text:
Yacouba Coulibaly Prénom

Yacouba Coulibaly

Patrick Villieu

Alexandru Minea

Lucie Aïcha

Sanou

Can Resource-backed Loans Mitigate Climate Change ?

Keywords: Resource-backed loans, Resource rents, Forest cover loss, Resource taxation, Environment, Climate Change, Propensity score matching O13, H81, C12, Q54, Q01

Resource-backed loans are used today by many resource-rich countries as an effective means of providing public goods and services. However, this type of financing can undermine environmental sustainability (e.g., forest cover loss, CO2 emissions, pollution, ecological collapse, material footprint, etc.). In this paper, we first use propensity score matching, which allows for self-selection bias in signature policies, coarsened exact matching, and the entropy balancing method to test whether resource-backed loans have a causal impact on forest cover loss in 64 developing countries from 2004 to 2018. Through a series of econometric and alternative specification tests, we find that resource-backed loans increase forest cover loss. Nevertheless, when we disaggregate resource-backed loans to run the regressions, we find that mineral, tobacco, and cocoa-backed loans increase forest cover, while oil-backed loans have no significant direct impact on forest cover. We recommend that signatory countries and those considering signing resource-backed loans put in place a very strong compensation mechanism, such as introducing taxes or reforming the current tax system in resource-backed loan agreements, to protect biodiversity and mitigate the environmental impacts of these loans. Signatory countries must ensure full transparency of resource-backed loans to make the characteristics of the loans more fluid, avoiding a situation of budgetary debauchery.

Introduction

Etymologically, natural capital includes the stocks of natural resources, land, and ecosystems that are essential for the sustainability of economic development [START_REF] Bank | Where is the wealth of nations ? Measuring capital for the 21st century[END_REF] and poverty reduction in developing countries [START_REF] Celentano | Welfare outcomes and the advance of the deforestation frontier in the brazilian amazon[END_REF][START_REF] Wunder | Poverty alleviation and tropical forests-what scope for synergies ?[END_REF].

Several countries have developed thanks to the exploitation and transformation of their natural capital wealth [START_REF] Halland | Le secteur des industries extractives : Points essentiels à l'intention des économistes, des spécialistes des finances publiques et des responsables politiques[END_REF]. Today, many developing countries are using their original endowment of natural capital in the form of oil, gas, minerals, etc. To access the financial market and obtain funds for their economic development goals. On this occasion, some countries in sub-Saharan Africa and Latin America have turned to a new type of financing called resource-backed loans market to circumvent the difficulties of accessing markets and raising funds to finance public goods and services [START_REF] Mihalyi | Resource-backed loans in subsaharan africa[END_REF].

Basically, resource-backed loans are financings provided to a government or public enterprise, with repayment either directly in the form of natural resources (in kind), such as Oil or minerals, or from revenues generated by those resources, or repayment is secured by future revenues related to the natural resources, or the reserves of the natural resources serve as collateral for the loan [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF][START_REF] Mihalyi | Resource-backed loans in subsaharan africa[END_REF].

As these loans depend on natural resources, this may increase countries' dependence on natural resources. However, heavy dependence on natural resources can lead to serious environmental problems [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF]. Indeed, the literature has shown that natural resource exploitation and economic activities through mining and gas rents [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], access to domestic credit [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF], public debt [START_REF] Culas | Debt and deforestation : A review of causes and empirical evidence[END_REF] exert strong pressure on the environment through land conversion, climate change, deforestation, and biodiversity loss. Statistically, the mining industry in Carajas, Brazil, has resulted in an annual deforestation of 6.100 km 2 through the conversion of timber to supply smelters [START_REF] Kricher | A neotropical companion : an introduction to the animals, plants, and ecosystems of the New World tropics[END_REF][START_REF] Moran | Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index[END_REF]. According to [START_REF] Sonter | Mining drives extensive deforestation in the brazilian amazon[END_REF], mining activities caused 11.67 km 2 of deforestation in the Amazon forest between 2005 and 2015. Global forest change data show that approximately 2032 hectares of forest area were lost between 2004 and 2018, broken down by country and by percentage of tree canopy greater than 20% [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF]. A recent study by the Food and Agriculture Organization showed that about 420 million hectares of forest have been lost to conversion to other land uses since 1990 [START_REF] Fao | Forests, biodiversity and people[END_REF]. This report shows that the rate of deforestation between 2015 and 2020 was estimated at 10 million hectares per year, compared to 16 million hectares per year in the 1990s. This means that the global primary forest area has decreased by more than 80 million hectares since 1990 [START_REF] Fao | Forests, biodiversity and people[END_REF].

Moreover, in recent years, these statistics have drawn the attention of governments, interest groups, civil society, and academia to the issue of climate change. Many studies affect forest cover. Not to mention the extent of deforestation and forest degradation that continues at alarming rates and contributes significantly to ongoing biodiversity loss, global warming, and environmental crises [START_REF] Fao | Forests, biodiversity and people[END_REF]. One possible explanation for this difference is that resource-backed loans force governments to exploit resources, especially land resources. Unlike other credits, there is no implicit pressure or obligation to use force on the forest layer. To our knowledge, there have been four studies of the economic impacts of resource-backed loans, for developing countries [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF][START_REF] Chapel | Impact of official development assistance projects for renewable energy on electrification in sub-saharan africa[END_REF][START_REF] Rivetti | Debt transparency in developing economies[END_REF]. None of these four examined the impact of resource-backed loans on deforestation in signatory countries. The opacity of resource-backed loans, i.e., the non-transparency of loans, would explain the fact that no previous study has examined impacts on forest cover because resources backed loans are not included in government budget statistics for most signatory countries and therefore may not reflect actual published statistics. For all the above reasons, this paper fills an important gap in both the literature on the socioeconomic impacts of resource-backed loans and the literature on the determinants of natural capital, particularly forest cover, as it is, to our knowledge, the first paper to estimate the impact of resource-backed loans on deforestation for a panel of two regions of the world (sub-Saharan Africa and Latin America). Particular attention is paid to the environmental impacts of resource-backed loans in order to enrich the policy debate on climate change and natural resource exploitation. In other words, resource-backed loans is a form of external credit or man-made capital, and increased access to it is a means to increase the stock of man-made capital [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF]. Thus, our research question is : Does increased access to man-made capital through the provision of external credit (resource-backed loans) lead to increased or decreased use of natural capital ? Contrary to [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] and [START_REF] Antle | Multiple equilibria, soil conservation investments, and the resilience of agricultural systems[END_REF], our intuition is that resourcebacked loans may not have a homogeneous impact because of their composition 1 may not have a more homogeneous impact on forest cover than domestic credit and public spending [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] and public debt [START_REF] Culas | Impact of credit policy on agricultural expansion and deforestation[END_REF]. Deforestation is the subject of a large literature. The topic has been the subject of a major debate in economics since the mid-1980s, in which two paradigms confronted each other. The first dealt with the relationship between economic growth and the environment using Kuznets' environmental curves, while the second focused on explanations for deforestation. Although these issues have been extensively studied from an empirical perspective [START_REF] Angelsen | Agricultural expansion and deforestation : modelling the impact of population, market forces and property rights[END_REF][START_REF] Bhattarai | Institutions and the environmental kuznets curve for deforestation : a crosscountry analysis for latin america, africa and asia[END_REF] ; [START_REF] Van | Nonlinearities and heterogeneity in environmental quality : An empirical analysis of deforestation[END_REF], no consensus has yet emerged. In light of the existing literature, our work examines the impact of resource-backed loans on the 1. The official report of the Natural Resource Governance Institute (NRGI) shows that there are now 52 resource-backed loans, composed of 43 credits backed by oil, 6 by minerals, 2 by cocoa, and 1 by tobacco.

environment through deforestation.

The contribution of this study to the literature is twofold. First, we add to the literature on efficient use of natural resources and deforestation [START_REF] Perman | Natural resource and environmental economics[END_REF][START_REF] Makunga | The extent and drivers of deforestation and forest degradation in masito-ugalla ecosystem, kigoma region, tanzania[END_REF][START_REF] Allen | The causes of deforestation in developing countries[END_REF][START_REF] Dove | A revisionist view of tropical deforestation and development[END_REF][START_REF] Bese | The effect of external debt on emissions : evidence from china[END_REF][START_REF] Bojang | Economic and social significance of forests for africa's sustainable development[END_REF][START_REF] Njoroge | Economic and social significance of forests for rwanda's sustainable development[END_REF][START_REF] Motel | A methodology to estimate impacts of domestic policies on deforestation : Compensated successful efforts for "avoided deforestation"(redd)[END_REF][START_REF] Delacote | Reducing deforestation and forest degradation : leakage or synergy ?[END_REF][START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF][START_REF] Combes | Deforestation and seigniorage in developing countries : A tradeoff ?[END_REF][START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF][START_REF] Culas | Impact of credit policy on agricultural expansion and deforestation[END_REF]Ranjan, 2019, etc.), focusing on environmental compliance and the clean development model. Second, we focus specifically on resource-backed loans, as its impact on borrowing countries' economies in particular and on climate change in general has been largely unexplored. To this end, we specifically address the link between resource-backed loans and deforestation, which to our knowledge has never been studied. We are the first to examine the impact of this type of loans on vegetation cover in a context of climate change that is so troubling for all countries in the world. Therefore, identifying the transmission channels of deforestationbacked loans is critical to enrich and guide policy discussions on this issue.

Our results first suggest that natural resource-backed loans exert high pressure on forest cover, i.e., significantly increase deforestation in countries that have taken these loans compared to those that have not (i), using a propensity score matching (PSM) method.

