Vulnerability to climate change in sub-Saharan Africa countries. Does international trade matter?

Abstract

In a global context of climate vulnerability, characterized by populations' exposure to extreme temperatures, floods, and droughts, we highlight the role of international trade in Sub-Saharan Africa (SSA) countries resilience to climate shocks. Therefore, this paper examines the direct and indirect role of international trade in climate change vulnerability using a country-time fixed effects model and a panel of 39 sub-Saharan African countries over the period 2000 to 2021. The results obtained using the Two-Way Fixed Effects (TWFE) method indicate that international trade, as a vector of wealth creation, directly reduces climate change vulnerability in SSA. We also find that international trade reduces climate change vulnerability through the channels of GDP, water availability, renewable energy and ICT. However, we show that food insecurity reduces the effect of international trade on climate change vulnerability. We recommend: intensifying trade practices to generate wealth, especially trade in environmental goods and services that allow for measuring, preventing, limiting, and reducing environmental damages. We also propose the implementation of government strategies to prepare for climate change, such as environmental policies. From the above, we have given this study implications not only in scientific terms but also in terms of commercial practice by the state and companies.

1. Introduction

Climate change continues to be an extremely intense phenomenon, characterized by abnormal levels of temperature and precipitation [1]. According to the World Meteorological Organization [2], the increase in temperature in Africa is greater than the increase in the average temperature of the Earth's surface. In addition, 8 of the 10 countries most vulnerable to climate change in the world are located in Southern, Central and West Africa [3]. In the face of these hazards, weak social, economic and governmental support in the fight against climate events exacerbates the climate vulnerability of the most exposed African countries [4]. Similarly, without financial and technical support, climate vulnerability exacerbates existing inequalities between developed and developing countries [5]. Although green funds were established at the 2001 COP, developing countries are still lacking the resources to effectively adapt to climate vulnerability [6].

Stephan and Schenker [7] point out that due to unfavorable terms of trade, developing countries cannot rely on trade to adapt to climate vulnerability, but must instead rely on financial assistance for adaptation, which is often irregular. Indeed, African trade accounts for about 3 % of global exports and imports [8], with SSA accounting for 1.7 % of global trade [9]. However, other statistics show that trade is a vector of wealth creation for SSA countries, as it accounts for almost 50 % of GDP [3]. These statistics thus support recent views on trade adaptation to climate vulnerability [[10], [11], [12]].

From a theoretical point of view, the theory of climate change vulnerability developed by Adger [13] and taken up by Füssel and Klein [14] represents the degree of exposure and sensitivity of populations to climatic shocks, as well as their capacity to adapt. Several factors have been identified as key determinants of climate vulnerability, such as low household income [15] and poor institutional quality [16]. In addition, Rana et al. [17] identify low levels of education, a weak health system and unfavorable living conditions as factors that increase climate vulnerability. Li et al. [18] find that dependence on natural resources and their depletion is at the root of climate vulnerability. Finally, for Dong et al. [19], it is the poor quality of infrastructure that prevents people from coping with extreme floods and droughts. However, while climate vulnerability theory has been applied to tourism [20], gender [21], infrastructure [22] and agriculture [23], to our knowledge it has not yet been applied to international trade.

Moreover, while most studies present international trade as a factor that exacerbates climate vulnerability, our study is one of the few to show that international trade, by its virtues, can constitute an adaptation strategy in the face of climate vulnerability. To do so, we draw on the theory of comparative advantage developed by Ricardo [24]. In the context of environmental economics, this theory shows that international trade is a mechanism for adapting to climate vulnerability for countries with a comparative advantage in the production of green goods and services [25]. This could, for example, allow countries dependent on fossil fuels to import renewable energy.

From the perspective of the empirical literature, Copeland and Taylor [26] separate three effects of international trade on climate vulnerability, namely: the scale effect or the detrimental effect [27,28], the composition effect or the valorization of comparative advantages [7], and the technical or technological effect [10,12,29], positioning international trade as a strategy for adapting to climate vulnerability. Furthermore, the World Trade Organization [3] report indicates that food security, economic growth, renewable energy, water availability, and ICT (phone and internet) are channels through which international trade reduces climate vulnerability.

However, to our knowledge, we do not identify any studies that use these factors as transmission channels between international trade and climate vulnerability. Moreover, very few studies have used food security as a transmission channel between international trade and climate vulnerability [30]. Given the context of SSA which is characterized by the inability of most populations to cope with famine and price volatility, we will instead use food insecurity as a transmission channel between international trade and climate vulnerability, following the approach of Burgess and Donaldson [31] and Verma et al. [32]. Based on the above, our article has four main contributions: (i) A theoretical contribution, as our study is one of the few to link international trade to the theory of climate vulnerability, positioning it not as a factor that exacerbates climate risks, but rather as a mitigation mechanism; (ii) An empirical contribution, as it enriches the literature on the direct role of international trade on climate vulnerability, introducing for the first time economic growth, renewable energy, water availability, and ICT as mechanisms through which international trade reduces climate vulnerability. Moreover, given the poverty-stricken African context, it uses food insecurity as a transmission channel, in contrast to existing studies. The choice of mediating variables is validated by the mediation analysis of Acharya et al. [33], whose validity is tested by Zhao et al. [34] and Baron and Kenny's [35] mediation test; (iii) A methodological contribution, because by using Squalli and Wilson's [36] index as a measure of international trade, it revisits the link between international trade and climate vulnerability; (iv) And finally, a cultural contribution, because it reinforces the perceived ideas according to which ancestral, ethnic and cultural biodiversity constitute a strategy of adaptation to climate vulnerability.

The objective of this paper is to analyze the role of international trade in the climate change adaptation strategy of 39 sub-Saharan African countries over the period 2000–2021 using a country-time fixed effects model. The results obtained using the TWFE estimator indicate that international trade directly reduces the climate vulnerability of SSA countries. Furthermore, we find that international trade reduces climate vulnerability in SSA through the mechanisms of economic growth, renewable energy, water availability, and ICT, while food insecurity increases this vulnerability. The results of such a study are therefore of considerable value, both scientifically, as they add to the literature on the subject, and commercially, as they encourage government and businesses to trade in green goods that are likely to reduce vulnerability.

The rest of the paper is structured as follows. Section 2 presents the stylized facts; Section 3 briefly reviews the existing theoretical and empirical literature; Section 4 presents the empirical strategy; Section 5 presents and discusses the results; Section 6 proposes robustness and sensitivity tests; and Section 7 concludes and suggests some policy recommendations.

2. Stylized facts

This study presents three stylized facts: (i) vulnerability to climate change is more entrenched in SSA; (ii) African international trade is important for growth but remains in deficit; and (iii) international trade is a strategy for adapting to climate vulnerability because of its benefits.

2.1. Climate vulnerability: a reality in SSA

Drawing on the work of Sarkodie and Strezov [4], we show on Fig. 1 below that Africa is the most vulnerable region in the world to climate change. Indeed, vulnerability scores in SSA range from high vulnerability to extreme and severe vulnerability. According to the United Nations [37], high economic dependence on climate-related activities and products, and weak institutions and governance structures are reasons that justify the low adaptive capacity of these countries to climate change vulnerability, in contrast to countries in the North and South. Furthermore, the IPCC [38] explains the vulnerability of SSA countries to their dependence on natural resources, the depletion and scarcity of which have created rivalries that lead to conflict. Indeed, the degradation of these resources, such as forests, soils and water, has led to rivalries between the populations that depend on them by reducing their living conditions. As a result, climate vulnerability, characterized by biodiversity loss, impacts human health, food security, and livelihoods [39].

Fig. 1.

Overall distribution of vulnerability to climate change around the world.

Source: map from climate vulnerability forum on vulnerability in Africa in 2015

2.2. International trade in SSA: scope and limits

According to the WTO [40], international trade plays a very important role in wealth creation in SSA, accounting for almost 50 % of GDP. However, external trade is giving way to a trade deficit characterized by high imports of manufactured goods. Indeed, despite the increase in Africa's trade in goods and services between 2005 and 2019, its share of global exports and imports is about 3 % [8]. According to the United Nations Conference on Trade and Development [9], SSA accounts for only 1.7 % of global international trade, and events such as the 2008 financial crisis and COVID-19 have significantly reduced SSA's weight in global trade.

2.3. International trade and climate vulnerability are negatively correlated

Fig. 2 shows that there is a negative correlation between international trade and climate vulnerability for Sub-Saharan African countries, making trade a strategy for adapting to climate change.

Fig. 2.

Correlation between international trade and climate vulnerability.

Source: authors construction

Even if the WTO [40] report shows that trade degrades the quality of the environment through CO2 emissions, it also shows that trade generates economic growth, promotes food security, encourages the exchange of technological innovations, ensures adaptation to climate change and, above all, allows the popularization and marketing of renewable energies. This can include the promotion of renewable energies such as solar, wind and photovoltaic energy to reduce dependence on fossil fuels. Trade can also contribute to the development of trading platforms for sustainable products such as agricultural products and forest resources [41]. Finally, economic policies can use international trade to force private and public companies to be more responsible by taking into account the environmental impacts of their activities [8].

In the following, we present the theoretical foundations and empirical work of our study.

3. Theoretical analysis of study

We associate the theory of climate vulnerability with that of comparative advantage to highlight the role of international trade in reducing climate vulnerability. The theory of climate vulnerability, developed by Adger [13] and taken up by Füssel and Klein [14], refers to the sensitivity, exposure, and adaptive capacity of populations to climate shocks. Several factors have been identified in the literature as key determinants of climate vulnerability. Identifying income as a determinant of vulnerability, Sarkodie et al. [15] find that high-income economies with good social preparedness and economic governance are not very vulnerable to climate change, whereas low-income developing economies are highly exposed and sensitive to these shocks. For Rana et al. [17], climate vulnerability is compounded by low levels of education, weak health systems, and poor living conditions. Furthermore, Li et al. [18] show that over-dependence on these resources can expose countries to climate vulnerability, particularly droughts, floods, and other extreme weather events, threatening production and income flows.

Paradoxically, they also find that revenues from natural resource extraction can be channeled into climate vulnerability mitigation and adaptation initiatives. Furthermore, Stef et al. [16] show that the quality of institutions through the protection of property rights, citizen participation in elections and freedom of expression, and control of corruption are determinants of climate vulnerability, which is not the case in developing countries. In addition to these determinants, there are specific determinants of vulnerability, such as infrastructure, the poor quality of which prevents people from coping with extreme floods and droughts [19]. However, while climate vulnerability theory has been applied to tourism [20], gender [21], infrastructure [22], and agriculture [23], to our knowledge it has not been applied to international trade. Therefore, from a theoretical perspective, this study aims to integrate international trade into the theory of climate vulnerability for the first time by positioning it as an adaptation mechanism. In this context, we draw on the theory of comparative advantage developed by Ricardo [24], according to which international trade benefits countries that specialize in the production of goods for which they have a comparative advantage over other countries. Adapted to environmental economics, this theory shows that international trade is a mechanism for adapting to climate vulnerability, assuming that climate change alters countries' comparative advantages and trade structures [42]. Gouel and Laborde [43] show that a country with a comparative advantage in solar energy production can export this technology to other countries, which will be able to reduce their dependence on fossil fuels and their greenhouse gas emissions. For De Melo [25], if international trade accelerates green growth, it allows vulnerable countries to specialize in sectors where they have a comparative advantage in green goods and services. Furthermore, according to Randhir and Hertel [44], flood-affected countries can import reconstruction technologies and equipment from countries that are relatively specialized in the production of these types of goods. In addition, hot countries that do not support agriculture can use trade to import agricultural products and specialize in exporting manufactured goods that are less climate-sensitive [11]. From this theoretical review, the following conceptual model can be derived in Fig. 3.

