid fragmentation. The most commonly used proxy for fragmentation is based on the Herfindahl index. This index was initially used to measure the degree of competition in a given industry. There has been a prevalent use of this index to measure political fragmentation (see e.g., Apergis and Pinar, 2021, 2023;Funke et al., 2016;Karahasan et al., 2021) and aid fragmentation (see, e.g., Djankov et al., 2009;Easterly, 2007;Knack and Rahman, 2007;Gehring et al., 2017), among others. The green aid fragmentation based on the Herfindahl index (HI) can be written as follows: FHIðÞ¼1−∑i¼N i¼1π2 ið1Þ where i=1,2,…,Nrepresent different donors, π i represents the share of donor i in overall green aid allocated to the recipient country in a given year. For instance, if aid received by a given country in a given year is fully distributed by a single donor (π i = 1), then the F(HI) will be zero, highlighting that there is no aid fragmentation. On the other hand, if many donors give a small portion of aid, then π i would be close to zero and the index value of F(HI) would be getting closer to 1 (i.e., there is a considerable fragmentation in aid). Furthermore, concentration indices have been employed to measure the overall concentration of the aid. The concentration ratios could be obtained as follows: CR NðÞ¼∑N i¼1πið2Þ The concentration ratios highlight the proportion of the aid given by N major donors. For instance, CR(1) would represent the share of the aid provided by the most significant donor (see, e.g., Djankov et al., 2009;Gehring et al., 2017). High concentration by a smaller number of donors would suggest lower levels of fragmentation. Based on the concentration ratios, two additional fragmentation proxies based on the share of three and four major donors (FCR(3) and FCR(4), respectively) could be calculated as follows: FCR 3ðÞ¼1−∑3 i¼1πið3Þ FCR 4ðÞ¼1−∑4 i¼1πi:ð4Þ Higher scores of FCR(3) and FCR(4) suggest higher fragmentation as the primary 3 and 4 donors only capture a small proportion of the green aid allocated in a given year. FCR(3) and FCR(4) would be equal to zero if all the aid is given by three and four primary donors, respectively. In other words, the aid would be concentrated, and the fragmentation would be lower. All fragmentation indices range between 0 and 1, and higher scores represent higher fragmentation (lower concentration). Using the data set from OECD (2022), we obtain the green aid per capita and fragmentation measures for 92 countries between 2002 and 2018. Fig. 1 provides the yearly average of aid per capita received by each country. There is a considerable variation in aid received by recipient countries. With few exceptions, Asian countries received relatively low levels of assistance, but we observe a significant variation in average aid per capita received by countries in Africa and Latin America. On average, Cabo Verde, Dominica and Jordan received the highest aid (i.e., 302, 176 and 125 dollars per capita, respectively). On the other hand, aid received by Iran and China roughly equates to one dollar per capita in a given year (see Appendix Table A1 for the yearly averages of green aid per capita between 2002 and 2018 for 92 countries). The fragmentation of green aid also shows variation across the globe. Panels (a), (b) and (c) of Fig. 2 offer yearly averages of aid fragmentation indices based on the Herfindahl index and shares of three and four significant donors (F(HI), FCR(3) and FCR(4), respectively), respectively. The aid received by most of the countries in sub-Saharan and western Africa is highly fragmented. On the other hand, aid fragmentation in different continents was relatively lower, but aid received by Bolivia, Guatemala, Honduras, Nicaragua, Nepal, Myanmar and Bangladesh was also highly fragmented. Overall, the average fragmentation index based on the Herfindahl index ranges between 0.436 (Grenada) and 0.868 (Mozambique), and FCR(3) ranges between 0.039 (Mauritius) and 0.496 (Mozambique), and the yearly averages of fragmentation measures are provided in Appendix A1. We also obtained the average fragmentation indices across 92 countries in a given year to explore the