Coraline Fortier

Bertrand Kibler

Julien Fatome
email: julien.fatome@u-bourgogne.fr

Christophe Finot

Stéphane Pitois

Guy Millot

Stephane Pitois

All-fibered high

Keywords: Nonlinear optics in fibers, femtosecond, low duty cycle, pulse compression PACS: G1.10.Nz, 61.66.Bi

come

Introduction

Laser sources emitting optical pulses at repetition rates ranging from GigaHertz to TeraHertz around 1.5 μm have stimulated a growing interest these last few years since they are involved in a host of applications such as optical sampling, component testing or ultra high capacity transmission system. A well-known and powerful technique used to generate such light sources relies on the temporal compression of a sinusoidal beating propagating in optical fiber line. Indeed, for more than ten years, this method has been successfully applied to generate femto-or picosecond pulse trains using various arrangements of optical fibers [1][2][3][4][5][6][7][8][START_REF]Nonlinear Fiber Optics[END_REF][START_REF] Pitois | [END_REF][11][12][13][14][15], such as modulational instability [1], dispersion decreasing fibers [2], step-like or comb-like fiber profiles [3][4][5][6]. Based on these previous results, Inoue et. al [3] have recently developed a 40-steps pulse generation comblike setup that enables to transform an initial 160-GHz beat-signal into a train of 320-fs well-separated pulses. In this work, we present an alternative approach to design a very low duty-cycle laser source emitting femtosecond pulses at a repetition rate of 160 GHz. The main advantage of our technique, previously demonstrated at 20-and 40-GHz [7,8], is that it allows to reduce the number of fiber sections to only three stages and reach a much higher output average power.

The first part of this paper is devoted to the presentation of the pulse source through numerical simulations based on the generalized nonlinear Schrödinger equation. Physical comprehension of the compression effect is obtained by showing evolution of the temporal and spectral pulse profiles all along the fiber line. Experimental results, showing the compression of the initial sinusoidal beating into a train of 380-fs pulses, are reported in the second part of the manuscript.

Design and Numerical Simulations

First, we study the nonlinear propagation of the initial 160-GHz beat-signal generated by combining two linearly polarized continuous waves with different frequencies and equal powers. Our model is based on the generalized nonlinear Schrödinger equation, including fiber loss and higher-order effects such as third-order dispersion, self- steepening, and Raman scattering [START_REF]Nonlinear Fiber Optics[END_REF]. Our alternative approach for high-bit-rate pulse compression involves a plified arrangement of commercially-available fiber segments with anomalous and normal dispersion [7]. The evolution of the beating is decomposed into three stages as illustrated in Figure 1. For each stage of the setup, some semi-empirical rules can be used to optimize and facilitate the design of our pulse train compressor [START_REF] Pitois | [END_REF]. The first stage of our setup aims to convert an initial sinusoidal beating into a well-separated Gaussian-like pulse train with a duty-cycle of 1/5 through the multiple four-wave mixing process taking place into a standard nonzero dispersionshifted fiber (NZ-DSF) [START_REF] Pitois | [END_REF]. The second stage allows the passive reshaping of Gaussian pulses into a train of linearly chirped pulses thanks to the combined effects of self-phase modulation and normal dispersion. At the output of the second segment, the linear chirp of these pulses can therefore compensated by simply adding an adequate length of anomalous dispersion fiber, which constitutes the final stage of the setup providing transformed-limited recompressed pulses.

Figure 1 and 3 shows the 160-GHz sinusoidal input signal evolving into compressed pulse train with ∼370fs FWHM pulses in both temporal and spectral domains. At the central wavelength of the initial beat signal, i.e., λ 0 ∼1555.82 nm, the NZ-DSF is assumed to have 0.21 dB/km loss, an anomalous dispersion of 1 ps/nm/km, a nonlinear coefficient of 1.7 W -1 .km -1 , and a third-order dispersion of 0.07 ps/nm 2 /km. The optimum fiber length for achievement of maximum compression with minimum chirp and pedestal in the first compressor stage was found to be Z1=2375 m. We observe in Fig. 1 the temporal reshaping of the input sinusoidal signal accompanied by its spectral broadening through multiple four-wave mixing.

The choice of the second segment is a normal dispersion-flattened highly nonlinear fiber (NDF-HNLF) exhibiting losses of 1 dB/km, an anomalous dispersion of -0.56 ps/nm/km, a nonlinear coefficient of 9 W -1 .km -1 , and a third-order dispersion of 0.01 ps/nm 2 /km. During the propagation in this segment, the pulses experience both temporal and spectral broadenings as well as a significant reshaping, with a flattening of the central part of the pulse [11]. A special attention was devoted to prevent temporal overlapping of consecutive pulses (due to the temporal broadening) which could lead to detrimental nonlinear interactions [12]. The optimum fiber length of the second stage is then Z2 = 494 m. The high nonlinearity of this type of fiber permits to achieve a strong spectral broadening as shown in Fig. 1. Let us outline that the reduced third-order dispersion is crucial to maintain a symmetric broadening.

The final stage is based on a 17-m long SMF-28 segment, assumed to have 0.22 dB/km loss, an anomalous dispersion of 17 ps/nm/km, a nonlinear coefficient of 1.3 W -1 .km -1 , and a third-order dispersion of 0.055 ps/nm 2 /km. As shown in Fig. 1, the spectrum bandwidth remains constant, we then ensure a linear pulse recompression free from soliton effect compression [12]. All these parameters are those of the fibers used in our experiments.

The evolution of both intensity and chirp profiles for one period of the pulse train at different stages is shown in Fig. 2(a,b). We confirm the conversion of the initial sinusoidal beating into Gaussian-like pulses with 1.22-ps FWHM duration (crosses), through the multiple four-wave mixing process. Fig. 2(c) shows the corresponding optical spectrum fitted by a continuous Gaussian envelope with a FWHM bandwidth of 0.37 THz, giving a timebandwidth product of 0.45, very close to the value of 0.441 corresponding to transformed-limited Gaussian pulses. Figure 2(a) confirms that the chirp across the pulses is nearly zero. Next, the second stage provides some linearly chirped pulses with ∼4.3-ps FWHM duration (circles). In particular, we can note the emergence of some modulated pulse wings due to the beginning of the overlapping of consecutive pulses. This effect suggests that we have reached the optimum length of the second stage. Finally, the subsequent linear compression in the third stage leads to 370-fs Gaussian-like pulses (solid line) with an excellent symmetry. Although some residual energy below 5% remains in low amplitude substructure in the wings, we here obtain lower pedestals than using soliton effect compression [START_REF]Nonlinear Fiber Optics[END_REF]. The combination of segments 2 and 3 has then enabled a 3.5 compression factor of the incoming Gaussian pulses. Fig. 2(d) shows the Gaussian-like optical spectrum of the compressed pulses at the third stage output with ∼1.2-THz FWHM bandwidth. A 100-MHz phase modulation is then applied to enlarge the diode linewidth so as to work far below the Brillouin scattering threshold. The beat-signal is finally amplified at an average power of 27 dBm by means of a polarization-maintaining Erbium-doped fiber amplifier (EDFA) and injected into the first compression stage. This first stage converts the initial beat-signal into a 1.3-ps Gaussian pulse train through a multiple four-wave mixing process taking place into a 1-km long NZ-DSF having an anomalous dispersion of D=1 ps/km.nm [START_REF] Pitois | [END_REF]. The second stage, which involves 500 m of commerciallyavailable HNLF from OFS (parameters given in section 2), provides the passive reshaping of the Gaussian pulses into linearly chirped pulses, thanks to the combined effects of self-phase modulation and normal dispersion [13]. The final temporal compression is performed in the third stage : 17 m of SMF allows the compensation of the linear chirp and thus the generation of ultrashort quasi-gaussian pulses. At the output of the fiber line, the resulting pulses were analyzed by means of an SHG-based autocorrelator, an opti- cal spectrum analyzer (OSA) and a home-made frequencyresolved optical gating (FROG) setup [15]. We can notice π -jump phase between two consecutive pulses which originates from the initial beat-signal. We have represented in 4 the experimental autocorrelation results at each step of the fiber compressor line. Crosses in Fig. 4a represents the 1.3-ps Gaussian pulses generated at the first fiber output thanks to the nonlinear compression of the initial sinusoidal beating. The corresponding spectrum, visible in Fig. 4b, reveals the large number of sidebands generated by the four-wave mixing process which takes place in this NZ-DSF [14].

Experimental Results

A schematic of the experimental setup is shown in

The spectrum at the output of the HNLF (second stage) is shown in Fig. 4c. As can be seen circles in Fig. 4a, the transformation of the Gaussian pulses into linearly chirped pulses in the normally dispersive fiber is accompanied by a large broadening of the intensity spectrum.

Finally, solid line in Fig. 4a represents the autocorrelation profile at the fiber line output, after the quasilinear temporal compression which occurs in the last piece of fiber (SMF). Assuming Fourier transformed-limited pulses, one may infer a FWHM temporal width equal to 380-fs. We have also observed that the spectra after the second-and third-stage outputs are nearly the same. This result is not surprising since light propagation in the last stage is essentially dominated by dispersive (linear) effects. Moreover, we have found that this output spectrum can be accurately fitted by a Gaussian enveloppe (dashed line in Fig. 4c) having a full-width at half-maximum of 1.1 THz. The Time-Bandwidth product of 0.45 is fully consistent with the value 0.44 usually associated with Fourier limit Gaussian pulses.