Fig. 3.

Conceptual model.

Source: author

4. Empirical work of the study

The direct and indirect effects of international trade on climate vulnerability are the focus of our empirical work.

4.1. A contrasting empirical literature

A particular focus of the literature on the explanatory factors of climate vulnerability shows that most empirical studies have focused more on institutional determinants and less on economic determinants [45]. Thus, by focusing on international trade, whose impact on climate vulnerability remains very ambiguous, we seek to enrich the literature on this topic. Indeed, according to Copeland and Taylor [26], we can distinguish three effects of international trade on climate vulnerability, namely the scale effect, the composition effect and the technological effect. According to the scale effect, any increase in international trade leads to an increase in industrial production and co₂ emissions, an approach that is consistent with the work of Sánchez-Chóliz and Duarte [27] and Lin and Sun [28].

However, with respect to the work of Stephan and Schenker [7], which shows that food production can move to regions that have the comparative advantage of being less vulnerable to climate change, the composition effect assumes that international trade shifts the production of goods and services according to the different comparative advantages of countries. The technological effect shows that international trade can reduce the cost of certain technologies, change production methods and reduce the level of co₂ emission intensity. The work of Shahbaz et al. [29] and Muhammad et al. [10] is part of this approach. However, more recent work in the African context, developed by Casella and De Melo [12], shows that international trade reduces the magnitude of damage associated with extreme drought events, because trade makes it possible to improve production levels of existing crops, to change land use patterns and to relocate labor to urban areas. First, our study aims to distinguish itself from works that present trade as a factor that exacerbates climate vulnerability [27,28]. Second, with the exception of the study by Mignamissi et al. [46], the limitation of most of these studies is based on the measurement of international trade of the level of imports and exports of goods. In fact, the study by Mignamissi et al. [46] measures international trade using the Squalli and Wilson index [36], which takes into account the weight of each country in international trade. Finally, in contrast to their work, which positions international trade as an aggravating factor of climate vulnerability, we will re-examine the link between these variables using the Squalli and Wilson index [36], trying to position trade as a strategy for adapting to climate vulnerability.

4.2. Highlighting transmission channels

The World Trade Organization [40] report identifies economic growth, renewable energy, information and communication technologies, water availability and food security as mechanisms by which international trade reduces climate vulnerability. Except for food security, our study is to our knowledge the first to empirically verify the role of these channels in the relationship that links trade to climate vulnerability.

The first channel is economic growth which according to the WTO [40] is a source of wealth creation for countries engaged in international trade. Indeed, studies by Irwin [47] reveal that developing economies that opened up to trade have, on average, enjoyed a 1 to 1.5 percent higher growth rate. Thus, higher economic growth can, in turn, provide financial support seen as preparation for adaptation.

According to the International Telecommunication Union [48], mobile, internet, and data telecommunications services play a key role in managing climate disasters. The WTO [8] shows that trade in telecommunications services helps businesses, citizens and governments prepare for climate shocks. Thus, commercial liberalization of telecommunications services can improve efficiency and facilitate the provision of more affordable, better quality and varied services, which can serve to support adaptation to climate change [8]. From the above, we admit that ICT constitutes the second channel through which international trade reduces climate vulnerability.

International trade in renewable energy can help support the transition to a low-carbon economy and encourage environmental innovation [40]. Furthermore, Garrett-Peltier [49] argues that the transition to a low-carbon economy could create employment-friendly business opportunities because the renewable energy sector is more labor-intensive than the fossil fuel sector. These arguments then position renewable energies as a mediating variable between international trade and climate vulnerability.

We believe that water availability is one channel through which trade reduces climate vulnerability. Indeed, water trade takes place through the commercial exchange (import or export) of water-intensive animal and plant products [50]. This virtual exchange of water allows for responding to local shortages [51] and compensating for the unequal distribution of water among countries [52] by storing water reserves from products.

Baldos and Hertel [30] point out that the integration of trading platforms and the removal of tax distortions increase food security by helping to reduce the volatility of agricultural prices on the one hand, and the vulnerability of populations to climate shocks on the other hand. Burgess and Donaldson [31] show that international trade resulting from the transport of crops from less climate-vulnerable regions to more climate-vulnerable regions reduces the volatility of local agricultural prices and thus climate vulnerability. For Verma et al. [32], free trade, characterized by the removal of tariffs on imports, cushions climate vulnerability by reducing commodity volatility. However, in the SSA context, characterized by the population's inability to cope with famine and price volatility, we use food insecurity as the transmission channel between international trade and climate vulnerability.

Based on theories and direct analyses of the impact of international trade on vulnerability, we formulate the following hypothesis:

International trade reduces vulnerability to climate change in sub-Saharan Africa.

Regarding the transmission channels through which international trade reduces climate vulnerability, we formulate the following hypothesis 2:

International trade affects climate vulnerability through economic growth, renewable energy consumption, ICT, water availability and food insecurity.

5. Study methodology

In our methodology, we present the study variables, data and analysis model.

5.1. Descriptions of the study variables

vul_it represents vulnerability to climate change and measures the ability or inability of a system to cope with extreme climate shocks. We use the climate change vulnerability indicator provided by the Nostras Damus Global Adaptation Index [53] database. This index assesses a country's vulnerability to climate change by considering six key sectors: food, water, health, ecosystem services, human habitat and infrastructure. For each sector, there are six indicators divided into three components: exposure, sensitivity and the sector's capacity to adapt to climate risks. The country climate vulnerability index is an arithmetic average of the climate change vulnerability indicators of different sectors [54]. In the work of Sarkodie and Strezov [4], the climate vulnerability index is normalized from 0 (less vulnerable) to 1 (more vulnerable). Appendix 1 presents the methodology used to calculate the climate vulnerability index.

Trade_it is trade openness measured by the Squalli and Wilson index [36], whose construction steps are presented in Appendix 2. According to these authors, the openness rate of each country must be corrected for its weight in world trade. The index is calculated using data on exports (X_i), imports (M_i) and GDP (Y_i) from the World Development Indicator Database [55].

Data on other control variables such as economic growth, migrant remittances, ICT (internet and phone), education, renewable energy, and water availability are taken from the World Development Indicator [55].

remf_it represents migrants' remittances and the share of income earned abroad. According to Randazzo et al. [56], migrant remittances directed towards the acquisition of durable goods reduce vulnerability to climate change.

Conf_it, which measures conflict, comes from the Armed Conflict Location and Event Data [57], a geo-referenced dataset that collects information on the number of conflict-related events and the number of deaths [58,59]. Buhaug and von Uexkull [60] find that armed conflict increases climate vulnerability.

Fl_it represents deforestation as measuring the amount of forest lost, with geo-referenced data taken from Hansen's Global Forest Change [61] database. For Alves de Oliveira et al. [62], deforestation increases climate vulnerability.

Coc_it, represents control of corruption, and is taken from the World Governance Indicator database [63]. Ang and Fredriksson [45] show that corruption increases climate vulnerability by reducing environmental policy stringency and environmental quality, and vice versa.

Based on data from the Climate Change Knowledge Portal [64], VAT_it and VAP_it represent, respectively, the variation of average temperatures and precipitation obtained by calculating the standard deviations of monthly temperatures and precipitation [65,66]. Chen et al. [54] assert that climate change i.e. abnormal temperatures and precipitation increases climate vulnerability.

To test the sensitivity of our work to the basic results and to identify potential transmission mechanisms, we add other control variables.

GDP_it is the rate of economic growth per capita. According to Formetta and Feyen [67], there is a negative relationship between GDP and climate vulnerability.

ICT_it is measured by internet (Int_it) and mobile phone (phone_it), representing the population's internet subscription rate per hundred people and the population's mobile phone subscription rate in the last three months, respectively. For Qureshi [68], ICT_it reduces climate vulnerability to the extent that the government and the population can quickly help disaster victims thanks to the use of phones and social networks. Educ_it is education measured by the number of students enrolled in secondary school. Muttarak and Lutz [69] point out that educated individuals and societies respond better to climate-related natural disasters. Re_it represents renewable energy consumption, and measures the share of renewable energy in total final energy consumption. According to Suman [70], the promotion and use of renewable energy is a strategy for adapting to climate change.

Wat_it represents the availability of fresh water. For Nyiwul [71], water availability reduces vulnerability to climate change. FIit represents food insecurity, measured by food price volatility [72], obtained by calculating annual standard deviations on monthly FAOSTAT [73] wheat consumer price index data. Nyiwul [71] shows that food insecurity reduces the capacity of states to adapt and mitigate states to climate change.

The table in Appendix 4 gives more information on the variables, their different sources, as well as descriptive statistics. These statistics overall show small variations between the variables (Appendix 4) meaning that the data results in unbiased results. Furthermore, the analysis of the correlation matrix in Appendix 5 validates the expected direction of the correlation (negative) between international trade and climate vulnerability on the one hand, and between the control variables and climate vulnerability on the other hand. However, a low correlation between the explanatory variables could indicate independence and the absence of multicollinearity. This intuition is confirmed by the VIF (variance of inflation factor) test in Appendix 6, which confirms the absence of multicollinearity between the variables (the VIF statistic is less than 10 %). Furthermore, our model does not suffer from any heteroskedasticity problem if we refer to the respective tests included in Appendix 6.

5.2. The study model

We distinguish the theoretical model from the empirical model.

5.2.1. The theoretical model of the study

Our theoretical model is inspired by the theory of climate vulnerability developed by Adger [13], whose climate vulnerability is a function of social vulnerability (explained by the level of poverty, income, adjustment of means of subsistence to climate shock, and the institutional and political framework) and environmental risks (climate risks such as strong winds and abnormal temperatures and precipitation). The model is written:

$$Vulnerability\text{to}\text{climate}\text{change}=\mathrm{f}\left(\text{social}\text{vulnerability};\text{environmental}\text{risk}\right)$$ (1)

An approach to this model is given by Sarkodie and Strevoz [4] who identify economic, social factors and governance as determinants of climate vulnerability. The model is written:

$$Vulnerability\text{to}\text{climate}\text{change}=\mathrm{f}\left(\text{Economic};\text{governance};\text{Social}\right)$$ (2)

5.2.2. The empirical model

Following the empirical work of Li and Tu [74], we construct a country-time fixed effects model1 that identifies international trade as an economic determinant of climate vulnerability reduction. In contrast to cross-sectional models, country-time fixed effects panel data models have the advantage of containing a great deal of information that is useful for regression [75]. In addition, these models make it possible to mitigate the endogeneity problems associated with omitted variables by specifically controlling for unobserved heterogeneity between individuals in the panel [76]. The introduction of the individual fixed specific effect allows for unobserved characteristics that may explain vulnerability to climate change, such as differences in exposure and sensitivity to climate vulnerability across countries. In addition, the time-specific effect has the advantage of controlling for common macroeconomic shocks.

The model is written as follows:

$$Vu{\mathrm{l}}_{it}=\beta +\gamma Trad{e}_{it}+X_{it}\lambda +{\psi }_i+{\rho }_t+{\epsilon }_{it}$$ (3)

In this study, we use data for the period 2000 to 2021 for a panel of 39 countries in Sub-Saharan Africa (Appendix 3). i and t are the individual and time dimensions, respectively, β is the constant, ρ_t and Ψ_i correspond respectively, to the time and individual dimensions, and ε_it is the error term. As a reminder, Vul_it is vulnerability to climate change, Trade_it international trade, and X_it the matrix of control variables presented above.