change in fragmentation over time. Fig. 3 and Fig. 4 present the fragmentation indices based on the Herfindahl index and shares of three and four primary donors (F(HI), FCR(3) and FCR(4)), respectively. The fragmentation of the green aid flows increased between 2002 and 2010 and reached the highest level in 2010, and there has been a decline in the fragmentation levels between 2010 and 2018. In line with the literature, as a measure of environmental degradation, we use CO2 emissions (measured in metric tons) per capita (Li et al., 2021;Sharma et al., 2019;Wu et al., 2021). We also control for a set of socio-economic factors that affect environmental degradation. We control for the GDP per capita (measured in constant 2015 US$), foreign direct investment (FDI) net inflows (% of GDP), trade openness (measured as the Fig. 1. Average aid per capita between 2002 and 2018. M. Pinar Science of the Total Environment 870 (2023) 161922 3 sum of exports and imports as a % of GDP), and urban population (urban population as a percentage of the total population). All these control variables are obtained from the World Development Indicators of the World Bank (2022). The descriptive statistics of the variables are provided in Table 1. 3.2. Empirical strategy The goal is to explore the role of green aid flows and their fragmentation in CO2 emissions per capita, and we use the following model specification: CO2it ¼α1CO2it−1þα2AIDit−1þα3FRAGit−1þα4AID FRAGit−1 þα5GDPit−1þα6GDP2 it−1þα7TOit−1þα8FDIit−1 þα9URBit−1þγiþβtþεit ð5Þ where CO2 is carbon dioxide emissions per capita, AID is the green aid flows per capita, FRAG is the fragmentation index, GDP is GDP per capita, GDP 2 is the squared term of the GDP per capita, TO denotes trade openness, FDI is the foreign direct investment, and URB indicates urbanization. Except for the fragmentation indices, all the variables are converted using the natural logarithm. We use the natural logarithm of these dependent and independent variables to transform the skewed data into normal distribution to use linear regression and eliminate the variance-covariance matrix variation and heteroscedasticity (Huang et al., 2022;Usman and Radulescu, 2022). On the other hand, the fragmentation indices are not converted with the natural logarithm to explore the relationship between green aid flows and CO2 emissions when fragmentation measures change by a unit, and also, fragmentation measures have a roughly normal distribution. The model allows for both country and time fixed effects, γ i and β t ,respectively. In a panel country framework, the disturbances, v i,t ,are uncorrelated. They are assumed to be independently distributed across countries with a zero mean. To avoid the presence of potential endogeneity issues, the two-step system generalized method of moments (GMM) estimation method, proposed by Arellano and Bover (1995) and Blundell and Bond (1998), is used. The endogeneity problem may arise due to reverse causality between CO2 emissions and other covariates. For instance, Sebri and Ben-Salha (2014) found that the rise in CO2 emissions leads to higher economic growth because of the energy-intensive sectors. Similarly, using Fig. 2. Yearly average aid fragmentation measures between 2002 and 2018. M. Pinar Science of the Total Environment 870 (2023) 161922 4 Granger causality methods, Wen and Dai (2020) showed that the increase in CO2 emissions in China led to a rise in trade openness. Along the same lines, a country that does not have strict regulations on CO2 emissions (i.e., experienced high CO2 emissions in previous years) could attract higher foreign direct investment. For instance, Seker et al. (2015) found that the increased CO2 emissions in Turkey led to increased foreign direct investment flows. Therefore, system GMM has been employed extensively by the existing studies to overcome endogeneity problems while assessing the factors affecting CO2 emissions (Bakhsh et al., 2021;Bhattacharya et al., 2017;Ren et al., 2022;Zhao et al., 2021,amongothers). 