(ii) when we disaggregate resource-backed loans to run the regressions, we find that mineral, tobacco, and cocoa-backed loans increase forest cover, while oil-backed loans have no significant direct impact on forest cover. (iii) By exploring the transmission channels, our results show that public debt, natural resource depletion, and commodity prices have a positive and statistically significant effect on forest cover loss in signatory countries. (iv)

The results also suggest that resource-backed loans have a positive and statistically significant effect on forest cover loss in signatory Sub-Saharan African countries, in contrast to Latin American countries where no significant effect was found.

The rest of the paper is structured as follows. Section 2 presents the stylized facts and the review of the existing literature on the subject is presented in Section 3. Then, Section 4 describes the methodology with successively a presentation of data and the empirical identification strategy used to estimate the impact of natural resource-backed loans on deforestation. The results are presented in Section 5 while in Section 6 we study the sensitivity and heterogeneity of these results respectively. Section 7 presents the transmission channels. Finally, Section 8 concludes the study and presents key policy recommendations from the findings.

Stylized facts

In this section, we present some stylized facts that characterize resource-backed loans, natural resource deposits, and the extent of deforestation in developing countries from 2004 to 2018.

Resource-backed loans

Historically, resource-backed loans has been around since the resource boom of the early millennium. The idea was to facilitate access to financial markets for resource-rich developing countries that had difficulty obtaining financing for economic development. In the 1980s, only Angola had shown interest in this type of financing to fund its military expenditures. Today, it is clear that this type of financing has attracted the attention of several developing countries. According to the NRGI report, there are 52 resource-backed loans in two regions 2 of the world (see Figure 3). In addition, 30 of the 52 resource-backed loans identified in developing countries during 2004-2018 went to African countries, while the remaining 22 went to Latin American countries [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. A total of 14 countries 3 in these two regions have subscribed to these loans totaling $164 million, including $98 million for Latin American countries and $66 million for Sub-Saharan African countries (Mihalyi et al. ;2020 ;[START_REF] Chapel | Impact of official development assistance projects for renewable energy on electrification in sub-saharan africa[END_REF].

The NRGI report indicates that the largest beneficiary of these loans is Guinea Conakry, which subscribed $200 million in 2017, backed by bauxite. This amount was equivalent to about 200% of the country's GDP at that time [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. The country has used this large sum to build multi-sectoral infrastructure, including the Coyah-Dabola road, the Conakry road network and sanitation, and university buildings. It should be 2. Sub-Saharan Africa and Latin America 3. These 14 countries are : Angola, Brazil, Chad, Democratic Republic of Congo, Ecuador, Ghana, Guinea, Niger, Republic of Congo, Sao Tome and Principe, Sudan, South Sudan, Venezuela and Zimbabwe (for more details, see [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF][START_REF] Chapel | Impact of official development assistance projects for renewable energy on electrification in sub-saharan africa[END_REF][START_REF] Coulibaly | How do natural resource-backed loans affect the public debt sustainability in developing countries ? empirical evidence[END_REF].

noted that most of these resource-backed loans were provided by China. In fact, China is the leader in terms of market share due to its unconditional aid policy and donations to developing countries. Following the signing of the $20 million agreement, China and the Guinean state agreed to disburse the sum gradually between 2017 and 2036, in return for which Guinea will grant mining concessions to Chinese companies. The Chinese companies that receive mining concessions and agreements will help repay this large financing programme for Guinea, according to the Guinea EITI 4 2017.

The second largest recipient of resource-backed loans in Africa is the Republic of Congo, with a total of $1.6 million in 2006, representing 20.7% of the country's GDP at the time. This amount was used for the implementation of infrastructure, the repayment of which must be made over a period of 20 years with oil [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. Besides these two countries, the Democratic Republic of Congo is the third largest beneficiary.

The country has subscribed resource-backed loans with China amounting to 3.000 million dollars or about 16% of its GDP in 2008. The repayment of this sum must be made through the exploitation and sale of copper & cobalt. In Latin America, Venezuela is the largest beneficiary with a contribution of 23.3% of GDP in the period 2004 to 2018.

The country signed its largest agreement in 2010 for an amount of 20255 million dollars, equivalent to about 7% of its GDP in that year. This amount of money was used to implement major projects. These include the financing of infrastructures : electricity, heavy industry, housing, agricultural projects, 6 million dollars at the discretion of Venezuela ; associated with the construction of highways and power plants [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. In conclusion, Ecuador and Brazil are behind Venezuela with a share of 15% and 1.4% of GDP, respectively, in the period 2004-2018.

Unlike African countries, all Latin American countries have linked their loan payments to oil. It should be noted, however, that non-oil-backed loans taken out during 2004-2018 are higher than oil-backed loans (see Table 1). Our intuition in this paper is that resource-backed loans will not only accelerate resource depletion, but can also degrade vegetation cover. Therefore, it would be detrimental to look at the evolution of natural resource rent mobilization, resource depletion, and the deforestation variable over time.

2.2 Resource-backed loans, rents, natural resources depletion and loss forest cover.

We therefore examine natural resource rent capture and natural resource extraction for countries with resource-backed loans versus countries without resource-backed loans over the period 2004 to 2018. Figure 2 shows that the rate of natural resource depletion is higher in countries with resource-backed loans than in countries without resource-backed loans (twice). Countries that have taken a resource-backed loan have, on average, a resource depletion rate of 8.74% of GNI in contrast to countries that have not taken a loan, which have, on average, only 4.18% of GNI during 2004-2018 (see Figure 2). As for the total rent, it is very high in the subscribing countries, averaging about 18% of GDP compared to 7% of GDP in the non-subscribing countries. Similar to Figure 3, forest loss is higher in signatory countries than in non-signatory countries. The literature on the macroeconomic impacts of resource-backed loans is nascent and short [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF][START_REF] Chapel | Impact of official development assistance projects for renewable energy on electrification in sub-saharan africa[END_REF][START_REF] Rivetti | Debt transparency in developing economies[END_REF][START_REF] Coulibaly | How do natural resource-backed loans affect the public debt sustainability in developing countries ? empirical evidence[END_REF], and that on deforestation is nonexistent. To do so, we link our literature to two channels through which resource-backed loans could affect forest cover, namely the handful of studies that estimate the effects of external debt and public spending, resource rents on deforestation.

Public debt, resource-backed loans and forest cover.

We begin the discussion by asking how resource-backed loans affect deforestation through the external debt channel, since these loans are a special type of external debt. [START_REF] Culas | Debt and deforestation : A review of causes and empirical evidence[END_REF] estimated the relationship between debt and deforestation through a crosscountry regression analysis using a set of panel data from 23 African countries between 1971 and 1994, and the results show that debt and deforestation are positively related in tropical developing countries. The author recommends reducing the debt burden of developing countries to expand opportunities for better environmental governance. These findings are consistent with those of [START_REF] Shandra | Debt, structural adjustment and deforestation : A cross-national study[END_REF], which used a cross-country model to estimate the impact of debt on deforestation in 62 countries from 1990 to 2005.

These authors find that debt and structural adjustment increase deforestation. However, the increasing number and density of international nongovernmental organisations is reducing deforestation.

In the same vein, [START_REF] Gullison | The role of foreign debt in deforestation in latin america[END_REF] examined the role of foreign debt in deforestation in Latin America. They found that external debt has contributed to economic stagnation and an associated increase in poverty in Latin America, which in turn has led to degradation of marginal lands affecting forest cover. The authors suggest that debt could be exchanged for forestry 5 and agricultural sector reform, which would have a very positive impact on forest conservation and management. Using instrumental variable methods, [START_REF] Lawell | One shape does not fit all : A nonparametric instrumental variable approach to estimating the income-pollution relationship at the global level[END_REF] estimates the global relationship between income and pollution over the period 1980 to 2012. They find that debt service may be correlated with deforestation as countries liquidate natural assets to pay their debts. [START_REF] Boly | Can public debt mitigate environmental debt ? theory and empirical evidence[END_REF], however, have found a long-term complementary relationship and a short-term substitution relationship between sovereign debt and the environment. The authors note that in the short run, government debt generates new resources that can be invested in mitigating activities, improving the quality of the environment 6 . In contrast, the government must pay down the debt burden.

Public expenditure and forest cover.

Resource-backed loans could help governments increase public spending to finance public goods and services. However, such spending has been shown to have negative environmental consequences. Indeed, an increase in public spending could trigger the clearing of forest land for agricultural production in the short term, which would increase deforestation. [START_REF] Galinato | The effects of corruption control, political stability and economic growth on deforestation-induced carbon dioxide emissions[END_REF] found, using a theoretical model, that increased public spending and the expansion of social safety nets in developing countries significantly affect forest cover. In fact, increased deforestation and carbon dioxide emissions are caused by land use change. These results are consistent with those of [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF], which uses both a theoretical model and the generalized method of moments (GMM) to estimate the impact of public spending and credit on forest cover loss in 63 developing countries during 2001-2012. The results show that public spending has a positive impact on forest cover. [START_REF] López | Fiscal spending and the environment : theory and empirics[END_REF], however, have modelled the environmental impact of the composition of public spending. The results show that shifting the composition of public spending toward social and public goods reduces pollution. This improves the quality of the environment. Conversely, increasing total public spending without changing its composition does not reduce pollution.

The collapse of commodity prices can be attributed to two consequences of resourcebacked loans on the loss of forest cover. First, resource-backed credits may jeopardize public debt situation [START_REF] Horn | China's overseas lending[END_REF] and prevent the country from mobilizing fiscal resources to finance public expenditures related to climate change. Second, the loss of 5. e.g., debt-for-nature swaps.

6. e.g financing green projects or low carbon policies.

fiscal revenues due to price rip-offs will increase the face value of the debt as it is denominated in U.S. dollars, forcing countries to increase their exploitation activities to comply with the trade. This will adversely affect forest cover, as activities related to natural resource exploitation and exploration increase forest cover loss in resource-rich countries.