The writing of this model in its expanded form is given by equation (4):

$$Vu{\mathrm{l}}_{it}=\beta +\gamma Trad{e}_{it}+{\lambda }_{1}rem{f}_{it}+{\lambda }_{2}edu{c}_{it}+{\lambda }_{3}con{f}_{it}+{\lambda }_4{\text{coc}}_{it}+{\lambda }_5{\text{fl}}_{it}+{\lambda }_{6}va{t}_{it}+{\lambda }_{7}va{p}_{it}+{\psi }_i+{\rho }_t+{\epsilon }_{it}$$ (4)

5.3. The estimation technique

In this study, we use the TWFE estimator to attenuate the endogeneity bias linked to the omission of variables by controlling the unobserved heterogeneity between the individuals on the panel [77]. Indeed, thanks to the introduction of the time and individual dimensions, this estimation method mitigates the variables omission bias by controlling for the unobserved heterogeneity among individuals vulnerable to climate change [74]. Specifically, these authors show that the introduction of the country fixed effect (Ψ_i) makes it possible to capture unobserved heterogeneity by removing differences in climate vulnerability between countries, notably differences in terms of exposure, sensitivity and adaptation. In addition, the introduction of the specific time effect (ρ_t) makes it possible to control possible common macroeconomic shocks that occur in all these countries. The choice of this technique is validated by the Fisher test,2 which rejects the null hypothesis of homogeneity of individual dimensions. However, as robustness checks, we use the system generalized method of moments (S-GMM), we redo the TWFE estimations while using the lagged explanatory variables of order 1 and 2 and control cross-sectional dependence with Driscoll-Kraay approach.

6. Study results and discussions

This section presents the results of the direct and indirect effects of international trade on climate vulnerability.

6.1. International trade as a strategy for adaptation to climate vulnerability

For all specifications (1–8) contained in Table 1, the results indicate that there is a negative relationship between international trade and climate vulnerability. Thus, any increase in international trade leads to a reduction in climate vulnerability. On a theoretical level, this result enriches Adger [13] ’s theory of climate vulnerability, and positions international trade as a factor in reducing climate vulnerability. Indeed, the wealth generated by trade could help finance the production of existing crops, change land use, move labor to urban areas, and switch to less polluting industrialization techniques. Furthermore, based on the theory of comparative advantages, these results indicate, thanks to international trade, that SSA countries can reduce their vulnerability to climate shocks by trading with countries for which they have a comparative advantage. For example, the comparative advantage that SSA countries have in terms of raw materials and non-renewable resources can constitute a strategy for adaptation to climate vulnerability. On the one hand, this advantage can allow them to trade with industrialized countries which have a head start and a comparative advantage in the use and production of renewable energies. Thus, in addition to increasing export earnings, this strategy will allow them to save on the cost of importing less polluting or environmentally friendly products, which are exempt from tariffs for developing countries under the Doha agreements. On the other hand, in the face of climatic disasters, imports of basic and essential food products can be used to reduce the vulnerability of affected populations.

Table 1.

Direct effect of international trade on climate change vulnerability in sub-Saharan Africa.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
International trade (SW index)	−0.059∗∗	−0.081∗∗∗	−0.065∗∗∗	−0.072∗∗∗	−0.050∗∗∗	−0.036∗∗∗	−0.036∗∗∗	−0.037∗∗∗
	(0.002)	(0.002)	(0.002)	(0.002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
Remittance fund		−0.005∗∗	−0.004∗	−0.004	−0.005∗∗	−0.008∗∗∗	−0.008∗∗∗	−0.009∗∗∗
		(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Education			−0.022∗∗∗	−0.018∗∗	−0.026∗∗∗	−0.033∗∗∗	−0.033∗∗∗	−0.037∗∗∗
			(0.007)	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)
Conflict				0.015∗∗∗	0.006∗∗	0.004	0.004	0.004
				(0.003)	(0.002)	(0.003)	(0.003)	(0.003)
Control of corruption					−0.027∗∗∗	−0.035∗∗∗	−0.035∗∗∗	−0.031∗∗∗
					(0.006)	(0.006)	(0.006)	(0.006)
Forest loss						0.017∗∗∗	0.017∗∗∗	0.018∗∗∗
						(0.001)	(0.001)	(0.001)
Variation of average temperature							0.021∗∗∗	0.021∗∗∗
							(0.006)	(0.007)
Variation of average precipitation								−0.0004
								(0.007)
Constant	−0.629∗∗∗	−0.626∗∗∗	−0.606∗∗∗	−0.611∗∗∗	−0.641∗∗∗	−0.729∗∗∗	−0.729∗∗∗	−0.603∗∗∗
	(0.024)	(0.024)	(0.034)	(0.036)	(0.035)	(0.045)	(0.045)	(0.055)
R-square	0.026	0.027	0.028	0.028	0.193	0.205	0.205	0.223
Fisher	1.57∗∗∗	2.84∗∗∗	2.83∗∗∗	3.81∗∗∗	5.33∗∗∗	5.58∗∗∗	5.78∗∗∗	5.59∗∗∗
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
observations	819	819	819	819	819	819	819	819
Number of countries	39	39	39	39	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

Furthermore, by importing and experimenting with green technological services from industrialized countries, SSA countries more endowed with non-renewable energies (polluting or harmful) can improve the quality of their environment. This can include technological services such as construction, operation and maintenance, and the distribution of renewable energy, as well as advice on reducing greenhouse gases. In addition, flood-affected SSA countries can import flood control technologies and reconstruction equipment from countries that are comparatively specialized in the production of these goods.

Another explanation for this result finds its meaning in the work of Copeland and Taylor [26] on the effects of trade on climate vulnerability. Firstly, through the composition effect, international trade constitutes an adaptation strategy to climate vulnerability if SSA countries trade in goods for which they have a comparative advantage over other countries. Indeed, these countries whose economic growth is agricultural as opposed to other countries whose economic growth is industrial, can exchange their raw materials with agricultural seeds and fertilizers more adapted to the extreme climatic conditions, to boost agricultural production. Secondly, through the technical effect, they can import mechanized and cheaper agricultural technologies. Ultimately, all of these analyses are broadly similar to the work of Muhammad et al. [10] and Casella and de Melo [12], which show that trade constitutes a real path towards adaptation to climate change. This result is confirmed by the work of Wang et al. [78] which shows that environmental regulation in terms of reducing climate vulnerability involves not only the reduction of carbon emissions, but also improving high quality international trade.

Likewise, based on the work of Randazzo et al. [56], we find that remittances, that is to say, financing from the diaspora, help reduce climate vulnerability. Indeed, most of the time, SSA populations receive funds from their relatives living in the West to cope with extreme climatic shocks and natural disasters. Similarly, referring to the work of Ang and Fredriksson [45] and Muttarak and Lutz [69], we find, respectively, that the control of corruption and education reduces the vulnerability of SSA countries to climate change. Indeed, the control of corruption can constitute a factor in mitigating climate change to the extent that it ensures the application of the environmental policies implemented. In addition, more educated populations adapt better to climate shocks compared to countries that are not.

However, like the work of Buhaug and von Uexkull [60], Alves de Oliveira et al. [62], and Chen et al. [54], we respectively find that conflict, deforestation and rising temperatures increase climate vulnerability in SSA. Indeed, conflicts weaken populations and reduce their capacity to adapt to climatic shocks. For example, the International Monetary Fund estimates that fragile countries such as the Central African Republic, Somalia and Sudan suffer more from floods, droughts, storms and other climate-related shocks than other, more stable African countries. Furthermore, excessive deforestation in this part of the world destroys the ozone layer and deteriorates ecosystems, which contributes to the fragmentation and disappearance of natural habitats useful to humans. Finally, the rise in temperatures and its effects on the health of populations reduces the capacity of affected populations to protect themselves against climatic shocks. The COP27 report shows, for example, that tropical diseases such as malaria and yellow fever are appearing in previously unaffected regions of Africa as a result of rising temperatures.

6.2. Potential transmission mechanisms and mediation analysis

World Trade Organization [40] report indicates that food security, economic growth, renewable energy, water availability, and ICT (phone and internet) are channels through which international trade reduces climate vulnerability. Therefore, we include these variables in our base model to test their sensitivity to the base results.

We find, like Formetta and Feyen [67] that, there is a negative relationship between economic growth and climate vulnerability. Therefore, any increase in economic wealth helps reduce the vulnerability of SSA countries to climate change. These results are contrary to those found by Zhou [79] in the context of a country like China whose economic growth is essentially a source of environmental pollution and climate vulnerability.

Like Suman [70] and Nyiwul [71], we find, respectively, that consumption of renewable energy and water availability reduce the vulnerability of SSA countries to climate change. Also, the production and consumption of renewable energies reduce the vulnerability of these countries to climate change. These are resources (sun, wind, water, etc.) that are, for the most part available in the region, but still underexploited. Also, water availability reduces climate vulnerability to the extent that the multiplication of water points, the preservation of courses and the storage of water allows populations exposed to climate vulnerability to better adapt. As in Qureshi [68], we find that ICT through internet and phone use reduces climate vulnerability in SSA countries. Indeed, thanks to ICT, most climate shocks and natural disasters make the rounds on social networks and digital platforms allowing the government and private individuals to react quickly with donations and humanitarian aid. As shown by Zhang et al. [80], the production of digital infrastructure such as the internet and telephone can strengthen economic resilience. As a result, we believe that this economic resilience could further contribute to addressing climate vulnerability.

Finally, as Nyiwul [71], we find that food insecurity increases climate vulnerability in SSA. Indeed, food insecurity reduces the capacity of SSA countries to adapt to climate shocks. According to the United Nations [81] report, the famine and poverty observed in the region reduce the capacity of populations to reduce climate vulnerability.

Through empirical (presented in sub- section 3.2.2) and econometric literature, we find in Table 2 and for all specifications (1–6) that, all these variables constitute potential transmission channels between international trade and climate change vulnerability. Econometrically, this intuition seems to be confirmed when we observe the direct results regarding the impact of international trade on the vulnerability indicators in these specifications. Indeed, when we introduce these control variables in the model, the magnitude of the coefficients associated with international trade decreases. Similarly, the introduction of these variables improves the value of R² and Fisher, indicating that these variables have modified the density of the effect of international trade on climate vulnerability. Finally, the inclusion of these control variables reduces the significance of the constant, allowing a better interpretation of the results in terms of causal effects, thus reducing any possible endogeneity linked to the omission of variables [82].

Table 2.