Figure 1

 1 Figure 1 Numerical simulations results: (a) Temporal intensity evolution for two period of the pulse train, (b) Spectrum evolution in the three stages compressor.

Fig- ure 3 .Figure 2

 32 Figure 2 Numerical simulations results: Evolution of the chirp (a) and intensity (b) profiles at different stages of the setup (dashed line: compressor input, crosses: first stage output, circles: second stage output, solid line: compressor output). Optical spectra and their corresponding gaussian fit (dashed line) at both the first stage output (c) and the end of the pulse compressor (d).

Figure 3

 3 Figure 3 Experimental setup

Figure 4

 4 Figure 4 (a) : Evolution of autocorrelation function at different stages of the fiber line, crosses: first stage output, circles : second stage output and solid line :compressor output. (b) solid line : optical spectrum at the first stage output ,dashed line : Gaussian fit. (c) solid line : optical spectrum at HNLF output , dashed line : Gaussian fit.

c 2008 by ASTRO, Ltd. Published exclusively by WILEY-VCH Verlag GmbH & Co. KGaA

Acknowledgment

We would like to acknowledge financial support of the Agence National de la Recherche (Projet ILIADE).

This last result was confirmed by the FROG measurements represented in Fig. 5. The retrieved intensity profile (solid line) confirms that the 160-GHz pulses have a Gaussian shape with a FWHM of 380 fs. Given the period of 6.25 ps, this constitutes a very low duty cycle of 1/17. The retrieved phase (dashed line) is also pretty flat, confirming that pulses are nearly transform-limited. Finally, the available peak power exceeds 6 W, which is quite high compared to ref. [3], essentially because of a large reduction of the number of fiber sections spliced together. This then leads to an output average power as high as 400 mW which is beneficial in the context of optical nonlinear processing. We obtained high peak-to-pedestal ratio of more than 18dB.

Conclusion

In conclusion, we have generated and characterized a 160-GHz Gaussian pulse source having a FWHM of 380 fs corresponding to a very low duty-cycle of 1/17. The experimental setup is based on the temporal compression of an initial beat-signal into only three stages of optical fiber, validating this alternative method to comb-like dispersion decreasing techniques. We finally believe that this kind of sources could find many applications in the field of ultrafast optics such as optical sampling or ultrahigh capacity transmission.


GHG emissions and fossil energy use as consequences of efforts of improving human well-being in Africa

Keywords: Human development, CO2 emissions, fossil energies, recursive modelling, spillovers

This paper assesses the relationship between greenhouse gas emissions, fossil energy use and economic development, arguing that human development is not responsible for carbon dioxide emissions. To address the mechanism through which the latter relationship operates, a recursive system of three equations is estimated. The empirical results for a sample of 41 Sub-Saharan African countries observed over the period from 1990 to 2013 support our reasoning. Specifically, contrary to causality analyses which imply economic growth causing pollution, our recursive analysis indicates that greenhouse gas emissions are direct consequences of fossil energy use. Thus, economic growth is not to blame for carbon dioxide emissions. In terms of environmental policy for African countries, this study encourages efforts towards less polluting and renewable energies supply as well as investments in energy efficient technologies.

Introduction

Anthropogenic pressures, greenhouse gas (GHG) emissions and resulting environmental changes have motivated systematic studies on the human-nature dynamics. In environmental economics for instance, the relationship between pollutant emissions, energy use and economic growth is one of the most exhaustively discussed subjects. Thereby, economic growth is often identified among the drivers of environmental degradation. This has been the case in the recent studies by [START_REF] Bedir | CO2 emissions and human development in OECD countries: Granger causality analysis with a panel data approach[END_REF], [START_REF] Mardani | Carbon dioxide (co2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017[END_REF] and [START_REF] Rafindadi | Globalization, energy use, and environmental degradation in South Africa: Startling empirical evidence from the Maki-cointegration test[END_REF] on carbon dioxide (CO 2 ), where by directly linking GHG emissions to income level, these studies state that economic growth causes CO 2 emissions. Such a perspective, largely encountered in existing literature, fails to describe the mechanism through which the GHG emissions, energy and development nexus operates. This paper proposes a reassessment of the latter relationship for a sample of Sub-Saharan African (SSA) countries.

African countries are mostly endowed in mineral and energy resources and largely rely on the latter. While recent awareness on environmental pollution and climate change motivates global efforts in reducing fossil energies use, a continuous decline in renewable energy use is noted across SSA over the last 20 years (World Bank, 2020). Moreover, energy supply in Africa predominantly results from coal, oil and gas (Africa Energy Outlook, 2019), the consequences of which in terms of GHG emissions are well documented [START_REF] Conti | International energy outlook 2016 with projections to 2040[END_REF]. Simultaneously, increasing access to electricity, life expectancy and school enrolment are observed (see Figures A.4,A.5 and A.6), motivating the question whether efforts of improving living conditions and human development finally resolve into fossil energy use in Africa. Though entire Africa is estimated to be responsible of circa 4% of the global CO 2 emissions [START_REF] Sy | Africa: Financing adaptation and mitigation in the world's most vulnerable region[END_REF], population growth, macroeconomic performances, increasing access to electricity and fossil energy consumption presume growing GHG emissions. In this context, we analyse the human development, energy and GHG emissions nexus for African countries, relying on three arguments.

Firstly, it is to observe, if economic production largely utilises renewable energies, economic development will not be identified as a driver of GHG emissions. Therefore, this paper realistically argues that economic growth is not to blame for pollutant emissions, whereas fossil energy use is. Discussing the causes of CO 2 emissions, it is essential to decouple economic growth from fossil energy use, since such a consideration will dictate environmental policies. For instance, assuming economic growth to be responsible for pollutant emissions justifies perspectives towards a 'de-growth' as a viable environmental policy. To the contrary, holding fossil energies use (and not economic growth) as a direct driver of GHG emissions promotes policies toward energy efficiency or energy transition. These observations motivate the present analysis in reconsidering the relationship between GHG emissions, energy and development. As described below (Figure 1), CO 2 emissions are to be specified as primarily driven by fossil energy use, the latter being promoted by efforts of improving living conditions (increasing access to electricity, production activities, urbanisation, ...)

GHG emission

Fossil energy use

Energy demand

Production activities

Access to electricity

Transportation and others

Efforts of improving social well-being and living conditions Energy demand/use, mostly from fossil sources (coal, oil and gas) Secondly, quality of life being at the heart of any development process, qualitative indicators of development should further be considered in investigating the relationship between GHG emissions, energy and development, especially in developing countries. For example, in SSA countries such as Equatorial Guinea, Gabon and Botswana, relatively high income levels are observed concurrently with very low levels of education and poor living conditions. 1 This observation can be extended to other SSA countries to reveal the inappropriateness of using economic growth as indicator of human development in SSA. Qualitative indicators, including not only per capita GDP but also social dimensions such as life expectancy and education, seem more accurate proxies for living conditions (UNDP, 2019). Surprisingly, in existing literature, whether the research questions are the long-run dynamics, direction of causality [START_REF] Huang | Causal relationship between energy consumption and GDP growth revisited: A dynamic panel data approach[END_REF][START_REF] Joyeux | Energy consumption and real income: A panel cointegration multi-country study[END_REF] or the drivers of GHG emissions and existence of an environmental Kuznets curve [START_REF] Nguyen-Van | Energy consumption and income: A semiparametric panel data analysis[END_REF][START_REF] Azomahou | A structural nonparametric reappraisal of the co2 emissions-income relationship[END_REF], rare are the studies using qualitative measures of development, even so in the African context. This paper, in addition to a recursive modelling, intends to exploit Human Development Index (HDI) as indicator of development level. 2Finally, primary energies being directly harvested from natural resources, neighbouring Sub-Saharan African (SSA) countries sharing similar geographical characteristics, likely share comparable endowments in energy resources as well as correlated intensities in fossil energy use. Therefore, geographical characteristics or spillovers should be considered, when addressing primary energy use. Unfortunately, assessing the link between CO 2 emissions, energy and human development in Africa, the two existing studies [START_REF] Ouedraogo | Energy consumption and human development: Evidence from a panel cointegration and error correction model[END_REF][START_REF] Aglina | Policy framework on energy access and key development indicators: Ecowas interventions and the case of Ghana[END_REF] disregard geographical spillovers in both primary energy use and CO 2 emissions. This study also intends to consider geographical spillovers in both primary energy use and CO 2 emissions. This paper contributes to the empirical literature on GHG emissions, energy and human development in SSA in two ways. Primarily, contrary to [START_REF] Omri | Co2 emissions, energy consumption and economic growth nexus in MENA countries: Evidence from simultaneous equations models[END_REF]; [START_REF] Nguyen | Trade off between environment, energy consumption and human development: Do levels of economic development matter?[END_REF] and [START_REF] Soukiazis | The interconnections between renewable energy, economic development and environmental pollution: A simultaneous equation system approach[END_REF] who advocate for simultaneity, this study relies on theoretical arguments to claim a recursive mechanism. It shows that efforts of improving human well-being and economic development are not to blame for CO 2 emissions. Furthermore, this study provides evidence of geographical spillovers in both fossil energy use and CO 2 emissions across SSA. Our sample selection follows a simple rationale. On the one hand, very few studies can be identified addressing GHG emissions, energy and human development for the entire SSA. Studies doing so have very limited samples. On the other hand, given their colonial history (mostly former colonies), their common socio-economic characteristics (countries at early stages of development) and most crucially their geographical characteristics (contiguity), SSA appears to be the ideal sample this recursive spatial analysis can be applied to.