4. Empirical findings and analysis To test the role of green aid fragmentation on the effectiveness of the green aid flows in reducing CO2 emissions, we first use the full sample size to obtain system GMM estimates. Table 2 provides the baseline results when we use different fragmentation proxies. The first three columns of Table 2 provide results without any control variables, and the last three columns of Table 2 offer the results when all controls are included in the estimation. The results remain consistently similar across different measures of fragmentation. Our findings highlight that the interaction term between green aid and fragmentation is significant and negative when various fragmentation measures are used. The effect of fragmentation on the CO2 emissions per capita is sizeable as an increase in fragmentation measures from 0 to 1 would decrease the impact of green aid on CO2 emissions per capita by ranging between 0.28 and 0.50 percentage points. Furthermore, coefficients of AID and AIDxFRAG are jointly significant at the 1 % level. While the green aid flows effectively reduce the CO2 emissions per capita at the lower fragmentation levels (25th percentile of fragmentation), green aid flows increase CO2 emissions per capita when green aid becomes more fragmented (50th and 75th percentiles of fragmentation). Table 2 also offers the diagnostic tests. The AR (2) test is the ArellanoBond test for the existence of second-order autocorrelation, and we reject the second-order serial correlation in all specifications. Furthermore, difference-in-Hansen is the test of the validity of GMM instruments and the test of overidentification is based on the Hansen J statistic. Our findings highlight that the instruments are valid as the p-values of the difference-inHansen and Hansen J statistics are greater than 0.1. Finally, the findings concerning the control variables confirm the existence of the EKC hypothesis as the coefficients of the GDP per capita and Fig. 3. Fragmentation index based on the Herfindahl Index between 2002 and 2018. Fig. 4. Fragmentation index based on aid by the three and four largest donors as a percentage of total aid [F(CR3), F(CR4)] between 2002 and 2018. M. Pinar Science of the Total Environment 870 (2023) 161922 5 squared term of the GDP per capita are positive and negative, respectively. Our findings confirm the pollution halo hypothesis as trade openness and FDI flows significantly reduce CO2 emissions per capita. On the other hand, urbanization levels also lead to a reduction in CO2 emissions. This finding is in line with Wang et al. (2021) and Nathaniel and Adeleye (2021), as they highlighted that urbanization levels increase energy efficiency and, therefore, reduce CO2 emissions. The existing studies found that green aid only effectively reduces CO2 emissions when countries have stronger institutions (Li et al., 2021;Ren et al., 2022). This finding is in line with the existing literature on foreign aid flows as most studies found that aid flows led to economic growth if countries had stronger institutions (Burnside and Dollar, 2000). Furthermore, institutional quality is a critical factor in reducing CO2 emissions (see e.g., Karim et al., 2022;Khan et al., 2021;Khan et al., 2022;Shan et al., 2021) and deployment of renewable energy (Baye et al., 2022; Chen et al., 2021;Uzar, 2020). Finally, a recent study by Gehring et al. (2017) found that institutional quality prevents the adverse effects of fragmentation and reinforces the positive impact of fragmentation. In other words, the detrimental effect of aid fragmentation on the effectiveness of green aid may be weaker in countries with stronger institutional quality. Therefore, to test whether the impact of fragmentation varies based on different institutional quality settings, we clustered the country sample into two groups: countries with lower institutional quality and countries with relatively better institutional quality. We use the rule of law proxy from the World Governance Indicators to measure institutional quality. To separate countries into two groups with different institutional quality levels, we obtained the average rule of law for each country between 2002 and 2018. We then used the median institutional quality to divide the countries into two groups: countries with poorer and stronger institutional quality. Table 3 offers the results when the analysis is carried out for two different groups of countries. For both groups of countries, when the fragmentation index based on the Herfindahl index, F(HI), is used, the interaction term between green aid and fragmentation is no longer significant. However, our findings align with the baseline estimations when the FCR(3) and FCR (4) are used. Our findings point out that the detrimental effect of fragmentation is lower in countries with stronger institutions. When FCR(3) and FCR(4) measures are used as fragmentation measures, we find that the green aid flows increase CO2 emissions at the 50th percentile of the fragmentation in countries with weaker institutions (columns 2 and 3 of Table 3). However, green aid flows reduce CO2 emissions at the 25th and 50th percentiles of the fragmentation but increase CO2 emissions at the 75th percentile of the fragmentation in countries with stronger institutional quality. Our findings align with Gehring et al. (2017).Even though fragmentation has a detrimental effect on the effectiveness of green aid in reducing CO2 emissions, the damaging impact of fragmentation is lower in countries with stronger institutions. Table 2 Green aid, fragmentation, and carbon emissions: Baseline estimations. (1) (2) (3) (4) (5) (6) Variables F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) CO2 per capita 0.994*** 0.981*** 0.984*** 0.976*** 0.962*** 0.968*** (0.00171) (0.00291) (0.00256) (0.00279) (0.00376) (0.00430) AID −0.182*** −0.102*** −0.0644*** −0.189*** −0.0966*** −0.0681*** (0.0103) (0.00694) (0.00457) (0.0135) (0.00595) (0.00435) AIDxFRAG 0.276*** 0.492*** 0.464*** 0.286*** 0.477*** 0.499*** (0.0144) (0.0278) (0.0271) (0.0194) (0.0259) (0.0283) FRAG −0.935*** −1.809*** −1.669*** −0.946*** −1.690*** −1.762*** (0.0552) (0.111) (0.103) (0.0728) (0.111) (0.121) GDPpc 0.0597 0.136** 0.155*** (0.0398) (0.0591) (0.0557) GDPpc^2 −0.00156 −0.00573 −0.00739** (0.00246) (0.00358) (0.00331) TRADE −0.00350 −0.00894 −0.0128* (0.00458) (0.00677) (0.00655) FDI −0.0413** −0.0998*** −0.0978*** (0.0210) (0.0246) (0.0245) URBAN −0.0176*** −0.0149** −0.0200*** (0.00648) (0.00720) (0.00633) Observations 1380 1380 1380 1380 1380 1380 AR[2] 0.457 0.323 0.293 0.542 0.374 0.367 Hansen overidentification 0.447 0.414 0.406 0.551 0.508 0.614 Difference-in-Hansen 0.422 0.166 0.300 0.641 0.406 0.739 Joint significance (AID, AIDxFRAG) p-value 0.000 0.000 0.000 0.000 0.000 0.000 Marginal effect of Aid at Frag. 25th percentile −0.0061 −0.0371 −0.0279 −0.0067 −0.0337 −0.0289 Frag. 50th percentile 0.0200 0.0088 0.0040 0.0204 0.0108 0.0055 Frag. 75th percentile 0.0391 0.0578 0.0448 0.0401 0.0583 0.0494 Notes: Columns (1) and (4) use F(HI), Columns (2) and (5) use F(CR3), and Columns (3) and (6) use F(CR4) as a proxy of aid fragmentation. Year dummies are included in all regressions. Dependent variable is CO2 emissions per capita. AR[2]is the test for auto-correlation of order 2. Difference in Hansen is the test of validity of GMM instruments in the level equation. The test of overidentification is based on the Hansen J statistic. The null hypothesis is that the instruments are valid (i.e., uncorrelated with the error term), and that the exclusions restrictions are valid (i.e., the instruments are correctly excluded from the second-stage equation). t-values are reported in parentheses and *, **, *** represent significance at the 10 %, 5 % and 1 %, respectively. Table 1 Descriptive statistics. Variable Mean SD Min Max CO2 emissions per capita 1.96 2.36 0.02 16.02 Aid per capita 40.91 55.58 0.28 655.76 F(HI) 0.69 0.15 0.03 0.91 F(CR3) 0.24 0.13 0.00 0.66 F(CR4) 0.17 0.11 0.00 0.56 Rule of law −0.56 0.60 −1.85 1.08 GDP per capita 3523.58 3125.61 281.97 18,062.46 Trade openness 74.44 34.90 0.17 311.35 FDI 4.55 6.90 −37.15 103.34 Urban 49.06 18.47 8.68 91.87 Notes: F(HI), F(CR3), F(CR4) represent fragmentation proxies based on the Herfindahl Index, concentration ratios based on the aid by the three and four largest donors as a percentage of total aid, respectively. M. Pinar Science of the Total Environment 870 (2023) 161922 6 