Potential transmission channels between international trade on climate change vulnerability.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)
International trade (SW index)	−0.018∗∗∗	−0.016∗∗∗	−0.017∗∗∗	−0.014∗∗∗	−0.017∗∗∗	0.014∗
	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.002)
Remittance fund	−0.006∗∗	−0.005∗∗	−0.006∗∗∗	−0.006∗∗∗	−0.024∗∗∗	−0.007∗∗∗
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Education	−0.027∗∗∗	−0.021∗∗∗	−0.027∗∗∗	−0.025∗∗∗	0.022∗∗	0.039∗∗∗
	(0.009)	(0.007)	(0.007)	(0.008)	(0.007)	(0.006)
Conflict	0.010∗∗∗	0.0180∗∗∗	0.030∗∗∗	0.011∗∗∗	0.004∗	−0.008∗∗∗
	(0.002)	(0.002)	(0.002)	(0.0023)	(0.002)	(0.002)
Control of corruption	−0.036∗∗∗	−0.029∗∗∗	−0.026∗∗∗	−0.033∗∗∗	−0.022∗∗∗	−0.034∗∗∗
	(0.008)	(0.006)	(0.006)	(0.006)	(0.006)	(0.007)
Forest loss	0.016∗∗∗	0.016∗∗∗	0.016∗∗∗	0.017∗∗∗	0.012∗∗∗	0.095∗
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.008)
Variation of average temperature	0.062∗∗∗	0.047∗∗∗	0.074∗∗∗	−0.011	0.010	0.017∗∗
	(0.008)	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)
Variation of average precipitation	−0.019∗∗	−0.014∗	−0.021∗∗∗	−0.014∗	−0.026∗∗∗	−0.004∗
	(0.009)	(0.007)	(0.007)	(0.007)	(0.007)	(0.002)
Growth Domestic Product (GDP)	−0.087∗∗∗
	(0.004)
Internet		−0.008∗∗∗
		(0.002)
Phone			−0.008∗∗∗
			(0.001)
Water				−0.018∗∗
				(0.007)
Renewable energy					−0.001∗∗∗
					(0.0001)
Food insecurity						0.024∗∗∗
						(0.007)
Constant	−0.547∗∗∗	−0.526∗∗∗	−0.558∗∗∗	−0.605∗∗∗	−0.596∗∗∗	−0.482∗∗∗
	(0.064)	(0.024)	(0.054)	(0.060)	(0.052)	(0.052)

R-square	0.201	0.161	0.186	0.151	0.221	0.127
Fisher	4.32∗∗∗	4.60∗∗∗	5.29∗∗∗	4.27∗∗∗	7.08∗∗∗	3.52
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
observations	819	819	819	819	819	819
Number of countries	39	39	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

In the previous analysis, we presented some mechanisms through which international trade affects climate vulnerability. In this part, we want to test the effectiveness of these mechanisms as a mediating variable using the mediation approach developed by Acharya et al. [33]. According to this approach, mediation analysis, which consists of introducing the treatment variable (international trade) and the mediating variable in the base model into the same equation, generally leads to biased and inconsistent estimators. They then propose a test that solves these econometric problems using a two-step procedure. In the first step, climate vulnerability (the outcome variable) is regressed on the mediating variable(s), international trade and a set of control variables. We then obtain the predicted values of the outcome by setting all mediators to zero, this is the “demediated” outcome. In the second step, we regress the “unmediated” outcome on international trade and the control variables that are confounding factors before the treatment. The coefficient associated with international trade is called the average conditional direct effect (ACDE) by Acharya et al. [33]. Thus, a change in the magnitude and significance of the ACDE relative to the base model coefficients reflects the effectiveness of the mediation effect. Following Acharya et al. [33], all regressions are estimated using a bootstrap method with 1000 replications, integrating country and time fixed effects. The estimation results presented in Table 3 show that, for all specifications (1–6), the considered mediating variables constitute transmission channels through which international trade affects climate vulnerability in SSA. Indeed, we find that the magnitudes of the coefficients associated with international trade decrease compared to the baseline model. For example, the impact of international trade on climate change vulnerability is weaker when the influence of mediating variables is extracted.

Table 3.

Mediation analysis.

Dep: climate change vulnerability	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)
ACDE of international trade	−0.019∗∗∗	−0.016∗∗∗	−0.015∗∗∗	−0.0180∗∗∗	−0.013∗∗∗	−0.010∗∗∗
	(0.002)	(0.001)	(0.001)	(0.002)	(0.002)	(0.002)
Constant	−0.625∗∗∗	−0.625∗∗∗	0.693∗∗∗	−0.604∗∗∗	−0.691∗∗	−0.693∗∗∗
	(0.024)	(0.024)	(0.027)	(0.032)	(0.020)	(0.021)
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observation	819	819	819	819	819	819
R2	0.241	0.167	0.110	0.162	0.141	0.134

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction Notes: This table presents the average controlled direct effects of international trade on climate change vulnerability, according to Acharya et al. (2016). These specifications correspond to different mediation variables, namely: (1) GDP per capita; (2) internet; (3) Phone; (4) Water availability; (5) Renewable energy; (6) Food insecurity. Bootstrap standard error in parenthesis. Source: author construction.

The results indicate that international trade transits through economic growth to influence climate change vulnerability. This result validates the idea that through economic growth, that is to say, the wealth created, international trade further reduces the vulnerability of SSA countries to climate change. Furthermore, these results show that economic growth resulting from commercial activities is decisive in adaptation to climate change. For example, Africa's foreign trade in goods in terms of exports and imports corresponded to 43.3 % of its GDP in 2018 [83]. As a result, this result is opposed to the existing literature which makes trade a factor in aggravating climate vulnerability, but positions it through its virtues as a factor in reducing climate risks [40]. Our results are consistent with those of Irwin [47] who reveal that the higher the economic growth of trade, the more numerous the adaptation strategies to climate change.

We further find that through renewable energy consumption, international trade reduces climate vulnerability. Indeed, results show that green energy resources enhance the role of international trade in reducing the vulnerability of SSA countries to climate change. The justification we give for this result is based on the argument that the trade in renewable energies makes it possible to make production processes cleaner by providing access to sustainable and renewable energy sources, a guarantee of the transition towards a low-carbon economy [40]. This result is close to that of Garrett-Peltier [49], for whom reducing climate vulnerability requires the transition to a low-carbon economy, whose commercial opportunities compared to fossil fuels are more favorable to employment.

About the work of Roch and Gendron [50], our results show that international trade transits by water availability to reduce climate change vulnerability. It emerges, as Yang and Zehnder [51] and Hoekstra and Hung [52] think, that trade in water-rich goods reduces climate vulnerability. In the SSA context, this result makes sense given that most of the goods and services exported to these countries are agricultural and require large quantities of water. As a result, the more water resources and reserves are available, the greater the trade in agricultural products, and the less vulnerable populations are.

We also find that the food insecurity constitutes a transmission channel through which international trade influences climate change vulnerability. This result indicates that food insecurity increases the vulnerability of SSA countries, thereby reducing the virtues of trade. Indeed, characterized in the African context by rising food prices and low purchasing power, food insecurity degrades the role of trade in the fight against climate vulnerability. This result is justified by the fact that SSA countries import and consume what they do not produce, but export what they do not consume. As a result, any fluctuation in the prices of imported agricultural products leads to an increase in inflation and the vulnerability of populations to climatic shocks. For example, one of the immediate causes of the crisis of 2008 was the volatility of prices of imported agricultural products, itself caused by unfavorable climatic conditions [84].

Finally, we find that ICT through the use of the internet and mobile phones improves the role of international trade in reducing climate vulnerability. Indeed, according to the Global System for Mobile Communication Association [85], ICT use increased from 0.04 % in 1991 to 26.43 % in 2017 in SSA. Like the WTO (2021c), we find that commercial liberalization of telecommunications services facilitates access to ICTs for all social strata, even those most vulnerable to climate change. Through this channel, access to information such as potential natural disasters and climate risks will facilitate the adaptation of these populations to climate vulnerability. Ultimately, the analysis of transmission channels does not modify our main results, and those of the influence of the control variables.

The preceding analysis indicates the existence of indirect effects of international trade on climate vulnerability. However, it does not make it possible to quantify the magnitude of the indirect effects. To take these aspects into account and complete the previous analysis, we now use structural equation modelling, the logic of which is summarized in Fig. 4.

Fig. 4.

The transmission mechanism of the effects of international on climate change vulnerability.

Source: author's construction

We now test the effectiveness of mediation and measure its magnitude using the approaches of Zhao et al. [34] and Baron and Kenny [35], whose structural equations are included in Appendix 8. According to the approach of Zhao et al. [34], there is no mediation if the coefficient of the indirect effect obtained from the Monte Carlo z-test is not significant. There is full mediation when the indirect effect criterion is significant, but the direct effect of international trade is not. Mediation is partial when, on the contrary, the direct effect is significant and, in particular, complementary when the indirect and direct effects are in the same direction and concurrent when these effects have opposite signs. Baron and Kenny [35] states that mediation is not possible if trade does not affect the mediator and/or if the mediator does not affect climate vulnerability. There is ‘some’ mediation if both of the above effects are significant, in which case (i) mediation is complete if the test of the indirect effect is significant but not the direct effect; (ii) it is partial if only one of the direct and indirect effects is significant; or (iii) neither is significant.

The results in Table 4 and for specifications 1–6 indicate that there is mediation between international trade and climate vulnerability. By the criterion of Zhao et al. [34], we find that the mediating effect of economic growth, ICT (internet and telephone), water availability, renewable energy and food insecurity is partial but concurrent. However, according to Baron and Kenny's [35] criterion, the mediating effect of these different variables is partial. We find that the most important mediations, in terms of total percentage of mediation, are those of renewable energy (27 %) and food insecurity (19 %). The importance of this mediating effect is due to the importance that African governments attach to energy transition and the fight against food insecurity.

Table 4.

Mediation analysis.

Mediation Variables	Economic Growth (1)	Internet (2)	Phone (3)	Water availability (4)	Renewable energy (5)	Food insecurity (6)
Mediated effects of international trade
Step ($X\to M$)	0.243∗∗	0.072	0.149∗∗∗	0.264∗∗∗	2579∗∗∗	−0.124∗∗∗
	(0.123)	(0.044)	(0.051)	(0.040)	(0.466)	(0.016)
Step ($M\to Y$)	−0.001∗∗	−0.007∗∗∗	−0.008∗∗∗	−0.004∗∗	−0.001∗∗∗	0.010∗
	(0.0008)	(0.002)	(0.001)	(0.002)	(0.0001)	(0.006)
Step ($X\to Y$)	−0.006∗∗	−0.007∗∗∗	−0.012∗∗∗	−0.011∗∗∗	−0.013∗∗∗	−0.0077∗∗∗
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Constant	−0.627∗∗∗	0.766∗∗∗	−1.704∗∗∗	−0.616∗∗∗	−0.694∗∗∗	4923∗∗∗
	(0.0132)	(0.214)	(0.100)	(0.005)	(0.013)	(0.080)
Bootstrap replications	500	500	500	500	500	500
Observations	819	819	819	819	819	819
Sobel test (indirect effect)	0.002∗∗∗	0.001∗∗∗	0.001∗∗∗	0.001∗∗∗	0.003∗∗∗	0.001∗∗∗
	(0.001)	(0.009)	(0.000)	(0.003)	(0.000)	(0.001)
RIT	0.006	0.006	0.011	0.010	0.010	0.006
RID	0.006	0.007	0.012	0.012	0.013	0.008
Conclusion of Zhao-Lynch-Chen	Concurrent. Partial mediation	Concurrent. Partial mediation	Concurrent. Partial mediation	Concurrent. Partial mediation	Concurrent. Partial mediation	Concurrent. Partial mediation
Conclusion of Baron-Kenny	Partial mediation	Partial mediation	Partial mediation	Partial mediation	Partial mediation	Partial mediation
% total effects mediated	6 %	9 %	11 %	11 %	27 %	19 %

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

7. Robustness and sensitivity tests

We test the quality of our results by three robustness tests and two sensitivity tests.

7.1. Robustness tests

To test the robustness of our results, we use the S-GMM as an alternative estimator, the non-parametric method and the Driscoll-Kraay [86] approach.

7.1.1. S-GMM as an alternative estimator

Omission of relevant variables, measurement error and reverse causality are the main endogeneity biases that may exist between international trade and climate vulnerability. Indeed, dynamic panel models are confronted with the correlation between unobservable country-specific effects and the lagged dependent variable, which leads to inconsistent estimators under ordinary least squares (OLS). As highlighted by Dauda et al. [87], the S-GMM estimator allows one to take into account possible endogeneity problems, in particular that of unobserved heterogeneity, which is solved thanks to the introduction of the lagged endogenous variable, called internal instrument, among the exogenous variables [88].