CO 2 emissions
The remainder of the paper is organised as follows. The next Section reviews the related literature. Sections 3 and 4 present the data and describe our econometric specification. Section 5 presents and discusses the results of our empirical analysis. In Section 6, we check our results for robustness and draw some conclusions in Section 7.

Related literature

Following the founding works by Grossman andKrueger (1991, 1995) and [START_REF] Shafik | Economic growth and environmental quality: Time-series and cross-country evidence volume 904[END_REF], a large literature has assessed the relationship between CO 2 emissions, energy and income. This literature highlights the causal link between these variables and globally suggests that economic growth and energy use cause environmental pollution [START_REF] Halicioglu | An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey[END_REF][START_REF] Gassebner | Determinants of pollution: What do we really know?[END_REF][START_REF] Liddle | The energy, economic growth, urbanization nexus across development: Evidence from heterogeneous panel estimates robust to cross-sectional dependence[END_REF]. This paper addressing the link between GHG emissions, primary energy use and human well-being, we wish to narrow down this literature review to studies using qualitative measures of human development instead of GDP per capita.3 Systematic and recent literature reviews of studies focusing on economic growth are presented by [START_REF] Ozturk | A literature survey on energy-growth nexus[END_REF], [START_REF] Tiba | Literature survey on the relationships between energy, environment and economic growth[END_REF] and [START_REF] Mardani | Carbon dioxide (co2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017[END_REF].

Besides economic growth, recent empirical studies have been questioning the role of qualitative measures of well-being (HDI, literacy rate and consumption) in energy consumption and GHG emissions. Although relatively short, this literature initiated, among others, by [START_REF] Pasternak | Global energy futures and human development: A framework for analysis[END_REF], [START_REF] Martinez | Understanding the role of energy consumption in human development through the use of saturation phenomena[END_REF] and [START_REF] Wu | Energy consumption inequality and human development[END_REF] enlightens the interactions between these phenomena. [START_REF] Pasternak | Global energy futures and human development: A framework for analysis[END_REF] and [START_REF] Martinez | Understanding the role of energy consumption in human development through the use of saturation phenomena[END_REF] analyses correlation between human development and energy use in most populated countries to conclude that large gains in human development are possible through access to energy in poor nations, as there is a significant association between electricity use and indicators of human development. [START_REF] Wu | Energy consumption inequality and human development[END_REF] note similarities in energy use and HDI inequalities across 129 countries from 1998 to 2007. Following these leading studies, [START_REF] Pîrlogea | The human development relies on energy: Panel data evidence[END_REF] and [START_REF] Niu | Electricity consumption and human development level: A comparative analysis based on panel data for 50 countries[END_REF] use regression analysis to provide evidence on the role of energy and access to electricity in human well-being. Their results indicate that the higher the GDP per capita, the greater electricity consumption and the higher human development. Similarly, [START_REF] Roy | Energy consumption and human development: Global perspective[END_REF] find results supporting a long-run relationship between energy and HDI which further suggest that energy use enhance social well-being. A takeaway appears in these studies. Similar to the income-energy nexus, a bidirectional causality (feedback hypothesis) appears between energy use and human well-being.

Given recent awareness on climate issues and increasing renewable energy use, a further aspect of the literature addressed how renewable energy use impacts social well-being. Overall, no convincing conclusion appears regarding the role of renewable energies in improving human development. On this, while [START_REF] Kazar | The renewable energy production-economic development nexus[END_REF] and [START_REF] Satrovic | The human development relies on renewable energy: Evidence from Turkey. 3rd International Energy and Engineering[END_REF] support the existence of a bidirectional causality between renewable energy and HDI, at least in the short-run, [START_REF] Wang | Renewable energy consumption, economic growth and human development index in Pakistan: Evidence form simultaneous equation model[END_REF] find no conclusive results. It can be observed that this specific area of environmental economics discussing human development and renewable energy consumption needs to be further explored. Regarding pollutant emissions and their link to energy and human development, inconclusive results are also noticeable. The existing studies on the latter topic also provide evidence indicating that energy use promotes human development, but remain inconclusive on how GHG emissions impact human wellbeing [START_REF] Nguyen | Trade off between environment, energy consumption and human development: Do levels of economic development matter?[END_REF][START_REF] Soukiazis | The interconnections between renewable energy, economic development and environmental pollution: A simultaneous equation system approach[END_REF][START_REF] Rahman | Environmental degradation: The role of electricity consumption, economic growth and globalisation[END_REF]. On the same topic, [START_REF] Bedir | CO2 emissions and human development in OECD countries: Granger causality analysis with a panel data approach[END_REF] show that GHG emissions affect HDI only in four European countries (Iceland, Norway, Portugal, and Switzerland). In conclusion, contrary to energy use, which is shown to have a bidirectional causality with human development, the impact of GHG emissions on human well-being remains an open question.

In the case of countries at early stages of development, specifically SSA, even scarcer are studies considering qualitative measures of well-being when addressing the pollutant emissions, energy use and development nexus. Consequently, only the studies by [START_REF] Ouedraogo | Energy consumption and human development: Evidence from a panel cointegration and error correction model[END_REF] and [START_REF] Aglina | Policy framework on energy access and key development indicators: Ecowas interventions and the case of Ghana[END_REF] were found. Considering human well-being indicators such as life expectancy and literacy rate, among others, [START_REF] Aglina | Policy framework on energy access and key development indicators: Ecowas interventions and the case of Ghana[END_REF] find positive impacts of energy consumption on human well-being. Similar results appear in the study by [START_REF] Ouedraogo | Energy consumption and human development: Evidence from a panel cointegration and error correction model[END_REF], who assesses the co-movement and the causality between energy use, electricity consumption and HDI in the Economic Community of West African States. The results specifically show a positive cointegration relationship between electricity consumption and HDI, implying that electricity consumption enhances human development.

Conclusively, though the renewable energy use and economic growth debate remains controversial, the literature shows that energy consumption and access to electricity drive human development. It is first to note that the specifications, even when exploiting simultaneous equations system, are less theory derived, leading to regressions models directly linking income to GHG emissions. Secondly, no specific attention has been given to fossil energy use in low-income countries, modelling the latter as a direct driver of pollutant emissions rather than production activities. Finally, questions regarding geographical spillovers in primary energy consumption and GHG emissions have been largely disregarded, notwithstanding their importance for energy policy. The recursive mechanism proposed by this paper to address the CO 2 emissionsenergy-human development nexus aims to fill these gaps.

Model specification

Theoretical background

Theoretical models on energy and resources use show that economic activities rely not only on labour and capital but also on energy resources to indirectly cause environmental degradation. Thereby, [START_REF] Stern | Energy and economic growth[END_REF] and [START_REF] Arbex | Solow meets Leontief: Economic growth and energy consumption[END_REF] for instance consider a production technology using labour (L), capital (K) and energy (E): Y = AL α1 K α2 E α3 . It is to observe that the latter formulation links production activities (income level) to energy consumption. Moreover, the existing literature suggests a bidirectional causality between energy use and income [START_REF] Niu | Electricity consumption and human development level: A comparative analysis based on panel data for 50 countries[END_REF][START_REF] Roy | Energy consumption and human development: Global perspective[END_REF], implying that not only economic growth depends on energy, still, also energy consumption conversely depends on income level. Exploiting these observations but also the resources harvest function of [START_REF] Eliasson | Renewable resources in an endogenously growing economy: Balanced growth and transitional dynamics[END_REF] as well as the semi-logarithmic energy-development function by [START_REF] Steinberger | From constraint to sufficiency: The decoupling of energy and carbon from human needs, 1975-2005[END_REF], energy use per capita (in log) can be directly expressed as depending on income per capita among other socio-economic characteristics: log EU pc = a 0 + a 1 log Y pc .4 This equation constitutes the theoretical foundation for our regression model 2 linking energy use to human development.

Concerning GHG emissions, [START_REF] Conti | International energy outlook 2016 with projections to 2040[END_REF] argue that 'because anthropogenic emissions of carbon dioxide result primarily from the combustion of fossil fuels, energy consumption is at the centre of the climate change debate'. Furthermore 'fossil-fuel systems, in particular, are the dominant contributors to the emissions of these gases' [START_REF] Dones | Greenhouse gas emissions from energy systems: Comparison and overview[END_REF]. These arguments, in addition to theoretical models on the dirty energy-pollution nexus (Van der [START_REF] Van Der Ploeg | Is there really a green paradox?[END_REF][START_REF] Dato | Energy transition under irreversibility: A two-sector approach[END_REF] directly link fossil energy use to environmental pollution. Therefore, we specify CO 2 emissions (in log) as primarily depending on fossil energy use: log CO2 pc = b 0 + b 1 log EU pc . The latter formulation supports the regression model 1.