Our findings concerning the control variables also show variation based on the institutional quality of countries. We confirm that trade openness led to CO2 emissions reduction in countries with weaker institutions, but trade openness leads to increased CO2 emissions in countries with stronger institutions. On the other hand, the EKC hypothesis is not confirmed for countries with weaker institutions, but the EKC hypothesis still holds for institutionally strong countries. Finally, an increase in urbanization levels in countries with poor institutional quality reduces CO2 emissions. The average green aid fragmentation levels were relatively higher in Sub-Saharan African (SSA) countries than in the rest of the regions. Therefore, we carried out our analysis only using the SSA countries and excluding the SSA countries from the analysis. Table 4 reports the results. The main finding of this paper still holds for most of the specifications. The coefficients of the AID and interaction term between AID and fragmentation (i.e., AIDxFRAG) are jointly significant at the 10 % level when F(HI) is used, but the coefficients are not jointly significant when FCR(3) and FCR(4) are used for the SSA countries. This finding suggests that the detrimental effect of fragmentation still holds for SSA countries when F(HI) proxy is used. However, when the other two proxies of fragmentation are used, green aid flows to the SSA countries have no significant effect on CO2 emissions. Finally, the main findings of this paper still hold when the SSA countries are excluded from the analysis (columns 4–6). We excluded SSA countries from the analysis and found that our findings were consistent with the baseline findings (Table 2). We also carry out analysis by excluding countries from other geographical clusters, and the results are shown in Table 5. Columns (1)–(3), (4)–(6), (7)–(9), and (10)–(12) of Table 5 provide the results when countries from East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA) are excluded, respectively. Firstly, the diagnostic tests (i.e., the AR (2) test, difference-in-Hansen and overidentification tests) suggest that there is no second-order serial correlation in all specifications, and our instruments are valid. Secondly, our findings highlight that the coefficients of the AID and interaction term between AID and fragmentation measures (i.e., AIDxFRAG) are jointly significant at the 1 % level when different fragmentation measures are used for all cases. Overall, the findings align with the baseline estimations when different geographical clusters are excluded from the sample. In other words, the baseline findings are robust to the exclusion of different geographical groups from the analysis. Finally, we also carry out additional robustness analysis when we exclude low-income, low middle-income, upper middle-income countries from our sample based on the World Bank's income classification to test whether the findings differ based on the development level of countries. Table 6 offers the results. And columns (1)–(3), columns (4)–(6), and columns (7)–(9) of this table present the results when we exclude low-income, low middle-income, and upper middle-income countries from our sample, respectively. The diagnostic tests (i.e., the AR (2) test, difference-in-Hansen and overidentification tests) suggest that there is no second-order serial correlation in all specifications and our instruments are valid. Furthermore, we also find that the coefficients of the AID and interaction term betweenAIDandfragmentationmeasures (i.e., AIDxFRAG) are jointly significant at the 1 % level when different fragmentation measures are used for all samples. When lowincome and low middle-income countries are excluded from the sample (i.e., results presented in columns (1)–(3) and columns (4)–(6), respectively), aid is only effective if the fragmentation level is lower than the Table 3 Green aid, fragmentation, and carbon emissions: countries with different institutional quality. Low institutional quality High institutional quality (1) (2) (3) (4) (5) (6) Variables F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) CO2 pc 1.022*** 