Thus, using as instruments the lagged values of the first difference of the endogenous variable, Arellano and Bond [89] developed a consistent estimator, called difference GMM. However, due to the persistence of the dependent variable, this estimator makes lagged values very poor instruments [90]. Using additional moment conditions, Blundell and Bond [90] proposed a more robust alternative estimator called the S-GMM estimator, from a system of two equations, one in level and the other in first difference.

Several arguments justify the use of this estimator in our study: first, our specification respects Roodman's [88] condition that the number of countries (45) must be greater than the number of periods (22). Second, the persistence or inertia condition is verified because there is a strong correlation between climate vulnerability and its past value.3 Third, extending the approach developed by Arellano and Bover [91], Roodman [88] shows that the GMM estimator is biased when the estimation strategy imposes too many instruments and overcomes this problem by limiting the number of instruments and maximizing the sample size using the direct orthogonal deviation technique.

Finally, unlike the GMM estimator, the S-GMM method corrects for endogeneity, heteroscedasticity, and autocorrelation of errors [92]. Thus, in the context of our study, the S-GMM allows for the correction of omitted variable bias through the use of instrumental variables [76]. Specifically, this method controls for unobserved heterogeneity in persistent differences in climate vulnerability across countries, including differences in exposure, sensitivity and adaptation. Furthermore, Liobikienė and Butkus [93] show that the S-GMM estimator allows for the correction of the double simultaneity that may exist between climate vulnerability and all explanatory variables. Moreover, it allows for correcting any endogeneity problem arising from a possible correlation between climate vulnerability indicators and explanatory variables such as water availability, food security, agricultural production and population growth rate. The choice of lagged exogenous variables as instruments puts an end to the debate related to the subjectivity of external instruments, the choice of which is not unanimous in the literature [94]. The results contained in Table 5, for all specifications (1–14) indicate that the use of S-GMM validates the hypothesis of the effect of international trade on the reduction of the climate vulnerability of SSA countries. Furthermore, the results indicate that the effect of the control variables on climate vulnerability is not modified. Furthermore, the absence of order 1 autocorrelation, the presence of order 2 autocorrelation and the significance of the Sargan test validate the robustness of our results.

Table 5.

Effect of international on climate change vulnerability in sub-Saharan Africa: S-GMM as an alternative estimator.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
climate change vulnerability (-1)	0.861∗∗∗	0.882∗∗∗	0.906∗∗∗	0.883∗∗∗	0.719∗∗∗	0.740∗∗∗	0.758∗∗∗	0.885∗∗∗	0.234∗∗∗	0.273∗∗	0.282∗∗	0.482∗	0.698∗∗∗	0.843∗∗∗
	(0.043)	(0.030)	(0.035)	(0.046)	(0.043)	(0.108)	(0.131)	(0.114)	(0.016)	(0.119)	(0.120)	(0.274)	(0.111)	(0.090)
International trade (SW index)	−0.006∗∗	−0.002∗	−0.174∗∗∗	−0.004∗∗	−0.01∗∗∗	−0.008∗∗	−0.007∗∗	−0.007∗	−0.024∗∗	−0.019∗∗∗	−0.019∗∗∗	−0.025∗∗	−0.007∗	−0.048∗∗
	(0.001)	(0.001)	(0.011)	(0.001)	(0.001)	(0.004)	(0.003)	(0.003)	(0.010)	(0.005)	(0.005)	(0.011)	(0.004)	(0.003)
Growth Domestic Product (GDP)		−0.003	−0.004	−0.012∗∗	−0.0009	−0.004∗∗	−0.0002	−0.008	−0.008∗	−0.010∗∗∗	−0.048∗∗∗	−0.013∗∗	−0.050∗∗∗	−0.010∗∗
		(0.002)	(0.003)	(0.005)	(0.002)	(0.002)	(0.001	(0.005)	(0.004)	(0.002)	(0.002)	(0.002)	(0.001)	(0.001)
Remittance fund			−0.003∗∗	−0.006∗	−0.003	−0.008∗∗	−0.007∗	−0.001	−0.020∗∗	−0.001	−0.001	−0.017∗	−0.037∗∗∗	−0.029∗∗
			(0.001)	(0.003)	(0.001)	(0.003)	(0.004)	(0.002)	(0.002)	(0.002)	(0.001)	(0.009)	(0.005)	(0.004)
Education				−0.0008	−0.002∗	−0.024∗∗	−0.014∗∗∗	−0.038∗	−0.017∗	−0.059∗∗∗	−0.085∗∗∗	−0.029	−0.034∗∗∗	−0.036∗∗
				(0.0013)	(0.001)	(0.0107)	(0.002)	(0.020)	(0.010)	(0.007)	(0.007)	(0.045)	(0.009)	(0.015)
Conflict					0.003∗∗∗	0.006∗∗∗	0.004∗	0.007∗∗∗	0.004	0.002	0.001	0.011	0.0002	0.034
					(0.0008)	(0.0002)	(0.002)	(0.002)	(0.003)	(0.002)	(0.002)	(0.008)	(0.002)	(0.002)
Control of corruption						−0.013∗∗	−0.026∗∗∗	−0.019∗∗∗	−0.038∗∗	−0.010	−0.012	−0.028∗∗	−0.049∗∗∗	−0.026∗∗
						(0.002)	(0.003)	(0.001)	(0.001)	(0.011)	(0.012)	(0.002)	(0.001)	(0.007)
Forest loss							0.004	0.003	0.001	0.003∗∗	0.002∗∗	0.009∗	0.032∗∗∗	0.017∗∗∗
							(0.003)	(0.005)	(0.002)	(0.001)	(0.001)	(0.004)	(0.002)	(0.001)
Internet								−0.019∗∗∗	−0.004∗∗	−0.009∗∗	−0.008∗∗∗	−0.007∗	0.014∗∗∗	0.020∗∗∗
								(0.001)	(0.001)	(0.0008)	(0.0009)	(0.004)	(0.001)	(0.001)
Phone									−0.002∗	−0.001∗∗	−0.001∗∗	−0.034∗∗	−0.0002	−0.023∗∗
									0.001)	(0.0007)	(0.0007)	(0.004)	(0.001)	(0.001)
Water										−0.071∗∗∗	−0.080∗∗∗	−0.029∗	−0.013∗	−0.035∗∗
										(0.001)	(0.002)	(0.017)	(0.007)	(0.016)
Variation of average temperature											0.068∗∗∗	0.037∗∗	0.030∗∗	0.028∗∗
											(0.006)	(0.015)	(0.013)	(0.010)

Variation of average precipitation												−0.038	−0.061∗∗	−0.035∗∗
												(0.024)	(0.016)	(0.015)
Renewable energy													−0.009∗∗	−0.014∗∗
													(0.0002)	(0.001)
Food insecurity														0.0160∗∗
														(0.001)
Constant	0.104∗∗∗	0.070∗∗∗	0.066∗∗∗	0.098∗∗∗	0.218∗∗∗	0.0815∗∗	0.174∗∗∗	0.042∗∗∗	0.635∗∗∗	0.533∗∗∗	0.561∗∗∗	0.452∗∗∗	0.237∗∗∗	−0.259∗∗
	(0.0263)	(0.0188)	(0.0222)	(0.0312)	(0.0329)	(0.0409)	(0.0113)	(0.0013)	(0.123)	(0.0991)	(0.0946)	(0.0412)	(0.0125)	(0.114)
Wald	647223	839575.8	744292.3	514685.2	932756.1	192213.1	209402.6	487095.6	709903.66	832746.11	9545268	174020.3	166745.1	238830.0
AR(1)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.002	0.008	0.018	0.002	0.025	0.0217	0.000
AR(2)	0.444	0.458	0.747	0.703	0.531	0.209	0.173	0.697	0.780	0.620	0.661	0.428	0.729	0.287
Sargan p-value	0.522	0.381	0.641	0.819	0.238	0.477	0.752	0.301	0.818	0.893	0.817	0.825	0.489	0.272
Instruments	26	26	31	26	31	25	24	25	21	30	29	25	26	25
observations	726	726	724	724	726	724	722	723	721	701	719	702	704	734
Number of countries	39	39	39	39	39	39	39	39	39	39	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

7.1.2. The non-parametric method: the use of lagged explanatory variables

The nonparametric method, which focuses on lagged explanatory variables and is inspired by the literature [95,96], allows us to test the robustness of the results obtained from the TWFE estimation, to control omitted variables bias. Bellemare et al. [95] show that the use of this technique leads to biased and inconsistent estimators. However, they show that this technique is effective in accounting for endogeneity if two conditions are met. First, if endogeneity is related to the omission of variables, in particular to unobserved heterogeneity, this technique applies when the unobserved factors are not dynamic. This first condition is verified in our study to the extent that differences in sensitivity, exposure and adaptation to climate vulnerability across SSA regions are static. Regarding the second condition, this technique applies when there is a contemporaneous or instantaneous causal relationship between the dependent and independent variables and when the endogeneity is of the reverse causality type. In the context of our study, the relationship between international trade and climate vulnerability is contemporaneous in the sense that the wealth derived from trade can be an important resource for the state in the fight against climate change.

Moreover, Gnimassoun and Santos [97] suggest that this approach ensures the robustness of our results and could mitigate endogeneity to some extent. Therefore, as recommended in the literature [95,96], we estimate an equation with the explanatory variables lagged by one and two periods, including country and time fixed effects. Thus, this method allows us to capture the lagged effects of exogenous variables on climate vulnerability. The results in Table 6 (supported by the results in Appendix 9 for the second-order lag) indicate that our main results confirm and do not modify the effect of international trade and the control variables on climate vulnerability.

Table 6.

Effect of international on climate change vulnerability in sub-Saharan Africa: robustness with lag1 explanatory variables.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
International trade (SW index)t-1	−0.008∗∗∗	−0.008∗∗	−0.007∗∗∗	−0.007∗∗	−0.004	−0.026∗∗	−0.019	−0.021∗	−0.026∗∗	−0.0457∗∗∗	−0.047∗∗∗	−0.043∗∗	−0.047∗∗	−0.048∗∗
	(0.002)	(0.003)	(0.003)	(0.003)	(0.003)	(0.010)	(0.013)	(0.011)	(0.010)	(0.009)	(0.009)	(0.012)	(0.015)	(0.015)
Growth domestic product (GDP)t-1		−0.006	−0.006	−0.005	−0.006	−0.007	−0.007	−0.010	−0.006	−0.003	−0.002	−0.001	−0.001	−0.001
		(0.005)	(0.005)	(0.005)	(0.005)	(0.009)	(0.009)	(0.008)	(0.007)	(0.006)	(0.006)	(0.006)	(0.006)	(0.007)
Remittances fundt-1			−0.005∗	−0.005∗	−0.004	−0.032∗∗	−0.036∗∗∗	−0.037∗∗∗	−0.028∗∗∗	−0.006	−0.009	−0.004	−0.011	−0.014
			(0.003)	(0.003)	(0.003)	(0.011)	(0.012)	(0.011)	(0.010)	(0.009)	(0.010)	(0.012)	(0.019)	(0.019)
Educationt-1				−0.023∗	−0.020∗	−0.010	−0.061	−0.005	−0.033	−0.251∗∗∗	−0.261∗∗∗	−0.257∗∗	−0.253∗∗	−0.210∗∗
				(0.009)	(0.009)	(0.048)	(0.067)	(0.064)	(0.059)	(0.066)	(0.069)	(0.070)	(0.072)	(0.085)
Conflictt-1					0.008∗∗∗	0.048∗∗	0.052∗∗	0.042∗∗	0.058∗∗∗	0.042∗∗∗	0.043∗∗∗	0.044∗∗∗	0.045∗∗∗	0.045∗∗∗
					(0.002)	(0.020)	(0.020)	(0.019)	(0.017)	(0.013)	(0.014)	(0.014)	(0.014)	(0.014)
Control of corruptiont-1						0.010	0.015	0.007	0.006	0.007	0.008	0.011	0.012	0.014
						(0.010)	(0.012)	(0.011)	(0.009)	(0.007)	(0.007)	(0.009)	(0.009)	(0.009)
Internett-1							−0.010	−0.002	−0.0012	−0.004	−0.005	−0.003	−0.005	−0.001
							(0.011)	(0.014)	(0.009)	(0.007)	(0.007)	(0.008)	(0.009)	(0.010)
Forest losst-1								0.016∗∗∗	0.003	0.005	0.004	0.005	0.005	0.006
								(0.005)	(0.006)	(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
Phonet-1									−0.016∗∗∗	−0.013∗∗∗	−0.013∗∗∗	−0.013∗∗	−0.013∗∗	−0.014∗∗
									(0.004)	(0.003)	(0.003)	(0.004)	(0.004)	(0.004)