To illustrate, [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF] conducted a theoretical analysis, supported by empirical evidence, and found an adverse effect of extractive industry rents on forest cover, but pointed out that the effects are not homogeneous depending on the type of rent.

They argue that maximizing rents is strongly associated with deforestation and that this can be offset by adopting a «polluter pays» model. Recent impact studies on resourcebacked loans, such as. [START_REF] Coulibaly | How do natural resource-backed loans affect the public debt sustainability in developing countries ? empirical evidence[END_REF], have shown that resource-backed loans improves debt dynamics over a seven-year period after signing due to the investments made and the tax revenues generated by those investments, which would allow for the financing of public expenditures directed toward green projects related to climate change or investments that meet environmental standards.

Data and Methodology

Data

Resource-backed loans variable

We use information about the existence of a resource-backed loan from NRGI. To date, NRGI remains the only institution that provides reliable and solid evidence on the existence and many details of resource-backed loans in developing countries.

A team of researchers led by Mihalyi, D., Adam, A. and Hwang, J. collected as much information as possible on resource-backed loans to obtain a somewhat larger database.

They compiled two original dataset maintained by institutions dedicated to researching China's overseas activities : the China-Africa loans dataset produced by the China-Africa

Research Initiative (CARI) at Johns Hopkins SAIS and the China-Latin America financial database of the Inter-American Dialogue and Global Development Policy Center at Boston University [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. Next, the authors supplemented these databases with additional literature searches to present their dataset. It should be noted that the data on these resource-backed loans are not fully comprehensive due to the relative opacity of these transactions [START_REF] Rivetti | Debt transparency in developing economies[END_REF]. There could certainly be other resource-backed loans in both regions than those listed by the authors as they are limited by publicly available information [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. However, it is the most comprehensive dataset on resource-backed loans for African and Latin America economies over a long period. Based on this information, we construct a binary variable that takes the value 1 if country i at a period t has taken out a resource-backed loan and 0 otherwise.

Forest Cover Loss variable

Following [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] and [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], we use forest cover data from global forest change. The global forest change contains a dataset mainly from the University of Maryland Department of Geographic Sciences and has recently been published and made freely available by [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF]. In this dataset, trees are defined as vegetation over 5 m in height expressed as a percentage per output grid cell as "2000% tree cover". Secondly, "loss of forest cover" is defined as a stand-replacing disturbance, or a change from a forested to a non-forested state. As for the 'Forest Cover Gain' is defined as the inverse of loss, or a non-forest to forest change entirely within the period 2000-2012. And finally, 'Forest Loss Year' is a disaggregation of total 'Forest Loss'

to annual time scales [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF]. Although all of these data based on Landsat satellite imagery published by [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF] may suffer from potential bias from inaccuracies in distinguishing between forests and plantations at the local level [START_REF] Tropek | Comment on "high-resolution global maps of 21st-century forest cover change[END_REF], it remains a potentially valuable source of information on forest cover. In contrast to [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] using a canopy cover >10% and [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], canopy cover >20%, this study considers a forest as any area with more than 25% trees in 2000, thus excluding all areas with a lower percentage of trees. We will use other cover thresholds in the robustness tests to take into account the forest typology.

Matching variables

Following the literature, we select some variables that can affect the level of deforestation (the key drivers). First, we capture the effect of the credit variable which is measured by domestic credit provided by the banking sector as a percentage of GDP. It has been shown in the literature that the more credit the private banking sector provides to agents, the more agricultural activities intensify. Consequently, deforestation accelerates the more [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF][START_REF] Culas | Impact of credit policy on agricultural expansion and deforestation[END_REF]. In the short term, its sign is positive on deforestation. However, in the long term, if income from agricultural activities is reinvested in conservation and environmental protection, it will have a negative effect on deforestation. We control for the effect of net official development assistance (ODA), which includes the disbursement of loans and grants to promote the economic development and well-being of countries and territories 7 . Just like at [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], we expect a negative impact of the net ODA received on the loss of forest cover. Similarly, we control for gross capital formation, or gross domestic investment, which consists of expenditures on the acquisition of fixed assets in the economy and net changes in the level of inventories in our model. Secondly, we add public expenditure which measures government consumption expenditure as a percentage of GDP. Previous studies have found significant positive impacts on deforestation [START_REF] Galinato | The effects of government spending on deforestation due to agricultural land expansion and co2 related emissions[END_REF][START_REF] Gupta | Fiscal policies, environmental pollution and economic growth[END_REF] 7. Defined by the World Bank in the context of the construction of the ODA variable in the dataset [START_REF] Gupta | Fiscal policies, environmental pollution and economic growth[END_REF][START_REF] López | Fiscal spending and the environment : theory and empirics[END_REF][START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF][START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF].

In our study we expect a positive effect on deforestation.

The effect of natural resource rents are controlled. This variable measures the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents as percent of GDP. [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], in their recent study they found a significant positive effect of resource rents, excluding forest rents, on deforestation. We believe that its sign on deforestation will be positive in our study.

Finally, we capture the effect of natural resource depletion, which is the sum of net forest depletion, energy depletion, and mineral depletion 8 . Its sign on deforestation is ambiguous because it depends on the level of depletion of natural resources.

Methodology

Identification strategy

In this study, we investigate whether signing a resource-backed loan has led to significant loss of forest cover. To do this, we need to compare the level of deforestation in countries that have signed up to resource-backed loans against non-signatories. This allows us to see more clearly the causal impact of resource-backed loans. Indeed, this comparison can be affected by a selection bias. Furthermore, treated and untreated individuals are not identical and their differences, in addition to being treated, may act as confounding factors if they impact their deforestation level.

In addition, the outcome of treated and control individuals may differ even in the absence of treatment. Table 2 below shows a simple comparison of our covariates by treatment group (treated and untreated). A clear difference is observed between the different units treated at the level of our covariates due, for example, to the macroeconomic conditions of the countries, thus leading to an endogeneity problem that may bias our conclusions. We solve this endogeneity by using a matching technique.

Propensity score matching

We follow the impact assessment methodology, which consists of assessing the average treatment effect on treated persons (ATT) proposed by [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal effects[END_REF].

Indeed, the PSM is a two-step process : first, using a probit model, we generate for each country a propensity score p(x), which estimates the probability that this country, with its vector of characteristics, will take out a resource-backed loan. Thus :

8. Net forest depletion is unit resource rents times the excess of round wood harvest over natural growth. However, Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime. About Mineral depletion, it's the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (see World Bank definition for more details).

(X)=Pr(T = 1|X ) = E(T|X )
Where, T= {0 ;1} is the binary variable indicating whether the country has signed a resource-backed loan and X is the vector of observed characteristics before treatment.

Table 3 reports the main descriptive statistics of the variables used for matching. Second, we assess the impact of resource-backed loans by estimating the average treatment effect on treated countries (ATT), expressed as follows :

AT T =E[(Y i 1 -Y i 0 )|T i =1]=E[(Y i 1 |T i =1)] -E[(Y i 0 |T i =1)] (1)
backed loan, and zero otherwise. Y i 1 captures the forest loss when the country signed an resource-backed loan, and Y i 0 is the forest loss that would have been observed if the country had not signed an resource-backed loan. The problem is that we cannot observe Y i 1 and Y i 0 simultaneously. Here, there is a counterfactual problem that arises.

One solution would be to compare average deforestation levels between signatory and non-signatory countries to get around this difficulty. However, this approach assumes that the allocation of treatment is random. Then, the assumption would be ad hoc because the choice to contract an resource-backed loan may be dictated by some omitted variables (macroeconomic situation, good governance, degree of vulnerability to exogenous shocks etc.) that also affect forest cover loss, which would lead to a self-selection bias. For this purpose, [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal effects[END_REF] and [START_REF] Smith | Does matching overcome lalonde's critique of nonexperimental estimators[END_REF] propose to replace in

Equation 1 the unobservable term E[(Y i 0 |T i 1 )] by the observable term E[(Y i 0 |T i 0 , Xi)] to
satisfy the conditional independence assumption. This gives us the following equation :

AT T =E[(Y i 1 |RBL i =1, X i )] -E[(Y i 0 |RBL i =0, X i )] (2)
A second assumption based on the existence of a common support, also called the overlap condition, must be verified (0 < p(X) <1). This hypothesis assumes that for each treated country, there is at least one comparable untreated country with a fairly similar propensity score. We finally obtain the following equation :

AT T =E[E{Y i 1 |RBL i =1, p(X i )} -E[(Y i 0 |RBL i =0, p(X i ))]
(3)

Selection of matching algorithms

In practice, treated countries are matched to untreated countries according to their propensity scores using different matching methods and the ATT is the difference in deforestation level results between treated and untreated countries matched on similar propensity score criteria. It is also possible to match each treated country with more than one control country. Thus, we match each treated country with the two and then the three closest neighbours in terms of propensity score. However, it should be noted that with this method, it's possible for a treated country to be matched with one or more control countries with a very distant propensity score leading to a poor match and potentially biased results. [START_REF] Dehejia | Propensity score-matching methods for nonexperimental causal studies[END_REF] proposed a radius calibre matching method to resolve this bias. This method consists of matching each treated country with all control countries that are within a well-defined neighbourhood threshold, called a caliper. In our study, we use a low (r = 0.005), medium (r = 0.05) and high (r = 0.01) calibre.

Finally, we use two last algorithms : kernel matching and local regression matching. The first associates each treated unit with a counterfactual equal to the average of all untreated units weighted by a weight inversely proportional to their distance from the treated unit considered. The second is a generalised version of the kernel estimator, but the difference is that it includes a linear term in the propensity score of a treated unit [START_REF] Heckman | Matching as an econometric evaluation estimator[END_REF].