0.980*** 1.005*** 1.010*** 0.923*** 0.956*** (0.0134) (0.0162) (0.0246) (0.0597) (0.0440) (0.0352) AID −0.00554 −0.0313*** −0.0217** −0.0697 −0.0724*** −0.0425*** (0.0183) (0.0112) (0.00981) (0.0441) (0.0178) (0.0131) AIDxFRAG 0.0123 0.184*** 0.200*** 0.113 0.280*** 0.207** (0.0240) (0.0379) (0.0404) (0.0793) (0.0927) (0.0943) FRAG −0.00107 −0.696*** −0.692*** −0.201 −0.799* −0.386 (0.0880) (0.146) (0.148) (0.276) (0.427) (0.401) GDPpc −0.0214 0.0752 0.0106 0.240 0.693* 0.555* (0.0758) (0.103) (0.160) (0.422) (0.384) (0.291) GDPpc^2 0.000146 −0.00234 −6.04e-05 −0.0146 −0.0391* −0.0322* (0.00429) (0.00583) (0.00874) (0.0238) (0.0217) (0.0168) TRADE 0.00648 −0.0186** −0.0167** −0.00862 0.0472** 0.0463** (0.00693) (0.00787) (0.00791) (0.0298) (0.0220) (0.0202) FDI 0.0117 0.0636 0.0332 −0.0275 −0.0115 −0.0373* (0.0595) (0.0571) (0.0568) (0.0196) (0.0214) (0.0194) URBAN −0.0417*** −0.0338*** −0.0477*** −0.0400 0.00241 −0.00648 (0.00924) (0.0108) (0.0100) (0.0258) (0.0184) (0.0174) Observations 690 690 690 690 690 690 Number of countries 46 46 46 46 46 46 AR[2] 0.282 0.376 0.369 0.636 0.321 0.323 Hansen overidentification 1.000 1.000 0.998 1.000 1.000 1.000 Difference-in-Hansen 1.000 1.000 1.000 1.000 1.000 1.000 Joint significance (AID, AIDxFRAG) p-value 0.643 0.000 0.000 0.158 0.000 0.004 Marginal effect of Aid at Frag. 25th percentile 0.0023 −0.0070 −0.0060 0.0023 −0.0355 −0.0262 Frag. 50th percentile 0.0035 0.0101 0.0078 0.0130 −0.0093 −0.0120 Frag. 75th percentile 0.0043 0.0285 0.0254 0.0208 0.0185 0.0062 Notes: Columns (1) and (4) use F(HI), Columns (2) and (5) use F(CR3), and Columns (3) and (6) use F(CR4) as a proxy of aid fragmentation. Year dummies are included in all regressions. Dependent variable is CO2 emissions per capita. AR[2]is the test for auto-correlation of order 2. Difference in Hansen is the test of validity of GMM instruments in the level equation. The test of overidentification is based on the Hansen J statistic. The null hypothesis is that the instruments are valid (i.e., uncorrelated with the error term), and that the exclusions restrictions are valid (i.e., the instruments are correctly excluded from the second-stage equation). t-values are reported in parentheses and *, **, *** represent significance at the 10 %, 5 % and 1 %, respectively. M. Pinar Science of the Total Environment 870 (2023) 161922 7 median fragmentation level. On the other hand, when upper middleincome countries are excluded from the analysis, we find that aid is effective even though the fragmentation level is at the median level. In other words, fragmentation is less of a problem for low-income and low middle-income countries. Overall, our findings highlight that the baseline results are robust when analyses are carried out using different country samples based on their development levels. 5. Conclusion and policy implications The existing literature on the effectiveness of green aid in reducing CO2 emissions found mixed results; however, neither of these studies controlled for aid fragmentation. This paper examined the role of green aid fragmentation on the effectiveness of green aid flows in reducing CO2 emissions in developing countries. Using panel data for 92 countries between 2002 and 2018 and dynamic panel data estimation techniques, this paper demonstrated that the green aid flows are only effective if aid is less fragmented. When the green aid fragmentation increases, the effectiveness of the green aid flows in reducing CO2 emissions declines, and even at high fragmentation levels, the green aid flows lead to increases in CO2 emissions. Our findings also highlight that the adverse effect of fragmentation is relatively lower in institutionally stronger countries. We also carried out various robustness analyses and demonstrated that the results are robust to the exclusion of different geographical clusters and income groups from the country sample. To increase the effectiveness of green aid in reducing CO2 emissions, the donors should reduce the fragmentation levels of aid. Furthermore, improving the institutional capacity in the recipient countries would also alleviate the negative implications of aid