Watert-1										−0.222∗∗∗	−0.216∗∗∗	−0.184∗∗	−0.212∗∗	−0.199∗∗
										(0.0490)	(0.0505)	(0.0694)	(0.0899)	(0.0911)
Variation of average temperaturet-1											0.0294	0.0488	0.0422	0.0382
											(0.0513)	(0.0593)	(0.0616)	(0.0619)
variation of average precipitationt-1												0.0521	0.0543	0.0821
												(0.0774)	(0.0787)	(0.0841)
Renewable energyt-1													−0.0412	−0.0679
													(0.0843)	(0.0891)
Food insecurityt-1														0.0199
														(0.0210)
Constant	−0.608∗∗∗	−0.56∗∗∗	−0.564∗∗∗	−0.478∗∗∗	−0.458∗∗∗	−0.220	−0.285	−0.133	−0.253	−0.355∗∗	−0.388∗∗	−0.728	−0.417	−0.409
	(0.0214)	(0.0625)	(0.0623)	(0.0707)	(0.0704)	(0.236)	(0.245)	(0.228)	(0.204)	(0.160)	(0.172)	(0.534)	(0.836)	(0.838)
R-square	0.541	0.544	0.632	0.761	0.791	0.802	0.808	0.848	0.895	0.938	0.939	0.940	0.941	0.941
Fisher	1.45∗	1.49∗	1.71∗	3.80∗∗∗	5.02∗∗∗	5.31∗∗∗	5.14∗∗∗	6.38∗∗∗	8.79∗∗∗	14.69∗∗∗	13.79∗∗∗	13.21∗∗∗	12.46∗∗∗	12.54∗∗∗
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
observations	818	818	818	818	818	818	818	818	818	818	818	818	818	818
Number of countries	39	39	39	39	39	39	39	39	39	39	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

7.2. Driscoll-Kraay approach to control geographical spatial correlation

To test for the effects of geographical spatial correlation of climate change, we run the cross-sectional dependence test of Pesaran [98]. The results of this test confirm the existence of cross-sectional dependence in our panel due to interactions between individuals. Several estimators have been developed to deal with this problem, such as White [99], Rogers [100] and Driscoll-Kraay [86]. However, as shown by Joshi et al. (2021), the Driscoll-Kraay [86] estimator is superior to those of White [99] and Rogers [100] in that it provides more conclusive empirical results and allows the problems of autocorrelation of errors and heteroscedasticity to be controlled for.

The results presented in Appendix 10 tend to confirm the previous results, although the magnitude of the coefficients is different.

7.3. Sensitivity tests

Readiness to climate change vulnerability variables and historical and cultural variables are introduced in turn into the model to test the sensitivity of our results.

7.3.1. Sensitivity to readiness variables

For Sarkodie and Strezov [4], economic, social, and governance readiness is very decisive in adaptation to climate vulnerability. The results in Table 7 show that economic readiness reduces the climate vulnerability of SSA countries (equations (1), (2), (3), (4))). This result finds its meaning to the extent that the first response given by actors in the diaspora, the State, and members of civil society in the event of extreme climate shocks is the provision of financial resources [4]. Furthermore, social preparedness reduces climate vulnerability to the extent that, even if adaptation to climate vulnerability on humans is ineffective [54], the first measures taken during natural disasters are taken by local populations before any state intervention. Finally, we find that governance readiness does not reduce climate vulnerability in SSA due to political instability, violence, corruption, and the absence of environmental policies observed in these regions. Furthermore, the introduction of this sensitivity test does not modify the quality of our first results.

Table 7.

Effect of international on Climate Change Vulnerability in sub-Saharan Africa: introduction of readiness variables.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)
International trade (SW index)	−0.0215∗	−0.0603∗∗∗	−0.0487∗∗∗	−0.0217∗
	(0.0123)	(0.0134)	(0.0162)	(0.0122)
Growth Domestic Product (GDP)	0.0304∗∗∗	−0.0616∗∗∗	−0.0225∗∗∗	−0.0325∗∗∗
	(0.00515)	(0.00522)	(0.00607)	(0.00519)
Remittance fund	−0.0134∗∗∗	−0.0140∗∗∗	−0.0115	−0.0568∗∗∗
	(0.00108)	(0.00138)	(0.0165)	(0.0119)
Education	−0.0101	−0.285∗∗∗	−0.245∗∗	−0.963∗∗∗
	(0.0567)	(0.0643)	(0.0892)	(0.0765)
Conflict	0.0340∗∗∗	0.0491∗∗∗	0.0484∗∗∗	0.0450∗∗∗
	(0.0110)	(0.0114)	(0.0137)	(0.0112)
Control of corruption	−0.0114∗	−0.0122∗	−0.0112	−0.00876
	(0.00593)	(0.00500)	(0.00908)	(0.00683)
Forest loss	0.0300∗∗∗	0.0202∗∗	0.00865	0.0158∗
	(0.00732)	(0.00782)	(0.00915)	(0.00931)
Internet	0.0642∗∗∗	0.0314∗∗∗	0.0739∗∗∗	0.00805∗
	(0.00417)	(0.00448)	(0.00530)	(0.00424)
Phone	−0.0140∗∗∗	−0.0116∗∗∗	−0.0119∗∗∗	−0.0148∗∗∗
	(0.00376)	(0.00325)	(0.00389)	(0.00381)
Water	−0.0551	−0.352∗∗∗	−0.163∗	−0.0390
	(0.0517)	(0.0905)	(0.0830)	(0.0689)
Variation of average temperature	0.0947∗∗∗	−0.0912∗	−0.115∗	−0.0952∗∗∗
	(0.0231)	(0.0488)	(0.0626)	(0.0233)
Variation of average precipitation	0.0909∗∗	0.102	0.127∗	0.124∗∗∗
	(0.0434)	(0.0614)	(0.0726)	(0.0458)
Renewable energy	−0.00261∗∗∗	−0.00373∗∗	−0.0184∗∗∗	−0.0188∗∗∗
	(0.000847)	(0.00138)	(0.00154)	(0.00114)
Food insecurity	0.0974∗∗∗	0.0808∗∗	0.0585	0.0909∗∗∗
	(0.0337)	(0.0291)	(0.0361)	(0.0334)
Economic readiness	−0.0603∗∗			−0.0450∗∗∗
	(0.0254)			(0.00698)
Social readiness		−1.370∗∗∗		−0.959∗∗
		(0.395)		(0.463)
Governance readiness			−0.122	−0.00969
			(0.104)	(0.498)
Constant	−1877∗∗∗	−4.312∗∗∗	−1554∗∗	−1543∗∗∗
	(0.275)	(0.275)	(0.689)	(0.316)
R-square	0.911	0.967	0.953	0.917
Fisher	13.99∗∗∗	20.21∗∗∗	14.03∗∗∗	13.66∗∗∗
Country fixed effects	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes
observations	819	819	819	819
Number of countries	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

7.3.2. Cultural values as a strategy for adaptation to climate change

Cultural values such as ancestral biodiversity and beliefs play a vital role in the climate change adaptation strategy [101]. For Heyd and Dupuis [102], beliefs, values, practices, habits, as well as techniques and material elements, interact with the behaviors of social groups who face the challenges posed by climate change. As an illustration, the work of Gélard [103] shows that the Aït Khebbach and Tafilalt tribes in Morocco use a “pot spoon”, transformed into a mannequin and adorned with feminine assets to ask for rain, to be less vulnerable to extreme temperature conditions. To empirically verify the impact of culture on climate vulnerability, we incorporate ethnic and linguistic fragmentation from the Laporta et al. [104] database. The results contained in Table 8 (equations (1), (2), (3))) indicate that culture reduces vulnerability to climate change through ethnic fractionalization. Indeed, as Omang and Nsoga [101] think, ethnic fractionalization, that is to say, the diversity of cultural and ethnic practices is decisive in the fight against climate vulnerability. However, according to equation (3), the results indicate that language and ethnic culture jointly reduce climate vulnerability. This result is important and could indicate that ancestral and traditional knowledge in terms of combating climate vulnerability specific to each tribe can be shared between ethnic groups, through the language that they have in common.

Table 8.

Effect of international on climate change vulnerability in sub-Saharan Africa: introduction of cultural variables.

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)
International trade (SW index)	−0.0328∗∗	−0.0388∗∗	−0.0111∗∗∗
	(0.0125)	(0.0139)	(0.00099)
Growth Domestic Product (GDP)	0.0945∗∗∗	0.0666∗∗∗	0.0367∗∗∗
	(0.00571)	(0.00656)	(0.00427)
Remittance fund	−0.0254∗∗∗	−0.0224∗∗∗	−0.0176
	(0.00150)	(0.00181)	(0.0109)
Education	−0.253∗∗∗	−0.230∗∗∗	0.0472
	(0.0625)	(0.0737)	(0.0411)
Conflict	0.0593∗∗∗	0.0591∗∗∗	0.00211
	(0.0124)	(0.0156)	(0.0116)
Control of corruption	−0.0409∗∗∗	−0.0765∗∗∗	−0.0955∗∗∗
	(0.00818)	(0.00921)	(0.00630)
Forest loss	−0.0586∗∗∗	0.0151∗∗∗	0.0648∗∗∗
	(0.00865)	(0.00929)	(0.00672)
Internet	−0.0639∗∗∗	−0.0679∗∗∗	−0.0231∗∗∗
	(0.00451)	(0.00512)	(0.00346)
Phone	−0.0103∗∗∗	−0.0127∗∗∗	0.00427
	(0.00340)	(0.00377)	(0.00354)
Water	0.0143	−0.0616	0.0102
	(0.0872)	(0.0967)	(0.0469)
Variation of average temperature	0.193∗∗∗	0.158∗∗	0.0922∗∗∗
	(0.0601)	(0.0720)	(0.0216)
Variation of average precipitation	0.177∗∗	0.173∗∗	0.00144
	(0.0636)	(0.0742)	(0.0400)
Renewable energy	−0.0393∗∗∗	−0.0534∗∗∗	0.0809∗∗∗
	(0.00117)	(0.00133)	(0.000737)
Food insecurity	0.0184∗∗∗	0.0460∗∗∗	0.0628∗∗
	(0.00341)	(0.00372)	(0.0294)
Ethnic fractionalization	−0.407∗∗∗		−1.636∗∗∗
	(0.128)		(0.319)
Linguistic fractionalization		−0.119	−0.858∗∗∗
		(0.0747)	(0.153)
Constant	−1947∗∗∗	−1876∗∗	−0.646∗∗
	(0.608)	(0.725)	(0.313)
R-square	0.965	0.955	0.9
Fisher	19.08∗∗∗	14.71	20.83
Country fixed effects	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes
observations	819	819	819
Number of countries	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