These theoretical discussions over equations 1 and 2 describe a recursive mechanism linking development level to energy use and afterwards fossil energy use to pollution, supporting our empirical strategy. Moreover, they question existing causality analyses directly linking GHG emissions to economic growth. Finally, they imply two obvious propositions: (i.) Efforts of improving living conditions (human development) strengthen primary energy use in SSA and (ii.) the higher primary energy use, the larger CO 2 emissions. In addition, given the geographical contiguity of (most) SSA countries, the existence of spatial spillovers in energy and CO 2 emissions across SSA is also postulated. These rationales will guide the recursive analysis on the relationship between GHG emissions, fossil energy consumption and human development.

Econometric model

Following with the discussions above, we define the regression models for CO 2 emissions and energy as follows:

log CO2 it = b 1 log EU it + x 1,it β + ε it + i , with ε it |x 1,it , EU it ∼ iid(0, σ 2 ε ) (1) log EU it = a 1 HDI it + x 2,it α + v it + ν i , with v it |x 2,it , EU it ∼ iid(0, σ 2 v ) (2)
where the index i refers to country and t to the year. ε it and v it stand for the error terms and i and ν i for individual effects. log CO2 it and log EU it respectively represent CO 2 emissions and primary energy use per capita, while the matrices x 1 and x 2 stand for additional determinants (control variables) that will be considered.

Regarding control variables, x 1 and x 2 , the recent literature mentions as drivers of CO 2 emissions, population dynamics, urbanization, industrial production (Martínez-Zarzoso and Maruotti, 2011), openness to trade and political institution [START_REF] Halicioglu | An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey[END_REF][START_REF] You | Democracy, financial openness, and global carbon dioxide emissions: Heterogeneity across existing emission levels[END_REF]. In addition, [START_REF] Aldy | Energy and carbon dynamics at advanced stages of development: An analysis of the US States, 1960-1999[END_REF] and [START_REF] Liddle | The energy, economic growth, urbanization nexus across development: Evidence from heterogeneous panel estimates robust to cross-sectional dependence[END_REF] mentioned GDP, while [START_REF] Soukiazis | The interconnections between renewable energy, economic development and environmental pollution: A simultaneous equation system approach[END_REF] consider education, spending on R&D and development level as driving energy use. Data on most of these variables are available and will be introduced into the regression models.

Primary energies being harvested from natural resources, we intend to address geographical spillovers in energy use, as well as in resulting CO 2 emissions. Indeed, 'everything is related to everything else, but near things are more related than distant things' [START_REF] Tobler | A computer movie simulating urban growth in the Detroit region[END_REF]. Neighbouring SSA countries sharing similar geographical characteristics, likely have comparable endowments in energy resources and could also show correlated intensities in primary energy use and GHG emissions. Let ω n×n be a common-border based connectivity matrix, the spatial lag of CO 2 emissions and energy use ( w ij EU jt ) will be introduced into the respective models.

Addressing endogeneity

The feedback hypothesis in the energy and development relationship suggests that not only energy use depends on living condition (as in Eq. 2), but also that access to electricity or energy use reversely drives well-being. [START_REF] Arbex | Solow meets Leontief: Economic growth and energy consumption[END_REF] and [START_REF] Omri | Co2 emissions, energy consumption and economic growth nexus in MENA countries: Evidence from simultaneous equations models[END_REF] provide some theoretical and empirical discussions on this issue of reverse causality (endogeneity). Additionally, equations 1 and 2 in our case may suffer from omitted variables and measurement errors also leading to endogeneity, which will bias the estimated parameters if not properly addressed.

Addressing endogeneity, we rely on instrumental variables technique, using as instrument for HDI the one year lag of income per capita (GDP pc,t-1 ) and of life expectancy (LE t-1 ). These variables are highly correlated with HDI and are unlikely to directly drive energy consumption observed in period t. Therefore, they seem good instruments for human development. The regression model becomes the following, where the same included instruments are considered: 4)

log CO2 it = ρ c n j=1 w ij CO2 jt + b 1 log EU it + x it β + ε it + i , ε it |x it ∼ iid(0, σ 2 ε ) (3) log EU it = ρ e n j=1 w ij EU jt + a 1 HDI it + x it α + v it + ν i , v it |x it ∼ iid(0, σ 2 v ) (
HDI it = ϕ 1 GDP pc,i,t-1 +ϕ 2 LE i,t-1 +x it δ+u it +µ i , u it |x it , GDP, LE ∼ iid(0, σ 2 µ ) (5)
where ρ c and ρ e respectively traduce the geographical spillovers in CO 2 emissions and primary energy use, while

n j=1 w ij CO2 jt and n j=1
w ij EU jt represent for a country i the average CO 2 emissions and primary energy use in the neighbouring countries.

Data and descriptive statistics

Investigating the relationship between CO 2 emissions, fossil energy use and human development, common determinants of GHG emissions and energy use identified in existing studies need to be considered. Thus, we collected data, among others, on primary energy use, CO 2 emissions, HDI, GDP per capita, population dynamics, access to electricity and trade. The final dataset is constituted by 41 SSA countries and is reduced to the period between 1990 and 2013 due to missing values.

-Carbon dioxide emissions are used as indicator of GHG emissions in SSA. By definition, it includes carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring WDI ( 2019).

-Fossil Energy use: We consider as proxy for fossil energy use data on total primary energy consumption. Primary energy use refers to the direct use, or supply to users without transformation of energies directly harvested from natural resources and which are dominated by fossil energies (coal, oil and gas) (United Nations, 1997).

-Human development: Living conditions or human well-being at country-level is represented by the HDI, which is 'a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living' (United Nations Development Programme, UNDP). Contrary to income per capita, the UNDP argues that HDI should be the ultimate criteria for assessing the development of a country. The HDI takes values ranging from 0 to 1 and is a more qualitative measure of living standard. -Control variables: Our dataset also includes data on industry and agriculture in share of GDP, population growth, access to electricity, openness to trade (imports and exports), net inflows foreign direct investments (FDI), as well as indicators of institutional characteristics, social unrest and weather differentials, among others.

Table 1 reports descriptive statistics of the variables involved in our study. By comparing the minimum values to the maximum for HDI, primary energy use and CO 2 emissions, differences are observable, suggesting a relatively heterogeneous sample. Since these descriptive statistics do not provide any understanding of the geographical distribution of energy use and pollutant emissions in SSA allowing between countries comparisons, Figures 2 and3 are proposed. Observing both maps, in addition to differences between countries, some similarities in the geographical distribution of energy use and CO 2 emissions are quite striking. ,11 Congo,Dem. Rep.,12 Equatorial Guinea,13 Eritrea,14 Ethiopia,15 Gabon,16 Gambia,The,17 Ghana,18 Guinea,19 Guinea Bissau,20 Kenya,21 Lesotho,22 Liberia,23 Madagascar,24 Malawi,25 Mali,26 Mauritania,27 Mozambique,28 Namibia,29 Niger,30 Nigeria,31 Rwanda,312 Senegal,33 Sierra Leone,34 South Africa,35 Swaziland,36 Tanzania,37 Togo,38 Uganda, 39 Zambia, 40 Zimbabwe, 41 Sudan.

Initially, compared to inland countries, coastal located SSA countries mostly and concurrently show higher HDI, energy use and pollutant emissions (see Figure 2, 3 andA.1 in Supplementary Materials for HDI). Analysing the CO 2 -energy-HDI nexus in SSA, geographical characteristics such as regional spillovers should be addressed. Moreover, countries showing high level of primary energy use also appear to be characterized by high levels of CO 2 emissions. However, an appropriate regression analysis will be more informative in answering the question whether or not efforts of improving living conditions (HDI) drive primary energy use and indirectly CO 2 emissions.5

Estimation results

5.1. Are there geographical spillovers in primary energy use and CO 2 emissions?

Evidences of geographical spillovers in primary energy use and in CO 2 emissions across SSA are a prerequisite for estimating the parameters of equations 3 and 4. Therefore, we primarily test for the presence of spatial spillovers in each yearly wave of our panel dataset. Our argument being that countries sharing similar geographical characteristics (contiguous countries), likely have comparable endowments in energy resources and also correlated intensities in primary energy use and CO 2 emissions, we exploit the connectivity matrix ω n×n (see The test results (Table 2) in 71% of the case show statistically significant spatial spillovers in primary energy use among SSA countries. This indicates that modelling primary energy use, spatial autocorrelation should be considered as done in equation 4. CO 2 emissions being direct consequences of fossil energy use, spatial autocorrelations are also expected in data on CO 2 emissions. The test results in 100% of the case support the latter perspective by also indicating statistically significant spatial effects. Finally, we perform the Durbin-Wu-Hausman test, the results of which largely support a fixed-effects specification. (see Table A.1 in Supplementary Materials).7

On living conditions or human development in SSA

Our specification introduces the model for human development (Eq. 5) as a first-stage regression, addressing endogeneity issues. Nevertheless, its outcomes remain crucial for the pollution-energy-development analysis intended in the paper. Table 3 reports the results of estimating fixed-effects models for HDI, exploiting the one year lag of GDP per capita (GDP pc,t-1 ) and life expectancy (LE t-1 ) as excluded instrumental variables.8 Subsequently, the predicted values of HDI will be used in estimating the parameters of the model for primary energy use in SSA (Eq. 4). The following discussions are essentially based on the model specification FE IV-ii, as the latter shows the highest predictive power (Adj. R-Squared). Observing the results, both our instrumental variables are pertinent predictors of human development across SSA. As GDP per capita and life expectancy are involved in computing the HDI, such results are predictable and principally support the statistical relevance of our instrumental variables.