fragmentation since countries with stronger institutions would have a better administrative capacity to cope with the fragmented aid. One of the limitations of the current study is that it does not capture the recent years due to data availability. This study considers the role of aid fragmentation in the effectiveness of green aid flows to reduce CO2 emissions between 2002 and 2018; however, future studies could evaluate the role of aid fragmentation in the effectiveness of green aid flows by using more recent periods. Secondly, we used CO2 emissions to measure environmental degradation (quality) in this study. However, some of the existing literature uses alternative proxies for environmental quality. For instance, the ecological footprint is one of the main proxies used for environmental quality (see e.g., Ansari, 2022;Destek and Sarkodie, 2019;Dogan et al., 2020;Huang et al., 2022;Zhan et al., 2021), and a future study may evaluate the role of green aid fragmentation and green aid for environmental quality by using ecological footprint as a proxy for environmental quality. Finally, this paper examined the effect of aggregate green aid received by host countries; however, green aid allocated by some donors may be more effective in improving environmental quality than other donors (see e.g., Minasyan et al., 2017;Hoeffler and Sterck, 2022). Therefore, a future study could evaluate the impact of green aid on environmental quality by disaggregating the data and assessing the effectiveness of green aid allocated by different donors. CRediT authorship contribution statement Mehmet Pinar: Conceptualization, Writing - original draft, Writing - Review and Editing, Formal Analysis, Data collection, Supervision. Table 4 Green aid, fragmentation, and CO2 emissions: different country classifications. SSA countries No SSA countries (1) (2) (3) (4) (5) (6) Variables F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) CO2 pc 1.005*** 0.938*** 1.032*** 0.983*** 0.946*** 0.964*** (0.108) (0.102) (0.108) (0.00672) (0.0193) (0.00837) AID −0.103** −0.0256 −0.0220 −0.0752*** −0.0604*** −0.0334*** (0.0494) (0.0247) (0.0214) (0.0283) (0.00626) (0.00707) AIDxFRAG 0.170** 0.201* 0.0284* 0.111** 0.312*** 0.229*** (0.0780) (0.105) (0.0165) (0.0465) (0.0355) (0.0468) FRAG −0.587* −0.541 0.230* −0.420** −1.194*** −0.819*** (0.352) (0.408) (0.133) (0.194) (0.124) (0.180) GDPpc −0.0453 −0.108 −0.541 0.402 0.148 0.468 (0.568) (0.624) (0.562) (0.248) (0.287) (0.334) GDPpc^2 0.00309 0.0129 0.0343 −0.0239 −0.00726 −0.0273 (0.0301) (0.0346) (0.0299) (0.0150) (0.0169) (0.0198) TRADE −0.0310 −0.0155 −0.0254 −0.00492 −0.00280 −0.00649 (0.0200) (0.0225) (0.0238) (0.0103) (0.0118) (0.00564) FDI 0.102 0.0366 0.0323 −0.0619* −0.0712* −0.0743** (0.0860) (0.0945) (0.0956) (0.0365) (0.0376) (0.0316) URBAN −0.0134 0.0329 −0.0134 −0.0349* −0.0121 −0.0402** (0.0497) (0.0444) (0.0516) (0.0188) (0.0177) (0.0195) Observations 570 570 570 810 810 810 Number of countries 38 38 38 54 54 54 AR[2] 0.213 0.258 0.264 0.427 0.449 0.402 Hansen overidentification 1.000 1.000 1.000 0.997 0.965 0.994 Difference-in-Hansen 1.000 1.000 1.000 1.000 1.000 1.000 Joint significance (AID, AIDxFRAG) p-value 0.093 0.123 0.189 0.001 0.000 0.000 Marginal effect of Aid at Frag. 25th percentile 0.005 0.001 −0.020 −0.004 −0.019 −0.015 Frag. 50th percentile 0.021 0.020 −0.018 0.006 0.010 0.000 Frag. 75th percentile 0.033 0.040 −0.015 0.014 0.041 0.021 Notes: Columns (1) and (4) use F(HI), Columns (2) and (5) use F(CR3), and Columns (3) and (6) use F(CR4) as a proxy of aid fragmentation. Year dummies are included in all regressions. Dependent variable is CO2 emissions per capita. AR[2]is the test for auto-correlation of order 2. Difference in Hansen is the test of validity of GMM instruments in the level equation. The test of overidentification is based on the Hansen J statistic. The null hypothesis is that the instruments are valid (i.e., uncorrelated with the error term), and that the exclusions restrictions are valid (i.e., the instruments are correctly excluded from the second-stage equation). t-values are reported in parentheses and *, **, *** represent significance at the 10 %, 