8. Conclusion and limitations

The objective of this article was to analyze the role of international trade in reducing climate vulnerability for 45 SSA countries over the period 2000 to 2021. The results obtained by the TWFE method indicate that international trade directly reduces the vulnerability of SSA countries to climate change. The justification that we have given to this result is based on the fact that contrary to theses which present international trade as a factor of aggravation of risks, we show that the wealth created by international trade can be used by the State to fight against climate vulnerability through development strategies oriented towards less polluting sectors of activity. Furthermore, we found that through economic growth, renewable energies, water availability and ICT (internet and phone), international trade indirectly reduces climate vulnerability. However, we found that food insecurity diminishes the effect of trade on reducing climate vulnerability in SSA. From these results, we were able to identify several contributions: first, (i) a theoretical contribution, because we associated international trade for the first time with the theory of climate vulnerability, and positioned it as a strategy of adaptation; Then, (ii) A methodological and empirical contribution; methodological since our article revisits the existing link between international trade and the environment using for the first time the Squalli and Wilson [36] index, and empirical because our article introduces for the first time economic growth, renewable energies, water availability and ICT as mechanisms by which international trade reduces climate vulnerability and opposes past analyzes presenting international trade as a factor in aggravating climate risks; and finally (iii) a cultural contribution because it reinforces the perceived ideas according to which ancestral, ethnic and cultural biodiversity constitutes an adaptation strategy to climate vulnerability. Also, the use in this article of various robustness and sensitivity tests has in no way modified the quality of our main results and the influence of the control variables. Based on the above, we propose several economic policy recommendations. First of all, we recommend scaling up business practices to create wealth. We especially recommend trade in environmental goods and services which, make it possible to measure, prevent, limit and minimize environmental damage, such as water pollution, air and soil, as well as problems related to waste, noise and ecosystems. Moreover, we recommend the implementation of strategies to prepare for climate change, especially the establishment of adequate environmental policies. In the long term, this study may have implications for science, but also for the commercial practices of governments and businesses. In scientific and academic terms, this study will not only add to the literature on the use of international trade as a strategy for adapting to climate change but will also enable the academic community to change the paradigm on the potential environmental impacts of international trade, which has been largely negative. In terms of commercial practice, this study should encourage trading partners to trade more in green products, which are likely to promote adaptation to climate vulnerability. This should be done by reducing transaction costs (tariffs), strengthening international cooperation, increasing competitiveness and raising revenues, which would facilitate the fight against climate change. Governments should use such studies to develop policies to mitigate climate shocks, such as the development of green energy. They should also set up mechanisms and subsidy or loan programs to finance investment in sustainable technologies. However, the main limitations of our study are related to time and space. In terms of time, it should be noted that our study was carried out over a relatively short period due to the unavailability of data on certain variables, whereas climate vulnerability and international trade are phenomena with long time horizons. In terms of space, the main limitation of our study is that it was carried out in sub-Saharan Africa, whereas the issues of international trade and climate vulnerability are global. Our future research could, for example, allow us to study this issue with a larger sample and over a relatively long period.

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Data availability

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Funding

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Appendix.

Appendix 1. elements for calculating the Climate Change Vulnerability Index

sector	Food	Water	health	ecosystem	Human habitat	Infrastructure
Exposure	Projected change in cereal yields	Projected change of annual runoff	Projected change of deaths from climate change induces disasters	Projected change in biome distribution	Projected change of warm period	Projected change in hydropower generation capacity
sensitivity	Food import dependency	Freshwater withdrawal rate	Slum population	Dependency on natural capital	Urban concentration	Dependency on imported energy
Adaptive capacity	Agriculture capacity	Access to reliable drinking water	Medical staff	Protected biomes	Quality of trade and transport-related infrastructure	Electricity access

Climate change vulnerability index = arithmetic means of [exposure (mean of six indicators of exposure) + sensitivity (mean of six indicators of sensitivity) - adaptive capacity (mean of six indicators of adaptive capacity)].

Source: author from literature

Appendix 2. calculation elements of the Squalli and Wilson Index (2011)

Squalli and Wilson (2011) propose an alternative way of measuring trade openness by combining two dimensions: on the one hand TS (Trade Share) represents the share of country i in international trade and on the other hand WTS world trade (World Trade Share).

The first important dimension represents the share of trade in overall economic activity TS, measured in the interval 0 $\le \frac{{\left(M+X\right)}_i}{{PIB}_i}\le \infty$.

The second dimension highlights the relative contribution that a country makes to total world trade. If we consider a set of countries, j = 78 …, n, where i ∈ j, then the share of world trade of country i is given by the relation:

$${WTS}_i=\frac{{\left(M+X\right)}_i}{{\sum }_{j=1}^n{\left(M+X\right)}_j}$$ (1)

WTS _i represents the total trade of country i relative to the total of world trade, the greater WTS _i, the greater the weight the country has in world trade (with $0\prec$ WTS _i $\prec 1$).

Let Dr be the distance ratio, measuring the deviation of WTS _i from the average of the WTS ratios of all countries $\bar{x}=\frac{1}{n}$ and described as follows:

$$D_r=\frac{{WTS}_i}{\bar{x}}-1$$ (2)

Where D _r > 0 when WTS _i > $\bar{x}$ and D _r < 0 when WTS _i < $\bar{x}$. So composite trade share (CTS), the simple product between D _r and TS is written as:

$$\text{CTS}=\left(1+D_r\right){TS}_i$$ (3)

By replacing (2) in (3), we obtain:

$${CTS}_i=\frac{1}{x}\left({WTS}_i\ast {TS}_i\right)=\mathrm{n}\left({WTS}_i\ast {TS}_i\right)$$ (4)

We deduce that:

$${CTS}_i=\frac{n{\left(M+X\right)}_i^2}{{PIB}_i{\sum }_{j=1}^n{\left(M+X\right)}_j}=\frac{{\left(M+X\right)}_i}{\frac{1}{n}{\sum }_{j=1}^n{\left(M+X\right)}_j}\ast \frac{{\left(M+X\right)}_i}{{PIB}_i}$$ (5)

It turns out that CTS _i is the index of Squalli and Wilson (2011) representing the trade share of country i (TS) adjusted by the proportion of a country's trade level relative to average world trade. Squalli and Wilson (2011) carried out various robustness tests on this variable which led to more interesting results.

Source: author.

Appendix 3. sample of the study

Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Chad, Congo, Democratic, Rep, Cote d'Ivoire, Congo, Rep, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, Zimbabwe.

Source: author.

Appendix 4. variables definition and descriptive statistic

Label	Definition _	Sources	Obs	Mean	Std. Dev.	Min	Max
ccv	It measures the capacity or inability of a system to cope with the adverse effects of climate change, including climate shocks. It is normalized from 0 (less vulnerable) to 1 (more vulnerable)	Nostra Damus Global Adaptation Index database	818	0.5455859	0.0650225	0.390805	0.7002952
Trade	The Squali and Wilson (2011) index measures international trade and is calculated from data such as exports (Xi), imports (Mi) and GDP (Yi). It makes it possible to correct the openness rate of any country by its weight in world trade	development database indicator (2021).	819	0.0276372	0.0571439	0	0.4011621
gdp	Conflict is a georeferenced dataset collecting information on the number of conflict-related events and the number of deaths	Armed Conflict Location and Event Data (ACLED, 2021)	819	3011.787	4476.529	−3716.02	24036.38
remf	It represents the share of income earned abroad	(WDI, 2021)	819	4.289471	7.098592	0.0001832	34.7804
Educ	It measures the number of students enrolled in secondary school	(WDI, 2021)	819	57.7151	32.06703	−19.0412	150.9597
conf	Conflict is a georeferenced dataset collecting information on the number of conflict-related events and the number of deaths	Armed Conflict Location and Event Data (ACLED, 2021)	819	161.6056	583.6683		7407
coc	It represents the control of corruption	WGI (2021)	819	−0.672895	0.6908055	−1.86871	7
fl	Deforestation measures the amount of forest lost in a geographic area	Georeferenced data from the Global Forest Change dataset by Hansen (2021)	819	242461.6	288852.6	0.9332071	1545801
int	This is the subscription rate of the population to the internet per hundred people	WDI (2021)	819	9.665058	13.73707	0.0087946	70
phone	This is the population's mobile phone subscription rate for the last three months.	WDI (2021)	819	3.677992	13.46397	−0.787424	98.46405
wat	This is the availability of freshwater	WDI (2021)	819	24.13167	87.62102	0.0203604	673.375
vat	It is the variation of average temperatures and average precipitation obtained by calculating the standard deviations of monthly temperatures	Climate Change Knowledge Portal (CCKP, 2021).	819	2.385488	1.501264	0.406761	7.972619
vap	It is the variation in average temperatures and average precipitation obtained by calculating the standard deviations of monthly precipitation	Climate Change Knowledge Portal (CCKP, 2021).	819	75.45468	49.69595	10.05458	361.4276
re	Consumption of renewable energies	WDI (2021)	819	69.01781	24.64967	7.72	98.34

fi

Food insecurity is measured by the volatility of food prices, obtained by calculating annual standard deviations on monthly consumer price index data.

FAOSTAT (2021)

819

222.6967

2561.315

0.91

69435.37

Source: author construction

Appendix 5. correlation matrix

	vul	trade	GDP	remf	Educ	conf	coc	fl	int	phone	wat	VAT	VAP	re	fi
vul	1.0000
trade	−0.0026∗	1.0000
gdp	−0.2050∗	−0.0504	1.0000
remf	−0.1845∗	−0.1375∗	0.4718∗	1.0000
Educ	−0.2405∗	0.2196∗	0.0223	0.1752∗	1.0000
conf	0.0475	0.1741∗	0.2028∗	0.1846∗	0.0935∗	1.0000
coc	−0.0035∗	−0.2052∗	−0.2223∗	−0.2239∗	−0.0466	0.1978	1.0000
fl	0.0781∗	0.2020∗	−0.2249∗	−0.0567	0.2892∗	0.0847∗	0.0867∗	1.0000
int	−0.0414	−0.0440	−0.0124	0.0804∗	0.0480	0.0124∗	0.0467	0.0214	1.0000
phone	−0.0168∗	−0.0798∗	−0.0619	0.0318	0.0673	0.033	0.0212	0.0496	0.0214	1.0000
wat	−0.0407	−0.0907∗	0.0914∗	0.0584	−0.0265	0.0002	0.0111	0.0708∗	0.0334	0.3543∗	1.0000
VAT	0.0116∗	−0.1852∗	−0.1457∗	0.0529	0.0582	0.1123∗	0.0712∗	0.0422	0.0005	0.0807∗	0.1012∗	1.0000
VAP	−0.0934∗	0.0013	0.4770∗	0.0865∗	−0.0036	0.0792	0.0503	0.1170	0.0002	0.2081∗	−0.2091∗	−0.4307∗	1.0000
re	−0.2265∗	0.0237	−0.0638	−0.1342∗	−0.0594	0.2003	0.0427	0.0714	0.1982	0.1566∗	−0.2868∗	−0.2579∗	0.3064∗	1.0000
fi	0.0041∗	−0.0045	−0.0138	0.0201	−0.0114	0.0006	0.0505	0.0102	0.0213	0.0111	0.0022	0.0294	0.0123	0.0152	1.0000

Source: authors

Appendix 6. post estimation test

VIF test of multicollinearity

	VIF	1/VIF
trade	1.33	0.751386
gdp	2.07	0.484226
remf	1.50	0.665998
Educ	1.34	0.746477
conf	1.24	0.808045
coc	1.21	0.825591
fl	1.39	0.716922
int	1.07	0.930936
phone	1.22	0.821582
wat	1.33	0.753113
vat	1.37	0.729288
vap	1.93	0.519433
re	1.38	0.726855
fi	1.03	0.969849

Source: author

LM test for autoregressive conditional heteroskedasticity (ARCH).

lags(p)	chi2	df	Prob > chi2
1	0.075	1	0.7844

H ₀: no ARCH effects vs. H ₁: ARCH(p) disturbance.