Regarding control variables, mean years of education and access to electricity significantly enhance human development in Sub-Saharan Africa, confirming conclusions by [START_REF] Ouedraogo | Energy consumption and human development: Evidence from a panel cointegration and error correction model[END_REF] and [START_REF] Aglina | Policy framework on energy access and key development indicators: Ecowas interventions and the case of Ghana[END_REF]. Contrary to access to electricity and education, FDI have a negative effect on human development in SSA. FDI in developing countries being very often attracted by resource endowments, oils deposits and even corruption [START_REF] Melo | Oil, foreign direct investment and corruption[END_REF], their primary goal seems not to be improving living conditions in SSA.

'The colonial origins of comparative development' literature [START_REF] Acemoglu | The colonial origins of comparative development: An empirical investigation[END_REF][START_REF] Acemoglu | The colonial origins of comparative development: An empirical investigation: Reply[END_REF] sheds light on how colonisation shapes current institutions and human development in former colonies. Following the latter, we address historical characteristics of SSA by controlling for the origin of the colonial power as proposed by [START_REF] Lawson | Institutions and geography: A "two sides of the same coin" story of primary energy use in Sub-Saharan Africa[END_REF]. The estimated parameters show that compared to not-colonized SSA countries, being a former colony undermines social well-being, independently of the colonial power's origin. Since colonisation has derailed countries from their natural path, former colonies of SSA hold institutions which derive from colonial and extractive institutions and consequently produce relatively poor performances in terms of education, income and standard of living. Thereby, it is to observe that the highest adverse impact for human development appears in former French colonies. Such a result indicates that the evidence regarding the colonial origin of comparative development can also be extended to human well-being. In contrast to the origin of the colonial powers categorical variable, improvement of living conditions (increase in HDI) in SSA can be expected to positively drive fossil energy use in SSA. The HDI-energy nexus is discussed in the next Section.

On fossil energy use and human development in SSA

Our regression model (Eq. 4) assumes that fossil energy use is promoted by efforts of improving living conditions and, hence, relates primary energy use to the index of human development. Exploiting predicted values of HDI from the first-stage regression (Eq. 5), we perform a second-stage regression for primary energy use per capita accounting for geographical spillovers. This delivers pertinent results regarding the causal link between living conditions and energy use (Table 4). The following discussions are based on model FE IV-ii.

Firstly, the estimated parameter for n j=1 w ij EU jt supports the existence of positive geographical spillovers in primary energy use, as suggested by the Moran I-tests for spatial effects (Table 2). Based on the latter, one can argue that neighbouring SSA countries, sharing similar geographical characteristics, likely have comparable endowments in energy resources and actually show co-related intensities in primary energy use. In addition to geographical spillovers, HDI positively drives energy use implying that efforts of improving living conditions increase energy demand and consequently fossil energy use in SSA. A similar positive relationship between HDI and energy consumption has been explored in recent studies, among others, by [START_REF] Niu | Electricity consumption and human development level: A comparative analysis based on panel data for 50 countries[END_REF] and [START_REF] Arto | The energy requirements of a developed world[END_REF]. In contrast to [START_REF] Pîrlogea | The human development relies on energy: Panel data evidence[END_REF], [START_REF] Niu | Electricity consumption and human development level: A comparative analysis based on panel data for 50 countries[END_REF] on the reverse hypothesis, [START_REF] Arto | The energy requirements of a developed world[END_REF] argues that efforts of improving human well-being drive energy use in SSA. Finally, since African countries appear among the most dynamic economies, a collective increase in primary energy use is to expect across SSA. Socio-economic variables reflecting the structure of economic production, openness to trade, demographic change, weather differentials and social unrest have been introduced into the regression model. Since electricity production in SSA mostly exploits oil, gas and coal sources (approximately 70% of the total, WDI ( 2019)), electricity supply and access to electricity are potential drivers of primary energy use in SSA. As expected, our empirical results show that access to electricity significantly promotes energy use, similar to industrial production and exports in GDP. These predictable outcomes suggest that increase in population share with access to electricity and economic production in the industry sector intensify energy demand which resolve into fossil energy consumption. Contrary to exports, the GDP share of imports negatively affects energy use and indicates that while SSA countries utilise fossil energies to produce and export goods (mostly raw materials and primary goods), imports of goods and services evidently do not participate in increasing energy demand.

Conclusively, the estimated parameter for FDI shows no significant effect on energy use, disproving the pollution haven hypothesis regarding fossil energy use in SSA.9 Interestingly, the rents of natural resources show a negative and statistically significant link to primary energy use, i.e. the more SSA economies rely on resource rents, the less fossil energy they utilise. Analysing such an effect, we first observe that the share of resource rents in GDP (resource harvest) does not significantly enhance human well-being in accordance with the resource curse hypothesis.10 Whether or not the negative link between resource rents and energy use characterises the resource curse paradox is left to future research.

On fossil energy use and CO 2 emissions in SSA

This paper argues that economic growth is not to blame for GHG emissions and identifies CO 2 emissions as direct consequences of fossil energy consumption. Thus, our regression model 3 relates CO 2 emissions to energy use (values predicted from Eq. 4), accounting for geographical spillovers, access to electricity, openness to trade and further socio-economic variables.

The estimated parameter for n j=1 w ij CO2 jt is positive and statistically significant.

This confirms the existence of spatial effects in CO 2 emissions and implies that SSA countries do not only show co-related intensities of fossil energy consumption, which also reflects into increasing carbon dioxide emissions (see Table 5). Fossil energy use being at the heart of CO 2 emissions, primary energy use and population access to electricity are both expected to significantly enhance CO 2 emissions. The results clearly support such a perspective by showing a positive and significant impact of primary energy use on CO 2 emissions. Similar conclusions advocating for causal effects running from energy to GHG emissions are also observed in the recent contributions by [START_REF] Omri | Co2 emissions, energy consumption and economic growth nexus in MENA countries: Evidence from simultaneous equations models[END_REF] and [START_REF] Soukiazis | The interconnections between renewable energy, economic development and environmental pollution: A simultaneous equation system approach[END_REF], among others. Globally, based on our results, the expected collective increase in primary energy use across SSA countries is also to associate with growing CO 2 emissions and further environmental consequences.

Among control variables, very appealing results appear regarding the role of access to electricity, FDI and natural resources rents. Access to electricity driving energy demand and fossil energy use is positively linked to CO 2 emissions. While natural resource rents are not significant for human development (and negatively affect primary energy use), they are positively related to CO 2 emissions. It is to acknowledge that the latter outcome raises questions on the resource curse hypothesis, on the relationship between resource rents and energy as well as on the resource rents-CO 2 emissions nexus. In fact, as there is a negative link between resource rents and energy, one expects a similar link between resource rents and CO 2 emissions. 11 A profound investigation on the forces behind this outcome is left for future research for not being the aim of this paper. Nevertheless, since non-linearities are possible justifications of such surprising outcomes, the general patterns of each of these nexuses are assessed. A U-shaped curve appears for each of the mentioned relationships, supporting the non-linearity postulate (see Figures ??,A .2 and A.3). Hence, a feasible explanation of this unpredictable result is the utilisation of a linear specification for the control variables, also for resource rents. Finally, net inflows FDI show a negative impact on CO 2 emissions, implying that investments made by foreigners into business interests in SSA countries do not systematically increase GHG emissions and environmental degradation. The latter result also disproves the pollution haven hypothesis concerning FDI. Globally, it is to note that the recursive regression model introduced by this paper to assess the mechanism through which the relationship between human development, energy and CO 2 emissions operates delivers results supporting our hypotheses. Firstly, these results show that efforts of improving human well-being are the direct forces driving fossil energy consumption in SSA. Secondly, CO 2 emissions appear as by-products of primary energy use, implying that economic growth and efforts of improving living condition are not directly to blame for GHG emissions. Finally, our preliminary tests and regression analysis support the existence of geographical spillovers in both primary energy consumption and CO 2 emissions. In the light of these results, a collective increase of primary energy use and pollutant emissions are to expect in a near future in individual SSA countries, where population growth, increasing access to electricity and economic performances are being observed.

Robustness check and non-parametric perspectives

Robustness analysis

This paper argues that causality analyses mostly failed to address the mechanisms through which the CO 2 emissions, energy and development nexus operates, by ignoring its recursive nature, qualitative measures of well-being, as well as geographical spillovers. Moreover, by directly linking income to GHG emissions (e.g. causality analyses), existing studies hold economic growth responsible for GHG emissions, although a direct link between these phenomena remains debatable. Thus, we propose a recursive analysis of the relationship between CO 2 emissions, primary energy use and human development for a sample of SSA countries. Our results mainly support the argument that efforts of improving human development create an energy demand and the higher fossil energy use, the higher GHG emissions. To test these conclusions for robustness, we proceed exactly as primarily, however, considering a different measure of human well-being, household final consumption expenditures.