5 % and 1 %, respectively. M. Pinar Science of the Total Environment 870 (2023) 161922 8 Table 5 Green aid, fragmentation, and CO2 emissions: different geographical clusters are excluded. No EAP countries No ECA countries No LAC countries No MENA countries (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Variables F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) F(HI) F(CR3) F(CR4) CO2 pc 0.992*** 0.981*** 0.981*** 0.966*** 0.967*** 0.968*** 0.984*** 0.977*** 0.974*** 0.971*** 0.962*** 0.968*** (0.00349) (0.00336) (0.00352) (0.00432) (0.00747) (0.00713) (0.00277) (0.00437) (0.00377) (0.00297) (0.00258) (0.00265) AID −0.187*** −0.0806*** −0.0590*** −0.137*** −0.0821*** −0.0563*** −0.0768*** −0.0549*** −0.0439*** −0.230*** −0.102*** −0.0699*** (0.0125) (0.00502) (0.00384) (0.0107) (0.00457) (0.00360) (0.00627) (0.00371) (0.00346) (0.00908) (0.00460) (0.00346) AIDxFRAG 0.298*** 0.455*** 0.505*** 0.202*** 0.377*** 0.350*** 0.109*** 0.285*** 0.322*** 0.352*** 0.513*** 0.534*** (0.0181) (0.0235) (0.0258) (0.0163) (0.0177) (0.0276) (0.00905) (0.0134) (0.0176) (0.0136) (0.0239) (0.0280) FRAG −0.981*** −1.521*** −1.708*** −0.644*** −1.235*** −1.138*** −0.365*** −1.030*** −1.152*** −1.094*** −1.777*** −1.858*** (0.0556) (0.0951) (0.107) (0.0606) (0.0782) (0.110) (0.0388) (0.0567) (0.0858) (0.0492) (0.101) (0.117) GDP pc −0.0501 0.0195 0.0653 0.128*** 0.142*** 0.182*** 0.172*** 0.207*** 0.212*** 0.0439 0.145** 0.155*** (0.0383) (0.0444) (0.0440) (0.0339) (0.0516) (0.0441) (0.0212) (0.0416) (0.0403) (0.0429) (0.0587) (0.0563) GDP pc^2 0.0045* 0.00077 −0.0024 −0.0055*** −0.0062** −0.0091*** −0.0101*** −0.0117*** −0.0118*** 0.00026 −0.0060* −0.0072** (0.0023) (0.0027) (0.0026) (0.0020) (0.0030) (0.0025) (0.0013) (0.0025) (0.0025) (0.0026) (0.0036) (0.0034) TRADE −0.0199** −0.0172** −0.0166** 0.00522 −0.00565 −0.00164 0.0107** −0.0101*** −0.00533 −0.000678 −0.00902 −0.0131** (0.00785) (0.00841) (0.00818) (0.00342) (0.00497) (0.00587) (0.00465) (0.00383) (0.00488) (0.00429) (0.00655) (0.00612) FDI −0.0644** −0.0835** −0.0909*** −0.00160 −0.00967 −0.00645 0.00597 −0.0119 −0.0246** −0.0286** −0.0738*** −0.0788*** (0.0327) (0.0364) (0.0342) (0.0107) (0.0112) (0.0106) (0.00882) (0.0102) (0.0104) (0.0137) (0.0200) (0.0206) URBAN −0.0169*** −0.0179*** −0.0214*** −0.0112** −0.0120** −0.0131*** −0.0236*** −0.0195*** −0.0224*** −0.0173*** −0.0157** −0.0210*** (0.00652) (0.00588) (0.00581) (0.00483) (0.00584) (0.00501) (0.00482) (0.00696) (0.00707) (0.00610) (0.00738) (0.00661) Observations 1245 1245 1245 1230 1230 1230 1065 1065 1065 1260 1260 1260 Number of countries 83 83 83 82 82 82 71 71 71 84 84 84 AR[2] 0.692 0.372 0.352 0.365 0.229 0.218 0.188 0.324 0.310 0.712 0.385 0.357 Hansen overidentification 0.442 0.408 0.433 0.760 0.492 0.633 0.757 0.652 0.780 0.391 0.572 0.556 Difference-in-Hansen 0.757 0.323 0.335 0.550 0.588 0.596 0.990 0.853 0.948 0.291 0.393 0.703 Joint significance (AID, AIDxFRAG) p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Marginal effect of Aid at Frag. 25th percentile 0.0030 −0.0206 −0.0193 −0.0082 −0.0324 −0.0288 −0.0073 −0.0173 −0.0186 −0.0056 −0.0343 −0.0279 Frag. 50th percentile 0.0311 0.0219 0.0155 0.0109 0.0028 −0.0047 0.0030 0.0093 0.0036 0.0277 0.0135 0.0088 Frag. 75th percentile 0.0517 0.0672 0.0599 0.0248 0.0403 0.0261 0.0105 0.0377 0.0319 0.0519 0.0646 0.0558 Notes: Columns (1), (4), (7) and (10) use F(HI), Columns (2), (5), (8) and (11) use F(CR3), and Columns (3), (6), (9) and (12) use F(CR4) as a proxy of aid fragmentation. Year dummies are included in all regressions. Dependent variable is CO2 emissions per capita. AR[2] is the test for auto-correlation of order 2. Difference in Hansen is the test of validity of GMM instruments in the level equation. The test of overidentificationisbasedontheHansenJ statistic.Thenullhypothesisisthattheinstrumentsarevalid(i.e.,uncorrelated with the error term), and that the exclusions restrictions are valid(i.e.,theinstrumentsarecorrectlyexcludedfromthesecond-stageequation).t-values are reported in parentheses and *, **, *** represent significanceatthe10%,5%and1%,respectively. M. Pinar Science of the Total Environment 870 (2023) 161922 9