Results

This section presents our main results. We present the propensity score estimates in subsection 5.1. Subsection 5.3 then presents the (ATT) estimation results.

Discussion of propensity scores

In Table 4, we present the results of the probit model used to predict the propensity scores for the matching algorithms. In column [1], we report the results of our basic model.

Net official development assistance and natural resource depletion reduce the probability of a country signing a resource-backed loan. However, natural resources rents and final consumption expenditure are positively correlated with resource-backed loans. The overall regression fit is acceptable with a Pseudo R2 of 0.1894 for our baseline model.

Quality of matching

We start checking the quality of our results with probabilities close to 0. Indeed, the Figure 6 shows that most of our treated individuals have a probability very close to 0. It would be very easy to match treated units with low propensity scores but not so easy to match treated units with a high propensity score. Note that there is no consensus in the literature on the best test to judge the effectiveness of this test [START_REF] Chapel | Impact of official development assistance projects for renewable energy on electrification in sub-saharan africa[END_REF].

Following [START_REF] Simone | The role of international institutions in fostering sub-saharan africa's electrification[END_REF], we re-estimate our propensity score only for matched individuals and compare the obtained Pseudo R2 with the one obtained before the matching process. If the matching worked well, the pseudo R2 of the probit model with only matched individuals had to be reduced considerably and turned out to be very low. This is the case in our study as we see in Table 5 that it has gone from 0.1894 to almost 0. Some authors rely instead on the standardised bias which calculates the percentage bias on each covariate. The bias must have decreased significantly compared to the prematching bias for each covariate, and the closer it is to 0, the more efficient our matching is. Finally, the p-value associated with the standardised bias must be greater than the critical value of 10% [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal effects[END_REF]. This is the case in our study. Source :Author's construction

The Figure 7 shows the distributions of estimated propensity score before and after matching (left and right respectively). Before matching, the common support appears to be quite large, but the graph shows that treated units with a high propensity score may not be matched because of the small number of nearby control units. After matching, the distribution of propensity score in the treated and untreated group is similar, which proves that the treated units were indeed matched with untreated units having a similar propensity score in our matches. Given that we have this graphical evidence our results

are not matching bias, we can safely move on to analyse the results.

Figure 7 -Common support before and after matching.

Source :Author's construction

Matching results

The Table 5 below presents the results of the ATT. The estimated coefficients are all positive and significant, with a magnitude ranging from 1.64 (N-nearest-Neighbors Matching) to 1.41 (Kernel Matching) percentage points. These results indicate that the signing of a resource-backed loan has contributed to increased deforestation in the recipient countries. A simple explanation for these results would be the formal obligations on debtor countries to repay its loan with natural resources. This would push countries to accelerate their resource, which in turn affects forest cover. In addition, infrastructure projects such as the construction of highways, hydroelectric dams, schools, social housing etc. created by resource-backed loans have also contributed to the loss of vegetation cover in the recipient countries.

Another channel is the accumulation effect of loans and the possibility of renegotiating loans in case of difficulty. In fact, the more the country has the possibility of renegotiating the maturity of these loans in a situation of inability to pay, the longer the repayment period increases and the extractive activities continue to intensify. As a result, the loss of forest cover accelerates further. 6 Robustness test

Modification of sample

The main objective of the theory of clean development is to meet environmental standards. In order to combat illegal and abusive deforestation, several developing countries have introduced restrictions on the export of timber both at national and international level. These bans aim to reduce the export of forest products, which could have a significant effect on the level of deforestation [START_REF] Burgess | The political economy of deforestation in the tropics[END_REF][START_REF] Resosudarmo | Is the log export ban effective ? revisiting the issue through the case of indonesia[END_REF]. Forest Trends has identified 72 countries in their database that actually restrict or have restricted the international export of one or more forest products. The extent of deforestation in those countries imposing one or more of these measures may differ whether or not it is addressed in our sample. To obtain a homogeneous sample and robust results, we excluded all countries that have adopted export bans on forest products. The results of the ATT are reported in remain robust (see Table 6).

Adding control variables

After modifying the sample, we also consider increasing the specificity of the baseline model to test the robustness of our results. To do this, we control across several additional variables that are likely to be positively or negatively correlated with both resource-backed loans and forest cover (Table 6, row [5]- [12]). These variables are : public debt, GDP per capita, public debt(t-1), demographic growth, rich countries, Minerals reserve horizon, Rainfall shocks and Temperature shocks. We begin the explanation and discussion of this modification in controlling the effect of the public debt. Indeed, the literature has shown that an unsustainable level of debt leads to a high rate of tropical deforestation mainly through the clearing of forest land for agricultural expansion [START_REF] Culas | Impact of credit policy on agricultural expansion and deforestation[END_REF]. In fact, heavy external debt can reduce the country's standing in the conventional financial market, pushing it towards China to sign a resource-backed loan agreement [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF], the repayment of which will require abusive deforestation. The variable GDP per capita measure measures the level of aggregate national wealth created over a period of time per capita. The higher level of GDP can help governments finance their development goals without resorting to resource-backed loans, financing green projects that will have a significant impact on forest cover. However, Its impact on deforestation remain ambiguous according to the literature. [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] found a positive and significant impact of GDP on deforestation. In contrast [START_REF] Foster | Economic growth and the rise of forests[END_REF], in their study on India show that neither the expansion of agricultural productivity nor rising wages have increased local forest cover.

We control for the effect of demographic growth. This variable can have an impact on the horizon and depletion of natural resources, the size of housing and farms. An increase in population fuels demand for arable land, fuel-wood and charcoal [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF]. Secondly, we monitor the effect of natural resource endowments (minerals, gas, oil etc.) on deforestation. In fact, natural resource endowment can easily attract foreign multinationals to rich countries [START_REF] Manyika | Disruptive technologies : Advances that will transform life, business, and the global economy[END_REF]. This could accelerate not only resource depletion but also deforestation. This variable is dummy, taking the value of 1 if the country is classified as rich in natural resources and 0 otherwise according to the IMF classification. In addition, the mineral reserve horizon is controlled in our model. Our intuition on this variable is based on the assumption that a long resource horizon leads to long exploitation and keeps the extractive industries going for a long time. This would further increase the loss of forest cover. Mineral reserve is a dummy variable equals 1 if a country's reserve horizon is greater than the median of all mineral-exporting countries, based on BP data.

Finally, we control for the effect of temperature and rainfall shocks which captures the effect of climate variability. These two variables measure the deviation of the average annual rainfall and temperature from its long-term trend (average rainfall and temperature from 1901 to 2021). The data is extracted from the Climate Research Unit (CRU)

database of the University of the Orient. Following [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] and [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], we control for the fact that, for example, high climate variability leads governments to tighten environmental standards which can reduce deforestation.

According to [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF], countries with lower levels of precipitation are often at risk of extreme weather. This is detrimental to vegetation in general, and to plants in particular, leading to a loss of forest cover.

We report the results of the propensity score in ). The new coefficients remain qualitatively and quantitatively comparable to the results of the baseline model (see Table 5).

Alternative matching method

The propensity score estimation technique may not provide robust evidence for our results. Indeed, propensity score matching has been widely criticised in the literature for producing fragile and non-robust estimates that can vary widely depending on the outcome model used [START_REF] King | Why propensity scores should not be used for matching[END_REF]. These authors believe that the PSM method may produce biased results if units that are far apart are progressively removed, the equilibrium will eventually deteriorate even if units close to each other on the propensity score remain.

To test the robustness of our results and avoid interpretation bias, we use Coarsened Exact

Matching as follows [START_REF] King | Why propensity scores should not be used for matching[END_REF]. This method compares each unit processed by the nearest control unit in terms of a rough exact match by first expanding the data which consists recoding indistinguishable values with the same values, and then matches the processed and unprocessed units on this expanded data [START_REF] Iacus | Causal inference without balance checking : Coarsened exact matching[END_REF]. Using this method, our estimated ATTs remain positive with similar magnitudes and significant (Table 7). Our results are robust when we use Coarsened Exact matching. This matching is better than propensity score matching because all individuals in our sample have the same probability of being matched, so we do not lose any observations after matching. However, our results may suffer from interpretation bias due to the fact that in the regressions for both methods we were unable to control for fixed effects. For this reason, we use yet another matching method more advanced in terms of identification strategy : entropy balancing method. This method was developed by [START_REF] Hainmueller | Entropy balancing for causal effects : A multivariate reweighting method to produce balanced samples in observational studies[END_REF] and implemented a few years later by [START_REF] Neuenkirch | The impact of us sanctions on poverty[END_REF], which is done in two simple steps.

The first step is based on the calculation of weights that are assigned to the control units (in our case, non-resource backed loans countries). In fact, this step calibrates the unit weights so that the re-weighted treatment and control group satisfies the predefined equilibrium conditions (one advantage of entropy balancing method). In the second step, the weights obtained in the previous step are used in a regression analysis with the treatment variable (resource-backed loan country) as the explanatory variable. Thus, it becomes easy to balance resource-backed loan signatory and non-signatory countries on the basis of observable characteristics also called "twins". Therefore, the average difference in forest cover loss between treated and untreated "twins" countries should be explained by the signing of a resource-backed loan. The literature has identified several advantages of the entropy balancy method but we will not cite them here in this paper (e.g. see, [START_REF] Hainmueller | Entropy balancing for causal effects : A multivariate reweighting method to produce balanced samples in observational studies[END_REF][START_REF] Balima | Do domestic bond markets participation help reduce financial dollarization in developing countries ?[END_REF]. However, an advantage that leads us to use this method is that it includes in the second step, individual and time fixed effects to control heterogeneity independently of the treatment [START_REF] Hainmueller | Entropy balancing for causal effects : A multivariate reweighting method to produce balanced samples in observational studies[END_REF].