• Using an alternative measure of living conditions

Using data on household expenditures on final consumption as indicator of living conditions shows slight changes in the estimated parameters, though, not in the signs and causal links previously established. 12 Observing the results of the first stage regression or for household final consumption (Table 6), the model shows high predictive power in addition to being globally significant. The one year lag per capita GDP, agricultural production and educational level positively drive household final consumption. As previously established, colonial characteristics of SSA countries reveal that being a former colony negatively affects human development, supporting the literature on the role of history in comparative development [START_REF] Acemoglu | The colonial origins of comparative development: An empirical investigation[END_REF][START_REF] Nunn | The importance of history for economic development[END_REF].

Regarding energy use, the results support the existence of geographical spillovers in primary energy use and confirm the role played by efforts of improving living conditions (household final consumption) in enhancing fossil energy consumption. Moreover, common determinants of fossil energy use in SSA such as industrial production, exportations and access to electricity remain statistically significant. Net inflows FDI is insignificant for primary energy use, while the rents of natural resources show, as previously, a negative effect.

Finally, observing the results for CO 2 emissions, the spatial spillovers in GHG emissions remains statistically significant. As previously shown, increasing energy demand, thus fossil energy use and access to electricity (which mainly results from coal sources) enhance GHG emissions in Africa. Among control variables, the level of education and FDI show a negative link, while natural resource rents surprisingly show as previously a positive link to CO 2 emissions per capita. As discussed above (see Figures A.2 and A.3), there seems to be non-linearity in the resource rents-energy use and the resource rent-GHG emissions nexus which explains the conflicting results, when a linear specification is used. Since these specific relationships are not the focal point of this study, a profound assessment of that is left to future research. • Using 4 years averaged data

Although the cross-sectional characteristic of our panel dataset (41 countries) is larger than its time-series dimension (24), motivating standard panel data approach as proposed above, it matters to check our results relying on k-years averages. This process reduces the time span of the data and mitigates concerns that (non-) stationarity of the series involved in the analysis may be affecting the results. Using 4-years averages, our conclusions are reviewed for robustness. The results of the latter procedure globally support our discussions regarding the link between human development, primary energy use and CO 2 emissions as presented above (Table 7 ). An exhaustive reading of the results of our recursive regression suggests that efforts of human development and of poverty alleviation drive fossil energy use and indirectly CO 2 emissions in SSA. Since the empirical results indicate fossil energy use driving GHG emissions, any environmental policy aiming at reducing GHG emissions and related environmental consequences in SSA should target energy supply and demand.

In conclusion, the robustness analyses relying on an alternative indicator of human development, household final consumption expenditures, and using 4-years averaged data globally supports our primary discussions regarding the link between pollutant emissions, fossil energy use and human development. As the literature largely questions linearity in the CO 2 emissions, energy and development nexus, we propose a non-parametric analysis of the core individual relationships.

A non-parametric perspective

Assessing general patterns of the core individual relationships we discussed above, it is satisfactory to only focus on primary energy use and to provide insights into its overall behaviour to human development (HDI) and household final consumption expenditures.

For this purpose, we use the local constant or Nadayara-Watson (NW) kernel estimation method, the aim of which is to directly estimate the functions E[EU it |HDI it ] = m(HDI it ) and E[EU it |HF C it ] = n(HF C it ), where m(.) and n(.) are non-parametric functions. Using the Nadayara-Watson estimator, we rely on the Silverman's rule-of-thumb bandwidth. Doing so, we obtain results clarifying interrogations concerning linearity in the energy and human development relationship. Figures 4,5 and 6 below depict the results of the non-parametric regression.

Overall, primary energy use per capita shows an increasing relationship to human development index, implying that the higher the level of development, the larger the demand for energy and consequently fossil energy consumption in SSA. The corresponding (heteroscedasticity-robust) confidence intervals indicate that the observed upward trend is statistically significant. The latter patterns (Figure 4) support the linear specification used throughout this paper, in addition to invalidating the Environmental Kuznets Curve hypothesis for SSA countries. The alternative indicator of living conditions, household final consumption, shows comparable patterns to primary energy use (Figure 5). Globally, our parametric and non-parametric analyses fairly indicate that efforts of improving living conditions mainly drive fossil energy use.

Carbon dioxide emissions primarily resulting from fossil energy consumption, increasing patters in CO 2 emissions are to associate with the growing primary energy use observed in SSA. As expected, the non-parametric result shows an upward trend between primary energy use and GHG emissions (Figure 6). This suggests that as long as the energy demand driven by efforts of improving living conditions is compensated by fossil energies, CO 2 emissions will be increasing in SSA. Therefore, environmental policies aiming at reducing CO 2 emissions in SSA should be promoting less polluting and renewable energies consumption as well as energy efficiency. This paper assesses the relationship between carbon dioxide emissions, primary energy use and human well-being in SSA countries, arguing that economic growth does not directly and systematically promote CO 2 emissions, fossil energy consumption does. Hence, contrary to existing studies (EKC and causality analyses) linking GHG emissions to economic growth, our analysis proposes to model CO 2 emissions as directly resulting from primary energy use, the latter being promoted by efforts of improving living conditions. We address further shortcomings of existing studies such as geographical spillovers, qualitative measures of economic development (HDI) and historical considerations.

Using a recursive specification, we derive results of a first stage regression indicating that income level, education, life expectancy and access to electricity improve human development in Africa. In accordance with the comparative development literature, we control for colonial history of SSA countries and find results supporting perspectives on the colonial origins of comparative development. Specifically, our analysis suggests that being a former colony negatively impacts human development in African countries. Since current political institutions in SSA countries mostly derive from colonial and extractive institutions, they produce relatively poor performances in terms of education, life expectancy and standard of living.

Regarding the drivers of energy use, our analysis indicates that efforts of improving human well-being principally lead to primary energy use. Socio-economic characteristics of SSA countries such as population growth and access to electricity are found to promote energy demand, thus primary energy use in SSA. Based on this, we can state that fossil energy consumption notably results from energy demand (due to access to electricity) and efforts of improving living conditions. Concerning GHG emissions, the results support our argument in identifying CO 2 emissions as a direct consequence of fossil energy use in SSA, rather than economic development. As neighbouring SSA countries show similar geographical characteristics and correlated intensities in primary energy use, it is not surprising to observe positive and significant geographical spillovers in GHG emissions across SSA. A comprehensive reading of the outcomes of our recursive analysis consists in concluding that CO 2 emissions in SSA are direct consequences of fossil energy use, the latter being promoted by efforts of improving living conditions. Such a conclusion also applies to post-industrial societies, as long as energy demand resulting from efforts of improving well-being is compensated by fossil energies.

The present paper questions studies analysing causality between economic growth and GHG emissions, holding economic growth responsible for environmental pollution. It rather argues for a recursive mechanism between GHG emissions, energy and human development. Hence, in terms of environmental strategy for Africa, instead of a de-growth, this study suggests reducing fossil energy use. This implies for SSA further effort towards energy efficiency and more largely a transition from traditional and fossil to less polluting and renewable energies. Indeed, improving human well-being is a priority in SSA. Nonetheless, environmental degradation and climate change threaten the future of African societies if not dealt with. Therefore, SSA countries are encouraged to tackle the issue of increasing GHG emissions at its source by fostering private and public initiatives and investments in renewable energies and energy saving technologies. Notes: Summary Table of studies on the relationship between human development indicators, energy and GHG emissions. We purposely reduced this to studies utilizing qualitative indicators of development. Recent systematic review of studies using GDP per capita are proposed by [START_REF] Ozturk | A literature survey on energy-growth nexus[END_REF], [START_REF] Tiba | Literature survey on the relationships between energy, environment and economic growth[END_REF] and [START_REF] Mardani | Carbon dioxide (co2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017[END_REF].

Figure 1 .

 1 Figure 1. Synopsis of the GHG emissions, energy and human development nexus

Figure 2 .

 2 Figure 2. Mean primary energy use in SSA, 1990-2013 List of countries: 1 Angola, 2 Benin, 3 Botswana, 4 Burkina Faso, 5 Burundi, 6 Cameroon, 7 Central African Republic, 8 Chad, 9 Congo,Rep., 10 Cote d'Ivoire, 11 Congo, Dem. Rep., 12 Equatorial Guinea, 13 Eritrea,14 Ethiopia, 15 Gabon, 16 Gambia, The, 17 Ghana, 18 Guinea, 19 Guinea Bissau, 20 Kenya, 21 Lesotho, 22 Liberia, 23 Madagascar, 24 Malawi, 25 Mali, 26 Mauritania, 27 Mozambique, 28 Namibia, 29 Niger, 30 Nigeria, 31 Rwanda, 312 Senegal, 33 Sierra Leone, 34 South Africa, 35 Swaziland, 36 Tanzania, 37 Togo, 38 Uganda, 39 Zambia, 40 Zimbabwe, 41 Sudan.

Figure 3 .