Appendix 7. Hausman test specification

Dependent variable	Hausman test		Model chosen
	Chi 2 (k)	P-value
Climate change vulnerability	63.27a	0.000	Fixed effect model

^aChi2(8).

Source: authors construction

Appendix 8. structural equations for mediation testing

$$me{d}_{it}={\phi }_1+{\theta }_{1}trad{e}_{it}+{\mu }_{it}$$ (1)

$$vu{l}_{it}={\phi }_2+{\theta }_{2}trade+{\theta }_{3}med+{\pi }_{it}$$ (2)

MED represents mediation, that is to say, the set of mediating variables such as economic growth, renewable energy, ICT, water availability and food insecurity. ${\theta }_1$ represents the direct effect of international trade on mediation (equation (5)). ${\theta }_3$ Measures the direct effect of mediation on climate vulnerability (equation (5)). The indirect effect is measured by ${\theta }_1\ast {\theta }_3$, while the total effect is measured by $\left({\theta }_1\ast {\theta }_3\right)+{\theta }_2$. The results contained below validate the mediating role of each of the variables

Appendix 9. effect of international on climate change vulnerability in sub-Saharan Africa: robustness with lag 2 explanatory variables

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
International trade (SW index)t -2	−0.006∗∗∗	−0.006∗∗	−0.006∗∗	−0.0053∗	−0.003	−0.037∗∗	−0.038∗∗	−0.041∗∗	−0.049∗∗∗	−0.084∗∗∗	−0.081∗∗	−0.069∗∗	−0.075∗∗	−0.075∗∗
	(0.002)	(0.003)	(0.003)	(0.003)	(0.003)	(0.016)	(0.017)	(0.016)	(0.016)	(0.011)	(0.0125)	(0.0146)	(0.0182)	(0.0176)
Growth domestic product (GDP)t-2		−0.008	−0.007	−0.007	−0.007	−0.003	−0.003	−0.008	−0.005	−0.00240	−0.00270	−0.00063	−0.00137	−0.00094
	(0.005)	(0.005)	(0.005)	(0.005)	(0.012)	(0.012)	(0.011)	(0.011)	(0.007)	(0.00744)	(0.0075)	(0.0077)	(0.0075)	(0.005)
Remittances fundt-2			−0.006∗∗	−0.005∗	−0.004	−0.039∗∗∗	−0.038∗∗∗	−0.035∗∗	−0.030∗∗	−0.020∗	−0.0233∗	−0.036∗∗	−0.0282	−0.0336∗
			(0.003)	(0.003)	(0.003)	(0.011)	(0.013)	(0.0120)	(0.011)	(0.010)	(0.0120)	(0.0142)	(0.0200)	(0.0196)
Educationt-2				−0.020∗∗	−0.015∗	−0.095	−0.086	−0.00660	−0.001	−0.377∗∗∗	−0.366∗∗	−0.355∗∗	−0.347∗∗	−0.341∗∗
				(0.009)	(0.009)	(0.081)	(0.095)	(0.0920)	(0.097)	(0.081)	(0.085)	(0.0832)	(0.0855)	(0.0829)
Conflictt-2					0.011∗∗∗	0.014	0.018	0.00758	0.003	0.026	0.026	0.0274	0.0276	0.0313
					(0.003)	(0.033)	(0.038)	(0.0355)	(0.035)	(0.022)	(0.022)	(0.0219)	(0.0222)	(0.0216)
Control of corruptiont-2						−0.016	−0.015	−0.00814	−0.004	−0.0131	−0.012	−0.0204∗	−0.0219∗	−0.0174
						(0.013)	(0.016)	(0.0148)	(0.014)	(0.009)	(0.009)	(0.0106)	(0.0111)	(0.0111)
Internett-2							−0.002	−0.00914	−0.013	−0.0001	−0.0004	−0.00629	−0.00429	−0.0100
							(0.016)	(0.0151)	(0.015)	(0.009	(0.010)	(0.0105)	(0.0113)	(0.0114)
Phonet-2								−0.020∗∗	−0.012	−0.003	−0.005	−0.00771	−0.00772	−0.00880
								(0.0076)	(0.009)	(0.006)	(0.006)	(0.0064)	(0.006)	(0.006)
Forest losst-2									0.011	0.008	0.008∗	0.00886∗	0.00858∗	0.00706
									(0.007)	(0.004)	(0.004)	(0.004)	(0.004)	(0.004)

Watert-2										−0.402∗∗∗	−0.406∗∗	−0.323∗∗	−0.357∗∗	−0.370∗∗
										(0.057)	(0.059)	(0.078)	(0.100)	(0.097)
Variation of average temperaturet-2											0.031	−0.020	−0.011	−0.038
											(0.061)	(0.068)	(0.071)	(0.070)
variation of average precipitationt-2												0.141	0.138	0.144
												(0.090)	(0.091)	(0.088)
Renewable energyt-2													−0.0009	−0.0005
													(0.0016)	(0.0016)
Food insecurityt-2														0.0710
														(0.0426)
Constant	−0.608∗∗∗	−0.567∗∗∗	−0.564∗∗∗	−0.478∗∗∗	−0.458∗∗	−0.220	−0.285	−0.133	−0.253	−0.355∗∗	−0.388∗∗	−0.728	−0.417	−0.409
	(0.0214)	(0.0625)	(0.0623)	(0.0707)	(0.0704)	(0.236)	(0.245)	(0.228)	(0.204)	(0.160)	(0.172)	(0.534)	(0.836)	(0.838)
R-square	0.36	0.49	0.60	0.049	0.8	0.678	0.679	0.740	0.770	0.914	0.915	0.922	0.923	0.930
Fisher	0.52∗	0.63∗	1.5	2.33∗∗∗	2.67∗∗∗	2.76∗∗∗	2.58∗∗∗	3.24∗∗∗	3.47∗∗∗	10.25∗∗∗	9.67∗∗∗	8.25∗∗∗	7.83∗∗∗	9.93∗∗∗
observations	817	817	817	817	817	817	817	817	817	817	817	817	817	817
Number of countries	39	39	39	39	39	39	39	39	39	39	39	39	39	39

Notes: standard errors in parentheses. Asterisks denote significance: ∗p < 0.1. ∗∗p < 0.05. ∗∗∗p < 0.00 Source: authors construction.

Appendix 10. effect of international trade on climate change vulnerability in sub-Saharan Africa: Using Discroll-Kraay estimator

Independent Variables	Dependent Variable: climate change vulnerability
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
International trade	−0.0031∗	−0.0053∗∗∗	−0.005∗∗∗	−0.005∗∗∗	−0.005∗∗∗	−0.006∗∗	−0.009∗∗∗	−0.009∗∗	−0.0100∗∗∗	−0.009∗∗∗	−0.009∗∗∗	−0.009∗∗	−0.007∗∗	−0.007∗∗
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.0009)
Growth Domestic Product		−0.00510	−0.00504	−0.00495	−0.00491	−0.00463	−0.00415	−0.00377	−0.00358	−0.00416	−0.00389	−0.00413	−0.00358	−0.00367
		(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.0029)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Remittance fund			−0.0009	−0.0009	−0.0010	−0.0008	−0.002∗∗	−0.002∗∗	−0.0032∗∗∗	−0.006∗∗∗	−0.006∗∗	−0.006∗∗	−0.005∗∗	−0.005∗∗
			(0.0006)	(0.0006)	(0.0007)	(0.0006)	(0.0008)	(0.0008)	(0.0010)	(0.0010)	(0.0011)	(0.0011)	(0.0012)	(0.0010)
Education				−0.007	−0.007∗	−0.001	0.013∗∗∗	0.013∗∗∗	0.0152∗∗∗	0.0242∗∗∗	0.024∗∗∗	0.024∗∗∗	0.019∗∗∗	0.019∗∗∗
				(0.004)	(0.004)	(0.003)	(0.003)	(0.003)	(0.0038)	(0.0046)	(0.0047)	(0.004)	(0.004)	(0.005)
Conflict					0.000554	−0.00261	0.000513	0.00159	0.00267	−0.000808	−0.0008	−0.00084	−0.00084	−0.00028
					(0.0016)	(0.0017)	(0.0018)	(0.0021)	(0.0025)	(0.0022)	(0.00227)	(0.0024)	(0.0023)	(0.0024)
Control of corruption						−0.017∗∗∗	−0.021∗∗∗	−0.021∗∗∗	−0.019∗∗∗	−0.029∗∗∗	−0.029∗∗	−0.027∗∗	−0.023∗∗	−0.023∗∗
						(0.002)	(0.002)	(0.002)	(0.003)	(0.003)	(0.003)	(0.004)	(0.004)	(0.004)
Forest loss							0.008∗∗∗	0.009∗∗∗	0.009∗∗∗	0.009∗∗∗	0.010∗∗∗	0.010∗∗∗	0.009∗∗∗	0090∗∗∗
							(0.0009)	(0.0009)	(0.0010)	(0.00107)	(0.00113)	(0.001)	(0.0009)	(0.0010)
Internet								−0.006∗∗	−0.0056∗∗∗	−0.004∗∗∗	−0.004∗∗	−0.004∗∗	−0.004∗∗	−0.004∗∗
								(0.0013)	(0.0013)	(0.0014)	(0.0014)	(0.0014)	(0.0015)	(0.0015)
Phone									−0.00258∗∗	−0.003∗∗∗	−0.003∗∗	−0.004∗∗	−0.002∗∗	−0.002∗∗
									(0.00109)	(0.00129)	(0.00127)	(0.001)	(0.001)	(0.001)
Water										−0.008∗∗∗	−0.010∗∗	−0.008∗∗	−0.009∗∗	0.009∗∗∗
										(0.000918)	(0.00117)	(0.0015)	(0.0015)	(0.0015)
Variation of average temperature											−0.008∗∗	−0.011∗∗	−0.008∗∗	−0.008∗
											(0.004)	(0.004)	(0.004)	(0.004)

Variation of average precipitation												−0.0119∗	−0.013∗∗	−0.012∗∗
												(0.006)	(0.005)	(0.005)
Renewable energy													0.025∗∗∗	0.025∗∗∗
													(0.005)	(0.005)
Food insecurity														0.002
														(0.003)
Constant	0.53∗∗∗	0.52∗∗∗	0.525∗∗∗	0.550∗∗∗	0.548∗∗∗	0.520∗∗∗	0.534∗∗∗	0.536∗∗∗	0.525∗∗∗	0.497∗∗∗	0.506∗∗∗	0.562∗∗∗	0.469∗∗∗	0.459∗∗∗
R-square	0.017	0.045	0.046	0.051	0.051	0.072	0.200	0.234	0.251	0.295	0.300	0.309	0.343	0.344
Fisher	6.51	7.63	16.37	11.52	10.25	12.54	21.5	19.5	21	18.2	19.2	16.8	24.2
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Times fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
observations	819	819	819	819	819	819	819	819	819	819	819	819	819	819
Number of countries	39	39	39	39	39	39	39	39	39	39	39	39	39	39

Source: authors construction