The results are reported in where the coefficients remain positive but insignificant. These results confirm our baseline finding that resource-backed loans has a positive impact on forest cover loss in signatory countries.

Alternative canopy thresholds of forest

The literature has identified shortcomings in the measurement of forest cover published by [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF]. For example, [START_REF] Tropek | Comment on "high-resolution global maps of 21st-century forest cover change[END_REF] point out that the classification of high-resolution satellite data based on a single, simplistic algorithm can only provide a limited insight into real forest dynamics at the local scale. Furthermore, the authors believe that the definition of forest given by [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF] includes any vegetation below 5 m that is considered forest. The inclusion of these vegetation types as forest further biases the estimates of forest cover gain and loss [START_REF] Tropek | Comment on "high-resolution global maps of 21st-century forest cover change[END_REF]. In addition, in the definition developed by [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF], all areas deforested and converted to plantations are classified as forest before the 2000 year, which leads to an overestimation of the total forest area and biases the definition of forest canopy [START_REF] Tropek | Comment on "high-resolution global maps of 21st-century forest cover change[END_REF].

For these reasons, we perform a robustness analysis following [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] that use narrower definitions of forest cover 9, 10 . We report the results of the ATT in Table 8 with the different algorithms. The observation is that the results do not change substantially, they therefore remain robust when we increase the density of the canopy. The Table 8 shows two major facts. First, we see clearly that when we increase the threshold for forest cover loss, resource-backed loans have a significant impact on deforestation. Second, the magnitude of resource-backed loans on deforestation is higher when the canopy threshold is higher than 75% (local linear regression).

Exploring heterogeneity

Many previous studies have attempted to show the negative effect of mining on the environment. For example, [START_REF] Cox | The mining industry as a net beneficiary of a global tax on carbon emissions[END_REF] shows that the global metals and mining industry contributes about 8% of the global carbon footprint. The mining of certain minerals such as copper, nickel, lead, zinc, silver, gold, platinum, palladium, aluminium and steel etc. destroys forest cover, yet forests are essential for the sequestration of CO2.

However, mining could help countries to accelerate the energy transition process in the context of low carbon apology as the minerals will be used to manufacture the electric bacteria that are much recommended for the energy transition. Therefore, the impact of mining on the environment may differ from that of oil exploitation.

We therefore test whether the impact of resource-backed loans on environmental sustainability, in particular forest cover, is heterogeneous with respect to the type of natural resource used as collateral. The results of the ATTs are reported in Table 9. We find that the impact of mineral, tobacco and cocoa-backed loans is positive and statistically 9. [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] use a floss >10% in their baseline regression and floss>15% canopy cover, >20% canopy cover, >25% canopy cover, >30% canopy cover, >50% canopy cover and floss>75% canopy cover in robustness. 10. In this study, we chose two canopy densities : 30% and 75% higher thresholds, to distinguish between areas with closed forests and areas with open forests such as pineapple, soybean or tea plantations.

significant at the 1% level (local linear regression, Panel A), while no significant impact is found for oil-backed loans on forest cover (Panel B).

These results corroborate the literature suggesting that access to extractive industry rents has a heterogeneous impact on forest cover in resource-rich countries [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF]. These results suggest that, while policy makers should be cautious about the environmental impact of resource-backed loans in general, they should in particular avoid borrowing too heavily using loans secured by minerals, tobacco and cocoa.

In addition, particular emphasis should be placed on the "polluter pays" principle developed by Arthur Pigou in 1920. This principle implies that pollution taxes or area taxes should be equal to the marginal damage caused (externalities). To this end, policy makers should strengthen tax systems to obtain a fair share of economic rents to improve forest protection and to promote environmental sustainability. Bootstrapped standard errors based on 50 replications in parentheses. *** p <0.01, ** p <0.05, * p <0.1

In Table 10, we test for potential heterogeneity that might exist between countries according to their given geographical area. Our treatment group contains three countries in the woolly Americas that have signed at least one resource-backed loan agreement compared to eleven countries in sub-Saharan Africa. Over the years, many conservation policies have been adopted in favour of Latin American countries for forest conservation and protection (e.g., conservation funds to protect the Amazon rain-forest in Brazil in 1992 by The Nature Conservancy).

When we desegregate countries by region, we find that the magnitude of resourcebacked loans on forest cover increases in the sub-Saharan African region (Table 10, panel A), while no effect is found in Latin America (Table 10, panel B). Two possible explanations can be drawn from these results. First, the fact that resource-backed loans has no impact on deforestation in Latin America is due to the resources used to repay the loans. Indeed, all Latin American countries have focused their repayment on oil, which does not directly affect the forest, and not on minerals. On the other hand, most mineralbacked loans are signed by sub-Saharan African countries that are destroying forest cover considerably.

Second, all Latin American countries in our sample that have signed a resource-backed loan have benefited from several debt relief programs for nature conservation and conservation funds. With this type of "debt-for-nature swaps" program's, when the creditor country grants debt relief to the debtors, the debtor country is obliged to use the funds for forest conservation, protection and reforestation. The beneficiary country then feels compelled to achieve better results in terms of environmental protection and performance in order to benefit more. This would minimise the risk of deforestation (see for instance d [START_REF] Chamon | Debt-for-climate swaps : Analysis, design, and implementation[END_REF][START_REF] Essers | Debt-for-climate swaps : Killing two birds with one stone ?[END_REF]. We believe that the "debt-for-nature swaps" program's has played a crucial role in the resource-backed loan agreements for Latin American countries. Unlike sub-Saharan African countries, which benefit less from debt relief, their loans are much more heavily weighted towards tobacco and cocoa, which would explain the positive and significant impact in this region (Table 10, local linear regression).

From these results, we encourage not only non-governmental organizations that advocate for the environment to strengthen their actions, help countries in obtaining conservation funds because this allows better environmental management. As for the countries of sub-Saharan Africa, they will have to put in place a very effective mechanism of taxation in the contract of exploitation of extractive industries to compensate for the damage caused by the loans backed on the forest cover.

Endogeneity treatment

We need to address the econometric problems that can arise when estimating the baseline specification. In fact, the forest loss is likely to be endogenous, due to a forest cover measurement error since the deforestation data we used in this study are satellite estimates of global forest change imagery [START_REF] Tropek | Comment on "high-resolution global maps of 21st-century forest cover change[END_REF]. Furthermore, the literature has emphasised the dynamics of the forest over time such that deforestation can be interpreted as a stationary state [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF]. Taking this dynamic into account gives us the following equation :

Lnf orestloss it =α0 + α it + (1 -β)lnf orestloss it-1 + γRBL it + X ′ it δ + ε it (4)
Where Lnf orestloss it is the present value of forest loss in hectares in the country in the year t. α it is a country fixed effect and ε it is the error term. RBL it is our variable of interest.

It represents the supply of man-made capital. Indeed, the underlying assumption is that an increase in resource-backed loans leads to more deforestation as they are necessarily repaid with minerals. X ′ it is a vector of control variables used in the basic model. The presence of the lagged dependent variable among the explanatory variables in Equation 4 creates a dynamic panel bias [START_REF] Nickell | Biases in dynamic models with fixed effects[END_REF] because of the correlation between the lagged dependent variable and the error term. dependent variable and the error term. This bias is particularly important for the panel data set with a short time dimension in such circumstances, a panel fixed effects estimator would not be panel estimator would not be appropriate [START_REF] Roodman | How to do xtabond2 : an introduction to 'difference'and 'system[END_REF].

We follow the literature on dynamic panel data estimation to solve these endogeneity issues using the system-GMM estimator [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF]. [START_REF] Combes | Public spending, credit and natural capital : Does access to capital foster deforestation ?[END_REF] adopt the same approach using the GMM system to estimate expenditure and credit on forest cover for 12 years of panel data from 63 developing countries over the period 2001-2012. [START_REF] Kinda | The effects of extractive industries rent on deforestation in developing countries[END_REF] also used this estimator in their study to estimate the effect of extractive industry rents on deforestation for a panel of 52 resource-rich developing countries over the period 2001-2017.

One of the main advantages of the systemic GMM estimator lies in the fact that this estimation technique can instrument other explanatory variables that could potentially be endogenous in addition to the main regressor which is endogenous. From In addition, with the GMM system estimator, it must be ensured that the total number of instruments does not exceed the number of countries to avoid the problem of «instrument proliferation» in the system. The number of countries to avoid the problem of «instrument proliferation» in estimates [START_REF] Roodman | How to do xtabond2 : an introduction to 'difference'and 'system[END_REF]. We collapse the instruments number to avoid proliferation.

We report the results of the GMM estimation in Table 11. The p-values at 5% of AR(1) and AR(2) and the Hansen tests support the validity of our results. In each specification, the lagged dependent variable is positive, significant at 1% and lower than 1 showing there are no fallacious regression. In column 1, we report the results of our basic model. Then we increase the density of our canopy in column 2 and 3 as before (see Table 8).