 3 Figure 3. Mean CO 2 emissions inSSA, 1990SSA, -2013 List of countries: 1 Angola, 2Benin, 3 Botswana, 4 Burkina Faso, 5 Burundi, 6 Cameroon, 7 Central African Republic, 8 Chad, 9 Congo,Rep., 10 Cote d'Ivoire, 11 Congo, Dem. Rep., 12 Equatorial Guinea, 13 Eritrea, 14 Ethiopia, 15 Gabon, 16 Gambia, The, 17 Ghana, 18 Guinea, 19 Guinea Bissau, 20 Kenya, 21 Lesotho, 22 Liberia, 23 Madagascar, 24 Malawi, 25 Mali, 26 Mauritania, 27 Mozambique, 28 Namibia, 29 Niger, 30 Nigeria, 31 Rwanda, 312 Senegal, 33 Sierra Leone, 34 South Africa, 35 Swaziland, 36 Tanzania, 37 Togo, 38 Uganda, 39 Zambia, 40 Zimbabwe, 41 Sudan.

Figure 5 .

 5 Figure 5. Primary energy use and household final consumption in SSA, 1990-2013

Fixed

  + Corr betw. access to En. and human dev.[START_REF] Martinez | Understanding the role of energy consumption in human development through the use of saturation phenomena[END_REF] 120 countries, 2006 HDI/Energy use p.c Correlation analysis + Corr betw. access to En. and human dev. No inference Wu et al. (2010) 129 countries, 1998-2007 Inequality in energy use/HDI Correlation and Reg. energy use → Human development No in-depth inference Decreasing inequality in en. use Ignore reverse causality Pîrlogea (2012) 6 EU countries, 1997-2008 HDI/En. use (renew. and fossil) FE model/GLS Energy use has mixed effects on HDI HDI/En. intensity, CO2 Renew. en. use → Human development Ignore reverse causality Niu et al. (2013) 50 countries, 1990-2009 Life exp. at birth, urban. rate/Electricity Panel Co-integration Bidirectional causality GDP p.c., Cons. expend./Electricity use Causality analysis HDI → Electricity cons. → HDI Roy et al. (2015) HDI/Energy use p.Various: Develop. → Renew. En. in long-run Ignore reverse causality

Table 1 .

 1 Descriptive statistics

		Units	N	Mean	St. Dev.	Min	Max	Source
	log.CO 2 emissions	M. tons per cap.	984	5.469	1.333	2.373	9.197	WDI
	log.Primary Energy use	Kj. per capita	984	15.306	1.182	12.616	18.622	EIA
	log.GDP per capita	$, PPP. 2011	984	7.597	0.899	5.508	10.833	WDI
	HDI	index	984	0.427	0.099	0.200	0.690	UNDP
	log.Consumption expenditures	$, PPP. 2011	696	6.504	0.815	5.151	8.443	WDI
	Agricultural activities	in % of GDP	984	28.659	16.787	0.892	78.655	WDI
	Industrial activities	in % of GDP	984	27.528	15.903	3.329	84.283	WDI
	Natural resources rents	in % of GDP	984	15.311	14.562	0.374	89.002	WDI
	FDI, net inflows	in % of GDP	984	4.226	10.651	-82.892	161.824	WDI
	Imports	in % of GDP	984	43.803	36.326	7.066	424.817	WDI
	Exports	in % of GDP	984	30.468	19.788	3.335	124.393	WDI
	Population growth	in %	984	2.619	1.037	-6.343	7.989	WDI
	Urban population growth	in %	984	4.066	1.729	-7.103	17.598	WDI
	Temperatures	• C	943	24.646	3.280	12.628	29.541	CCKP
	Total Rainfalls	in mm	943	1,044.569	596.912	70.418	3,282.239	CCKP
	Political stability	index	984	-0.680	0.897	-2.989	1.183	WDI
	Unrest (Riots and protests)	Counts	656	15.848	50.408	0.000	993.000	ACLED
	Poverty gap at 1.9$a day	%, PPP. 2011	622	19.944	12.030	1.800	63.600	WDI
	Access to electricity	in %	982	51.450	25.489	0.010	100.000	WDI
	Mean years of Education	years	946	3.913	1.921	0.700	10.100	UNDP
	Life expectancy	years	984	53.146	5.946	27.610	65.914	UNDP

Notes: n = 41 SSA countries observed over 1990-2013, 24 periods. N varies because of missing values. ACLED stands for Armed Conflict Location & Event Data and CCKP for "Climate Change Knowledge Portal".

Table 2 .

 2 Table A.3 in Supplementary Materials). 6 Test for spatial dependence in primary energy use and CO 2 emissions

		Primary energy use	CO 2 emissions
	Yearly waves of the panel dataset Moran I p-value Moran I p-value
	Wave 1990	.133	.076	.223	.012
	Wave 1991	.116	.099	.409	.000
	Wave 1992	.093	.141	.426	.000
	Wave 1993	.089	.149	.387	.000
	Wave 1994	.083	.163	.378	.000
	Wave 1995	.088	.151	.415	.000
	Wave 1996	.067	.202	.382	.000
	Wave 1997	.072	.188	.465	.000
	Wave 1998	.100	.128	.468	.000
	Wave 1999	.130	.080	.485	.000
	Wave 2000	.168	.039	.474	.000
	Wave 2001	.187	.027	.484	.000
	Wave 2002	.242	.007	.466	.000
	Wave 2003	.241	.008	.492	.000
	Wave 2004	.224	.011	.464	.000
	Wave 2005	.279	.003	.457	.000
	Wave 2006	.276	.003	.455	.000
	Wave 2007	.304	.002	.462	.000
	Wave 2008	.329	.000	.471	.000
	Wave 2009	.326	.001	.472	.000
	Wave 2010	.344	.000	.466	.000
	Wave 2011	.321	.000	.456	.000
	Wave 2012	.318	.001	.456	.000
	Wave 2013	.312	.001	.455	.000

Notes: Moran-I test under randomisation using data on primary energy use and CO 2 emissions per capita (in log). H 0 is no spatial dependence. Each yearly wave consists of 41 observations.

Table 3 .

 3 Standard FE regression model for Human Development IndexNotes: Dependent variable is the Human Development Index. In bracket are bootstrapped standard errors. Lag.lnGDP per capita and Life expectancy are the excluded instrumental variables used in the first stage. ' * * * ', ' * * ' and ' * ' respectively stand for significance level at 1, 5 and 10%.

			First stage regression
	Covariates / Models	FE I	FE II	FE III	FE IV-i	FE IV-ii
	Lag.lnGDP per capita	.026 * * * (.005)	.031 * * * (.006)	.024 * * * (.007)	.032 * * * (.006)	.032 * * * (.006)
	Life expectancy	.008 * * * (.002)	.008 * * * (.000)	.008 * * * (.000)	.007 * * * (.002)	.007 * * * (.003)
	Agriculture, GDP share	-.010 * * * (.001) -.009 * * * (.001) -.007 * * * (.001)	-.013 (.013)	-.013 (.013)
	Industry, GDP share	-.004 * * (.002) -.005 * * (.002) -.004 * * (.002)	-.001 (.001)	-.001 (.002)
	Rents of nat. resources	.005 * * * (.001)	.005 * * * (.001)	.003 * * * (.001)	.004 (.011)	.004 (.011)
	FDI, net inflows		.007(.010)	.008 (.011)	-.003 * * (.001) -.003 * * (.001)
	Imports, GDP share		.009 * (.005)	.007 (.006)	.006 (.004)	.006 (.005)
	Exports, GDP share		-.002 (.010)	-.004 (.009)	-.001 (.001)	-.001 (.001)
	Population growth			.003 * * (.001)	.007 * * * (.002)	.007 * * * (.002)
	Urban population growth			-.002 * * * (.000)	-.003 (.002)	-.003 (.002)
	Average Temperatures			.017 * * * (.002)	.006 (.017)	.006 (.018)
	Total rainfalls, yearly			.013 * * * (.004)	-.008 (.043)	-.008 (.040)
	Political stability				-.004 * (.002)	-.004 * (.002)
	Riots incidence				-.003 (.002)	-.003 (.002)
	Poverty gap, at 1.9$				-.036 * * * (.009) -.036 * * * (.008)
	Access to electricity				.027 * * * (.010)	.027 * * * (.010)
	Mean years of education				.015 * * * (.002)	.015 * * * (.002)
	Intercept					-.233 * * * (.057)
	Origin of the colonial power (categorical, ref. = not colonized):
	Belgian					-.038 * * * (.013)
	British					-.056 * * * (.011)
	French					-.059 * * * (.016)
	Portuguese					-.057 * * * (.008)
	Country dummy
	Observations	946	946	915	557	557
	F-stat (p-value)	646.501 (.000) 409.631 (.000) 333.484 (.000) 228.727 (.000)	631.30 (.000)
	Adj. R-Squared	.771	.774	.812	.873	.984

Table 4 .