After endogeneity treatment, the resource-backed loan variable remains statistically significant at the 1% level with a coefficient that remains positive. Indeed, with the GMM estimator, as the canopy threshold increases, the magnitude of resource-backed loans on deforestation decreases but remains significant. Globally, all results can thus be comparable to the ATTs. In addition, another indirect channel through which resource-backed loans affects forest cover would be commodity prices. In fact, since resource-backed loans are typically denominated in U.S. dollars, a collapse in commodity prices could increase the face value of these loans and cause borrowing countries to exploit more natural resources to repay their loans [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. By way of illustration, the Natural Resource Governance Institute (NRGI) report shows that of the 14 countries that signed resource-backed loans, 10 faced serious debt problems after commodity prices collapsed in 2014 [START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]. As a result, resource-backed loans could put implicit pressure on the country's forest cover. In resource-backed loans contracts, the government can pay less money when commodity prices (or production levels or profits) are low and repay the loan more quickly when conditions are favorable. The idea is to mitigate the volatility of the loans and maintain the debt burden. Indeed, the period of rising commodity prices could lead to a strong expansion of natural resource exploitation, which would be associated with a high risk for the country's forest cover. In fact, the exploitation of natural resources is technically more profitable in times of price increase than in times of price decrease. We check this channel by increasing resource-backed loans with commodity prices from the IMF/World Bank database. Table 13 presents the results. All coefficients are positive and statistically significant (columns [1]-[3]). These results can be explained by the fact that in times of rising prices it is financially profitable to exploit natural resources in order to repay the loans signed. An activity that would cause great damage to the forest cover. In Table 14, we cross-reference desegregated resource-backed loans with commodity prices because resource-backed loans in Table 13 include oil-based loans, which in fact have no significant effect on forest cover (see Table 9 ). The results show that the signing of a loan backed by minerals, tobacco and cocoa, combined with increased commodity prices, leads to the destruction of forest land in the host countries. In this subsection, we examine whether resource-backed loans can harm environmental sustainability by increasing greenhouse gases, or whether they can mitigate climate change by accelerating the energy transition. Resource-backed loans can accelerate the pace of production of minerals, which are essential for making batteries for the energy transition.

Similarly, it is possible that tax revenues from investments made with resource-backed loans could be used to fund renewable energy sources and green projects, which can mitigate the effect of greenhouse gases and likely CO2 sequestration. On the other hand, the fact that resource-backed loans are invested in public infrastructure projects such as roads, schools, hospitals, social housing, dams, bridges, etc., may stimulate demand for energy and thus increase greenhouse gas emissions. Table B7 in appendix presents the results.

Not surprisingly, the results confirm that resource-backed loans increase greenhouse gas emissions in signatory countries as opposed to non-signatory countries (columns [1]-[4]).

We find the same effects when the resource-backed loans are disaggregated and country and the time fixed effects are controlled. These results could be explained by the fact that resource-backed loan borrowing countries tend to accelerate fossil fuel production to meet their resource-backed loan repayment obligations.

Concluding remarks

The relationship between man-made capital and natural capital has given rise to a wide-ranging debate, due to the actions to combat climate change , the increase in environmental issues and the phenomenon «curse of natural resources». [START_REF] Raworth | A safe and just space for humanity : can we live within the doughnut ? Oxfam[END_REF]2017). This could be interpreted as proof of Tinbergen rule that at least one policy instrument is needed for each policy objective. The issue of resourcebacked loans and climate change is an emerging and topical one, as resource-rich countries have to reconcile the dual objectives of finding resources to finance development without compromising environmental sustainability for a healthy planet. There are many avenues of research to be explored on this subject. Indeed, future research could look at the impact of resource-backed loans on the sustainable development index, which measures the ecological efficiency of human development constructed by [START_REF] Hickel | The sustainable development index : Measuring the ecological efficiency of human development in the anthropocene[END_REF]. We intend to explore this avenues of research later. Table B4 -Robustness : The effect of natural resource-backed loans on forest cover loss (Alternative matching : entropy balancing method).

[1]

[2]

[3]

[4]

[5]

[6]

[7]

Figure 1 -

 1 Figure 1 -Location of countries signatories to RBL in the world.

Figure 2 -

 2 Figure 2 -Average of natural resource rent and resource depletion.

Figure 3 -Figure 4 -

 34 Figure 3 -Average of forest loss and natural resources.

Figure 6 -

 6 Figure 6 -Graphs of propensity score by treatment status.

  parentheses. *** p <0.01, ** p <0.05, * p <0.1

  parentheses. *** p <0.01, ** p <0.05, * p <0.1 49

Table 1 -

 1 The share of resources used in repayment agreements in total resource-backed loans over the period2004-2018.

		Resource-backed loans signed by	Resource-backed loans	Total
		Minerals, Tobacco and Cocoa	signed by Oil
	The share of	227%	185%	412%
	resource-backed or about 55% of total GDP	or about 45% of total GDP
	loans in GDP

Source : Author's calculation based on NRGI,

[START_REF] Mihalyi | Resource-backed loans : pitfalls and potential[END_REF]

dataset.

4. Extractive Industries Transparency Initiative report (see :https://www.reuters.com/ article/us-guinea-mining-china/china-to-loan-guinea-20-billion-to-secure-aluminium -ore-idUSKCN1BH1YT)

Table 2 -

 2 Characteristic of treatment and control groups In this table of matching covariates, country-year observations where an resourcebacked loans exists (the treatment group) are in column [1] and country-year observations where no resource-backed loans exists (the potential control group) are in column[2].Column [3] reports the differences in means between the treatment and control groups.

		[1]	[2]	[3]=[2]-[1]
	Variables	Mean in treated Mean in Untreated Difference
	Natural resources depletion	10,74	4,35	-6,39***
	Natural resources rents	18,77	7,92	-10,85***
	Log GFC	3,15	3,09	-0,06
	Log Credit	2,6	2,9	0,3***
	Expenditure	14,72	14,18	-0,54
	Oda	3,41	5,2	1,79***
	Note : *** p<0.01, ** p<0.05, * p<0.1
	p

Table 3 -

 3 Descriptive Statistics

	Variable	Obs	Mean	Std. Dev.	Min	Max
	Dependent variable
	Log Forest loss	890	8.987	3.261	0	15.498
	Treatment variable
	Resource-backed loans	960	0.139	0.346	0	1
	Control variables
	Log Credit	932	2.891	0.905	-4.903	4.666
	Oda	946	5.852	7.74	-2.313	92.141
	Log GFC	811	3.093	0.398	0.422	4.375
	Expenditure	800	14.135	5.975	2.047	79.169
	Natural resources rents	947	9.426	10.573	0	62.697
	Natural resources depletion 922	5.136	8.393	0	71.291

Table 4 -

 4 Estimation of the propensity score

	Dependent variable : RBL	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]
	Log Credit	-0.0143	-0.0604	-0.0651	0.0385	-0.0192	0.0659	-0.0769	0.0482	-0.0123
		(0.1004)	(0.1017)	(0.1066)	(0.1104)	(0.1091)	(0.1092)	(0.0991)	(0.1066)	(0.1030)
	Oda	-0.0690*** -0.0809*** -0.0878*** -0.0626*** -0.0515*** -0.0725*** -0.0459*** -0.0737*** -0.0757***
		(0.0165)	(0.0184)	(0.0183)	(0.0172)	(0.0172)	(0.0166)	(0.0161)	(0.0171)	(0.0172)
	Log GFC	-0.0373	0.0375	0.0231	0.0207	0.0031	-0.3141*	0.0395	-0.0772	-0.0675
		(0.1573)	(0.1775)	(0.1649)	(0.1690)	(0.1739)	(0.1770)	(0.1589)	(0.1596)	(0.1590)
	Expenditure	0.0277***	0.0266**	0.0236*	0.0249**	0.0552***	0.0364*** 0.0291***	0.0231**	0.0361***
		(0.0098)	(0.0105)	(0.0124)	(0.0109)	(0.0129)	(0.0106)	(0.0099)	(0.0098)	(0.0113)
	Natural resources rents	0.0859*** 0.1120*** 0.1171*** 0.0893***	0.0828***	0.0785*** 0.0756*** 0.0902*** 0.0831***
		(0.0113)	(0.0135)	(0.0139)	(0.0119)	(0.0121)	(0.0118)	(0.0116)	(0.0117)	(0.0114)
	Natural resources depletion	-0.0548*** -0.0819*** -0.0885*** -0.0563*** -0.0309** -0.0532*** -0.0487*** -0.0557*** -0.0518***
		(0.0127)	(0.0157)	(0.0160)	(0.0131)	(0.0140)	(0.0130)	(0.0131)	(0.0131)	(0.0128)
	Public Debt		0.0203***
			(0.0068)
	Log Gdp per capita			-0.0525*
				(0.0269)
	Public Debt (t-1)				0.0153**
					(0.0068)
	Log Demographic growth					0.4805***
						(0.0628)
	Rich countries						0.6382***
							(0.1787)
	Minerals reserve horizon							0.9162***
								(0.1876)
	Rainfall shocks								-0.0031***
									(0.0011)
	Temperature shocks									0.0499**
										(0.0237)
	Constant	-1.6774*** -1.8683*** -1.3160** -2.0737*** -10.3363*** -1.4788** -1.8996*** -1.3586** -2.8938***
		(0.5598)	(0.6077)	(0.6166)	(0.5984)	(1.3070)	(0.5744)	(0.5573)	(0.5790)	(0.8100)
	Pseudo R2	0.1894	0.2368	0.2325	0.2062	0.3112	0.2103	0.2229	0.2024	0.1968
	Observations	776	771	742	722	776	776	761	776	776
	Standard errors in parentheses *** p <0.01, ** p <0.05, * p <0.1

Table 5 -

 5 Average Treatment Effect on Treated (ATT)

	Kernel	Matching		1.4115***	(0.4142)		635		0.007	2.45	0.946
	Dependent Variable : 1-Nearest Neighbor 2-Nearest Neighbor 3-Nearest Neighbor Radius Matching Local lineair regression	Matching Log forest loss Matching r=0.005 r=0.05 r=0.01	Baseline	1.3083*** ATT 1.6365*** 1.5401*** 1.4751*** 1.5311** 1.4389*** 1.6501***	(0.3645) (0.5976) (0.5547) (0.4171) (0.6206) (0.3466) (0.5059)	Observations	635 Untreated 635 635 635 635 635 635	Matching Quality	0.010 Pseudo-R2 0.010 0.008 0.009 0.087 0.007 0.051	2.4 Rosenbaum bounds sensitivity test 2.05 2.15 2.5 1.85 2.5 2.1	0.860 Standardized bias (p-value) 0.860 0.905 0.894 0.014 0.937 0.120	Bootstrapped standard errors based on 50 replications in parentheses. *** p <0.01, ** p <0.05, * p <0.1

Table 4

 4

	(column [2]-[9]). The re-

Table 6 -

 6 Robustness of ATTs with the use of additional control variables.

	Dependent Variable : 1-Nearest Neighbor 2-Nearest Neighbor 3-Nearest Neighbor Radius Matching Local lineair regression Kernel	Log forest loss Matching r=0.005 r=0.05 r=0.01 Matching Matching	Baseline	[1]ATT 1.6365*** 1.5401*** 1.4751*** 1.5311* 1.4389*** 1.6501*** 1.3083*** 1.4115***	(0.5976) (0.5547) (0.4171) (0.6206) (0.3466) (0.5059) (0.3645) (0.4142)	Observations	Untreated 635 635 635 635 635 635 635 635	Excliding countries with	[2] Forest froduct export ban 1.5163* 1.3467 1.7255*** -0.4744 0.2052 -0.7595 1.5428* 0.2513	(0.8998) (0.9406) (0.6268) (2.1421) (0.9534) (1.2426) (0.6122) (1.040)	[3] Domestic natural forest logging ban 1.3958** 1.1740** 1.1624** 0.9939 0.9482* 0.9823* 0.8148* 0.9486**	(0.6386) (0.4681) (0.5081) (0.8119) (0.5112) (0.5916) (0.4469) (0.4901)	[4] Sawnwood Export Ban 1.2576** 1.6413* 1.5225*** 2.2579*** 1.6960*** 2.2153** 1.7433*** 1.6674***	(0.6245) (0.6452) (0.5010) (0.7150) (0.3662) (0.5922) (0.4863) (0.4314)	Adding control variables	[5] Adding Public Debt 0.7522* 0.8738** 0.9919* 1.4455* 0.9971*** 1.2347*** 0.9679*** 1.0243***	(0.4326) (0.4625) (0.4174) ( 0.5768) (0.3391) (0.4635) (0.3642) (0.3791)	[6] Adding log Gdp per capita -0.2629 -0.1359 -0.1354 -0.4148 0.0621 -0.3985 0.0429 0.0393	(0.3753) (0.4243) (0.4568) (0.5729) (0.3078) (0.4357) (0.3755) (0.2978)	[7] Adding Public Debt (t-1) 0.8751* 1.0943* 1.2164** 1.1915* 1.3042*** 1.4021* 1.1982*** 1.3113***	(0.5291) (0.4612) (0.5435) (0.6629) (0.49135) (0.5452) (0.3298) (0.4933)	[8] Adding Log Demographic growth -0.1688 -0.2306 -0.1562 -0.3219 -0.0853 -0.2879 0.0109 -0.0330	(0.5369) (0.4193) (0.3909) (0.5173) (0.3452) (0.5034) (0.3680) (0.3701)	[9] Adding Rich countries 0.5970 0.9624* 1.0041** 1.0738 1.0988*** 1.2087*** 1.1038*** 1.1111***	(0.5960) (0.3989) (0.4324) (0.6711) (0.3300) (0.4137) (0.3674) (0.2824)	[10] Adding Minerals reserve horizon 0.9227* 0.9689* 1.2084*** 1.2143** 1.0196*** 1.3620* 0.8877*** 1.0119***	(0.4924) (0.5109) (0.3836) (0.6098) (0.3253) (0.5274) (0.3363) (0.3785)	[11] Adding Rainfall shocks 1.3601*** 1.3397* 1.3388*** 1.4861* 1.5543*** 1.5824*** 1.4386*** 1.5364***	(0 .4200) (0.5585) (0.3933) (0.5761) (0.4351) (0.4643) (0.3421) (0.3864)	[12] Adding Temperature shocks 1.1218** 1.2439*** 1.1560*** 1.5156* 1.3422*** 1.3572*** 1.2743*** 1.3038***	(0.4022) (0.4872) (0.4278) (0.3945) (0.6313) (0.3954) (0.5130) (0.3615)	Bootstrapped standard errors based on 50 replications in parentheses. *** p <0.01, ** p <0.05, * p <0.1

Table 7 -

 7 Robustness of the ATTs with change of matching method.

	Propensity Score Coarsened Exact

Table 8 -

 8 Robustness of the ATT with alternative the forest definition

	Dependent Variable : 1-Nearest Neighbor 2-Nearest Neighbor	3-Nearest Neighbor		Radius Matching		Local lineair regression	Kernel
	Log forest loss		Matching		r=0.005	r=0.05	r=0.01	Matching	Matching
				Panel A : >25% canopy cover
	Baseline : ATT	1.6365***	1.5401***	1.4751***	1.5311*	1.4389***	1.6501***	1.3083***	1.4115***
		(0.5976)	(0.5547)	(0.4171)	(0.6206)	(0.3466)	(0.5059)	(0.3645)	(0.4142)
	Observations	727	727	727	727	727	727	727	727
				Panel A : >30% canopy cover
	ATT	0.9011	1.2792*	1.5030***	1.5623*	1.4331***	1.7098***	1.2941***	1.3889***
		(0.6928)	(0.5461)	(0.5444)	(0.6277)	(0.4816)	(0.5980)	(0.4307)	(0.3941)
	Observations	722	722	722	722	722	722	722	722
				Panel A : >75% canopy cover
	ATT	1.4538***	1.9148***	1.7643***	2.4374***	1.9863***	2.3103***	1.9090***	1.8884***
		(0.54046)	(0.5271)	(0.4429)	(0.6094)	(0.4126)	(0.6489)	(0.3730)	(0.3928)
	Observations	648	648	648	648	648	648	648	648
	Bootstrapped standard errors based on 50 replications in parentheses. *** p <0.01, ** p <0.05, * p <0.1

Table 9 -

 9 Heterogeneous impacts of resource-backed loans on deforestation by type of resource-backed loans

	Dependent Variable :	1-Nearest Neighbor 2-Nearest Neighbor 3-Nearest Neighbor			Radius Matching	Local lineair regression	Kernel
	Log forest loss		Matching			r=0.005		r=0.05	r=0.01	Matching	Matching
	Panel A : Minerals, tobacco and cocoa backed loans
	ATT	2.4063*		2.4334***	2.4927***	3.1577 ***	3.8842***	3.4395***	2.2840***	3.9391***
		(0.95760)	(0.7331)	(0.7102)		(0.7049)		(0.7357)	(0.7184)	(0.8646)	(0.6661)
	Observations	648		648	648		648		648	648	648	648
	Treated	13		13	13		13		13	13	13	13
	Untreated	635		635	635		635		635	635	635	635
	Panel B : Oil backed loans
	ATT	0.2623		0.3202	0.2006		0.4030		0.6063	0.5405	0.5664	0.5527
		(0.6926)		(0.7958)	(0.5444)		(0.7858)		(0.6222)	(0.7408)	(0.6824)	(0.5215)
	Panel B : Latin America
	ATT	-0.1422	0.4983	0.1485		-0.3283		0.0926	0.4254	0.1980	0.0543
		(1.4070)	(1.0247)	(0.7881)	(1.6414)	(0.9810)	(1.2986)	(1.4866)	(0.8781)
	Observations	252	252	252		252			252	252	252	252
	Treated	28

Table 11 -

 11 Panel two-step system GMM estimation results using different canopy cover.

	Dependent Variable :	Baseline
	Forest loss	>25% canopy cover	>30% canopy cover	>75% canopy cover
		[1]	[2]	[3]
	Log forest loss, Lagged	0.661***	0.721***	0.862***
		(0.038)	(0.028)	(0.026)
	Resource-backed loans	1.977***	1.483**	0.734*
		(0.651)	(0.602)	(0.437)
	Log Credit	-0.440**	-0.415*	-0.481*
		(0.205)	(0.234)	(0.280)
	Oda	-0.204***	-0.195***	-0.152***
		(0.028)	(0.033)	(0.029)
	Log GFC	-1.917***	-2.317***	-0.800*
		(0.442)	(0.464)	(0.414)
	Expenditure	0.003	0.066**	-0.059**
		(0.021)	(0.030)	(0.026)
	Natural resources rents	0.223***	0.215***	0.052*
		(0.039)	(0.042)	(0.028)
	Natural resources depletion	-0.269***	-0.231***	-0.128***
		(0.050)	(0.049)	(0.034)
	Constant	10.398***	10.026***	6.579***
		(1.455)	(1.505)	(0.830)
	Number of observations	678	676	596
	Number of countries	54	54	50
	Number of instruments	39	39	39
	AR(1) test, p value	0.000	0.000	0.001
	AR(2) test, p value	0.218	0.283	0.183
	Hansen, (p-value)	0.120	0.146	0.188
	Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1, GMM-System -Generalized Method of
	Moments estimator with country fixed effects.The study period is 2004-2018. The lagged Forest Loss is
	endogenous ; resource-backed loans are strictly exogenous while other variables are considered

Table 12 -

 12 Panel two-step system GMM estimation results by of resource-backed loans. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1, GMM-System -Generalized Method of Moments estimator with country fixed effects.The study period is 2004-2018. The lagged Forest Loss is endogenous ; resource-backed loans are strictly exogenous while other variables are considered.

	Dependent variable :	Mineral, tobacco and	Oils
	Log forest loss	cocoa backed loans	backed loans
		>25% canopy cover	>25% canopy cover
		[1]	[2]
	Log forest loss, lagged	0.694***	0.741***
		(0.028)	(0.025)
	Resource-backed loans	3.738*	0.809
		(1.917)	(0.678)
	Log Credit	-0.287	-0.466**
		(0.180)	(0.230)
	Oda	-0.188***	-0.208***
		(0.029)	(0.028)
	Log GFC	-1.657***	-2.282***
		(0.380)	(0.430)
	Expenditure	0.046	0.067**
		(0.028)	(0.028)
	Natural resources rents	0.187***	0.237***
		(0.042)	(0.050)
	Natural resources depletion	-0.169***	-0.257***
		(0.045)	(0.058)
	Constant	8.264***	10.039***
		(1.070)	(1.446)
	Observations	598	640
	Number of countries	52	53
	Number of instruments	39	39
	AR(1) test, p value	0.000	0.000
	AR(2) test, p value	0.273	0.139
	Hansen, (p-value)	0.186	0.249

The main channels through which resource-backed loans can affect forest cover

  Baseline estimates have shown that resource-backed loans are associated with greater loss of forest cover. We identified two channels that could amplify the effect of resourcebacked loans : Over-indebtedness and natural resource depletion. In fact, resource-backed loans are likely to exacerbate debt or even make it unsustainable. While unsustainable public debt significantly limits the government's ability to address climate change and fund environmental action. In addition, resource-backed loans may also accelerate the depletion of natural resources through an increase in material footprint, which puts great pressure on forest cover. This section tests the two identified channels by estimating the impact of resource-backed loans on public debt and natural resources depletion. we reestimate our baseline model using a simple OLS estimator, and replacing our dependent variable with the potential channel. Results presented in TableB6in appendix show that signing resource-backed loans increases public debt level by 0.84 percentage points and natural resource depletion by 1.34 percentage points. These results confirm our two main channels and show that the destructive effect of resource-backed loans on forest cover can be triggered by excessive debt and natural resource depletion.

Table 13

 13

	-Resource-backed loans, commodity prices and forest cover
		[1]	[2]	[3]
	VARIABLES	Loss forest cover
	RBL*Commodity term of trade 1 0.0186***
		(0.0033)
	RBL*Commodity term of trade 2		0.0211***
			(0.0035)
	RBL*Term of trade (WDI)			0.0079***
				(0.0017)
	Constant	8.9328*** 8.9163*** 8.9697***
		(0.1139)	(0.1139)	(0.1118)
	Observations	862	862	877
	R-squared	0.0355	0.0400	0.0253
	Standard errors in parentheses. *** p <0.01, ** p <0.05, * p <0.1

Table 14 -

 14 Commodity prices and minerals,tobacco and cocoa backed loans

	[1]	[2]	[3]

Does resource-backed loans mitigate the destabilizing effect of greenhouse gases ?

  So then, our estimation results support the existence of a complementary relationship between resource-backed loans and natural capital. The main recommendations of this paper suggest that resource-backed loans and especially mineral, tobacco and cocoa backed loans arrangements should combine environmental instruments with economic instruments for the implementation of sustainable development objectives. Signatory countries should include and/or improve tax instruments in resource-backed loans contracts to compensate for environmental damage because any vision of development must fit within planetary limits, in other words, resources must be mobilized to improve human well-being, but without violating the parameters of sustainability

	The literature review,
	both theoretical and empirical on this topic, is divided into two streams highlighting how
	man-made capital impacts natural capital. A group of researchers indicates that there is
	a substituability relationship between artificial capital and natural capital. On the other
	hand, some researchers find a complementary relationship between these two types of
	capital. These results show that the relationship between these two types of capital is
	ambiguous. Building on what has been argued in the literature, this paper estimates the
	impact of resource-backed loans on forest cover in developing countries. The analysis fo-
	cuses on the direct and indirect effects of resource-backed loans on forest cover loss, with a
	focus on comparing these impacts according to the nature of the resource-backed loans. Re-
	gressions are run using propensity score matching on panel data and generalized moment

methods (GMM). Estimation results indicate that resource-backed loans are associated with increased forest cover loss. While resource-backed loans represent a good opportunity to finance critical development needs, they can also put pressure on borrowing countries to increase the rate of resource production in order to repay the loan, a factor that could affect environmental sustainability.

Table A2 -

 A2 List of resource-backed loans

	Country	Loan year	Loans in millions Resources used	loans country	loans entity	Sectors targeted
			of USD				for investment
	Angola	2004	2000	Oil	China	Eximbank	Infrastructure
	Angola	2007	500	Oil	China	Eximbank	Infrastructure
	Angola	2007	2000	Oil	China	Eximbank	Infrastructure
	Angola	2009	2000	Oil	China	Eximbank	Infrastructure

Table B3 -

 B3 Summary statistics for all variables

	Variable	Observations	Mean	Std. Dev.	Min	Max
	Dependant variable
	Forest loss	945	118397.46 409805.96	0	5435241
	Treatment variable
	Resource-backed loans (RBL)	960	0.139	0.346	0	1
	Matching variables
	Credit	932	25.378	20.681	0.007	106.26
	Oda	946	5.852	7.74	-2.313	92.141
	GFC	818	23.506	9.245	0	79.401
	Expenditure	800	14.135	5.975	2.047	79.169
	Natural resources depletion	947	9.426	10.573	0	62.697
	Natural resources rents	922	5.136	8.393	0	71.291
	Control variables
	Mineral reserve horizon	915	0.082	.274	0	1
	Rainfall shocks	960	118.014	66.398	7.172	414.894
	Temperature shocks	960	24.483	3.082	12.309	29.376
	Rich countries	960	0.5	0.5	0	1
	Demographic growth	960	21703187 36068539	70387	2.095e+08
	Gdp per capita	915	173.838	462.468	0.437	3139.523
	ER	960	0.094	0.292	0	1
	FR	960	0.156	0.363	0	1
	Public Debt	948	-1.407	14.968	-165.829	154.785
	Government Stability	690	7.79	1.415	4.583	11
	Tax revenue	518	15.459	6.396	3.856	39.988
	Brent oil price	960	75.700	23.733	38.287	112.257
	Term of trade	945	130.853	48.756	21.397	458.574
	Commodity term of trade 1	930	112.983	24.308	55.098	223.025
	Commodity term of trade 2	930	99.886	5.053	65.479	110.784
	Greenhouse gases emissions	960	74378.25	166099	100	1118100

Table B5 -

 B5 Robustness : entropy balancing method with threshold canopy>30%.

	VARIABLES	[1]	[2]	[3]	[4]
	Resource-backed loans	1.8309***	0.2399*	1.6817***	0.1986
		(0.3166)	(0.1345)	(0.3242)	(0.1260)
	Log Credit	1.7497*** 0.6451*** 1.8693*** 0.6230***
		(0.3214)	(0.1196)	(0.3239)	(0.1410)
	Oda	0.0629*	-0.0217*	0.0615*	-0.0148
		(0.0352)	(0.0116)	(0.0366)	(0.0105)
	Log GFC	-2.0568***	0.0253	-2.3103***	0.2268
		(0.5286)	(0.1686)	(0.5077)	(0.1667)
	Expenditure	-0.1235*** -0.0313*** -0.1324*** -0.0270***
		(0.0347)	(0.0081)	(0.0336)	(0.0082)
	Natural resources rents	0.1013***	0.0064	0.1230***	0.0050
		(0.0219)	(0.0071)	(0.0233)	(0.0059)
	Natural resources depletion -0.0310**	-0.0111	-0.0379**	-0.0057
		(0.0158)	(0.0084)	(0.0151)	(0.0083)
	Constant	11.1688*** 10.5534*** 11.8084*** 9.7848***
		(1.5384)	(0.5190)	(1.4921)	(0.4757)
	Observations	725	725	725	725
	R-squared	0.2967	0.9671	0.3197	0.9708
	Time/FE	NO	NO	YES	YES
	Country/FE	NO	YES	NO	YES
	Standard errors in parentheses. *** p <0.01, ** p <0.05, * p <0.1

Table B6 -

 B6 Validity of transmission channels : Resource-backed loans, public debt and natural resource depletion

	[2]
	[1]

Table B7 -

 B7 Resource-backed loans and Greenhouse gases

		[1]	[2]	[3]	[4]
	VARIABLES		Greenhouse gases
	Resource-backed loans	1.1994*** 0.2166*** 1.1919*** 0.0638***
		(0.1707)	(0.0242)	(0.1750)	(0.0207)
	Constant	9.5683*** 11.0219*** 9.5221*** 11.0063***
		(0.0635)	(0.0441)	(0.2301)	(0.0376)
	Time/FE	No	No	Yes	Yes
	Country/FE	No	Yes	No	Yes
	Observations	960	960	960	960
	R-squared	0.0490	0.9946	0.0491	0.9965
	Mineral, tabacco and cocoa RBL 1.1460*** 0.1680*** 1.1247***	0.0079
		(0.2646)	(0.0515)	(0.2670)	(0.0408)
	Constant	9.6724*** 11.0705*** 9.5242*** 11.0556***
		(0.0616)	(0.0642)	(0.2336)	(0.0517)
	Time/FE	No	No	Yes	Yes
	Country/FE	No	Yes	No	Yes
	Observations	960	960	960	960
	R-squared	0.0192	0.9942	0.0208	0.9965
	Oil resouce-backed loans	1.2483*** 0.2225*** 1.2366*** 0.0692***
		(0.1895)	(0.0260)	(0.1940)	(0.0220)
	Constant	9.5979*** 11.0160*** 9.5400*** 11.0006***
		(0.0627)	(0.0452)	(0.2307)	(0.0384)
	Time/FE	No	No	Yes	Yes
	Country/FE	No	Yes	No	Yes
	Observations	960	960	960	960
	R-squared	0.0433	0.9945	0.0436	0.9965
	Standard errors in parentheses.

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

28 28 28 28

CDB Budget support and debt rollover

Appendix B : Data and variable definition