 4 FE Spatial lag model Primary Energy Use

			Second stage regression
	Covariates / Models	FE I	FE II	FE III	FE IV-i	FE IV-ii
	Spatial lag of energy use, ρ e	.227 * * * (.040)	.167 * * * (.037)	.120 * * * (.039)	.179 * * * (.051)	.179 * * * (.052)
	Human Development index	1.657 * * * (.220) 1.555 * * * (.201) 1.792 * * * (.208)	.760 * * (.377)	.760 * * (.375)
	Agriculture, GDP share	.007 * * (.003)	.005 * * (.002)	.006 * * (.002)	.003 (.003)	.003 (.003)
	Industry, GDP share	.016 * * * (.002)	.011 * * * (.002)	.011 * * * (.002)	.012 * * * (.003)	.012 * * * (.003)
	Rents of nat. resources	-.014 * * * (.003) -.013 * * * (.002) -.015 * * * (.002) -.012 * * * (.003) -.012 * * * (.003)
	FDI, net inflows		-.004(.016)	-.006 (.017)	-.004 (.018)	-.004 (.018)
	Imports, GDP share		-.007 * * * (.001)	-.006 (.018)	-.002 * (.001)	-.002 * (.001)
	Exports, GDP share		.012 (.002)	.012 * * * (.002)	.007 * * * (.002)	.007 * * * (.002)
	Population growth			-.040 * * * (.020)	.018 (.030)	.018 (.029)
	Urban population growth			-.014(.015)	.004 (.022)	.004 (.021)
	Average Temperatures			.020(.024)	.045 * (.028)	.045 * (.028)
	Total rainfalls, yearly			-.006 (.007)	.003 (.007)	.003 (.006)
	Political stability				-.009 (.024)	-.009 (.023)
	Riots incidence				.003 (.002)	.003 (.002)
	Poverty gap, at 1.9$				.004 * * (.002)	.004 * * (.002)
	Access to electricity				.011 * * * (.002)	.011 * * * (.002)
	Mean years of education				-.032 (.031)	-.032 (.032)
	Intercept					1.151 * * * (.095)
	Origin of the colonial power (categorical, ref. = not colonized):
	Belgian					-1.981 * * * (.013)
	British					-.872 * * * (.011)
	French					-2.156 * * * (.016)
	Portuguese					-.770 * * * (.008)
	Country dummy
	Observations	946	946	915	557	557
	F-stat (p-value)	29.364 (.000)	54.868 (.000)	40.376 (.000)	11.416 (.000)	354.9 (.000)
	Adj. R-Squared	.140	.293	.321	.397	.972

Notes: Dependent variable is primary energy use per capita. In brackets are bootstrapped standard errors. ' * * * ', ' * * ' and ' * ' respectively stand for significance level at 1, 5 and 10%.

Table 5 .

 5 FE Spatial lag model of CO 2 Emissions

			Third stage regression
	Covariates / Models	FE I	FE II	FE III	FE IV-i	FE IV-ii
	Spatial lag of CO 2 , ρ c	.149 * * * (.045)	.146 * * * (.045)	.195 * (.053)	.213 * * * (.075)	.213 * * * (.069)
	Primary Energy use	.308 * * * (.042)	.267 * * * (.038)	.327 * * * (.001)	.187 * * * (.027)	.187 * * * (.026)
	Agriculture, GDP share	.005 * * (.002)	.005 * * (.002)	.006 * * (.002)	.006 * (.003)	.006 * * (.002)
	Industry, GDP share	.001 (.003)	-.003 (.002)	-.004 * (.002)	-.001 (.003)	-.001 (.003)
	Rents of nat. resources	.006 * * * (.002)	.005 * * (.002)	.004 * (.002)	.007 * * * (.002)	.007 * * * (.002)
	FDI, net inflows		-.004(.015)	-.007 (.017)	-.005 * * (.002)	-.005 * * * (.001)
	Imports, GDP share		-.004 * * * (.001) -.003 * * * (.001)	.001 (.001)	.001 (.001)
	Exports, GDP share		.010 * * * (.002)	.009 * * * (.002)	-.002 (.002)	-.002 (.002)
	Population growth			-.006 (.012)	.046 (.029)	.046 (.029)
	Urban population growth			-.027 * * (.013)	-.039 * (.024)	-.039 * (.024)
	Average Temperatures			.095 * * * (.025)	.024 (.027)	.024 (.027)
	Total rainfalls, yearly			.011 (.007)	.012 * * (.005)	.012 * * (.005)
	Political stability				.005 (.024)	.005 (.024)
	Riots incidence				.001 (.001)	.001 (.001)
	Poverty gap, at 1.9$				-.010 * * * (.002) -.010 * * * (.003)
	Access to electricity				.007 * * * (.001)	.007 * * * (.002)
	Mean years of education				-.107 * * (.029)	-.107 * * (.031)
	Intercept					2.014 * * * (.737)
	Origin of the colonial power (categorical, ref. = not colonized):
	Belgian					-1.762 * * * (.229)
	British					-.289 * * (.121)
	French					-.963 * * * (.267)
	Portuguese					-.012 (.178)
	Country dummy
	Observations	946	946	915	557	557
	F-stat (p-value)	32.766 (.000) 33.851 (.000)	35.412 (.000)	11.949 (.000)	473.0 (.000)
	Adj. R-Squared	.154	.191	.289	.298	.979

Notes: Dependent variable is CO 2 emissions per capita. Lag.lnGDP per capita and Life expectancy are the excluded instrumental variables used in the first stage. In bracket are bootstrapped standard errors. ' * * * ', ' * * ' and ' * ' respectively stand for significance level at 1, 5 and 10%.

Table 6 .

 6 CO 2 emission, primary energy use and household final consumption Notes: Lag.lnGDP per capita and Life expectancy are the excluded instrumental variables used in the first stage. In bracket are bootstrapped standard errors. ' * * * ', ' * * ' and ' * ' respectively stand for significance level at 1, 5 and 10%.

		Dependent variables:
		Household final consumption Primary energy use CO 2 emissions
	Lag.lnGDP per capita	.363 * * * (.064)
	Life expectancy	.003 (.003)
	Spatial lag of energy use		.154 * * (.073)
	H. final consumption exp. p.c.		.204 * * * (.066)
	Spatial lag of CO 2			.396 * * * (.073)
	Primary Energy use			.213 * * * (.034)
	Agriculture, GDP share	.007 * * * (.002)	.010 * * * (.004)	.008 * * (.004)
	Industry, GDP share	.001 (.002)	.018 * * * (.003)	-.002 (.003)
	Rents of nat. resources	-.001 (.001)	-.013 * * * (.002)	.009 * * * (.002)
	FDI, net inflows	-.001 (.001)	.001 (.002)	-.004 * * (.002)
	Imports, GDP share	-.002 * * * (.0005)	-.002 * * (.001)	.002 * * * (.001)
	Exports, GDP share	.002 (.001)	.009 * * * (.002)	-.004 * * (.002)
	Population growth	.093 * * * (.024)	.037 (.041)	.023 (.037)
	Urban population growth	-.062 * * * (.018)	-.001 (.031)	-.022 (.028)
	Average Temperatures	.030 (.024)	.096 * * (.043)	.029 (.038)
	Total rainfalls, yearly	-.00004 (.0001)	.0001 (.0001)	.0001 (.0001)
	Political stability	.034 (.019)	-.037 (.034)	-.060 * (.031)
	Riots incidence	-.0003 (.0003)	.0004 (.0005)	.0003 (.0004)
	Poverty gap, at 1.9$	-.001 (.001)	.003 (.003)	-.013 * * * (.002)
	Access to electricity	.002 * (.001)	.014 * * * (.002)	.006 * * * (.002)
	Mean years of education	.131 * * * (.025)	-.049 (.045)	-.100 * * (.041)
	Intercept	3.003 * * * (.749)	9.155 * * * (1.500)	.228 (1.051)
	Origin of the colonial power (categorical, ref. = not colonized):
	Belgian	-.702 * * * (.157)	-1.744 * * * (.204)	-1.275 * * * (.274)
	British	-1.196 * * * (.105)	-.830 * * * (.193)	-.592 * * * (.160)
	French	-1.114 * * * (.202)	-2.371 * * * (.359)	-.712 * * (.333)
	Portuguese	-.561 * * * (.112)	-.952 * * * (.204)	.423 * (.211)
	Country dummy
	Observations	416	416	416
	F-stat (p-value)	374.903 (.000)	274.091 (.000)	456.221 (.000)
	Adj. R-Squared	.979	.972	.983

Table 7 .

 7 Regression Results using 4 years average data Notes: Dependent variable for the first stage is the HDI. Lag.lnGDP per capita and Life expectancy are the excluded instrumental variables used in the first stage. Primary energy use and CO 2 emissions are the dependent variables for the second and third stages. In bracket are bootstrapped standard errors. '

		Dependent variables:
		Human Development index	Primary energy use	CO 2 emissions
	Lag.lnGDP per capita	.043 * * * (.010)
	Life expectancy	.007 * * * (.000)
	Spatial lag of energy use		.172 * (.103)
	Human Development index		1.364 * (.727)
	Spatial lag of CO 2			.306 * * (.117)
	Primary Energy use			.432 * * * (.081)
	Agriculture, GDP share	-.002 (.004)	-.001 (.006)	.012 * * (.005)
	Industry, GDP share	-.002 (.004)	.009 (.006)	-.004 (.005)
	Rents of nat. resources	.002 (.003)	-.011 * * (.004)	.006 (.004)
	FDI, net inflows	-.001 * (.000)	-.006 (.005)	-.004 (.005)
	Imports, GDP share	.001 (.001)	-.003 * (.002)	.003 * (.002)
	Exports, GDP share	-.005 * (.002)	.006 * (.004)	-.003 (.003)
	Population growth	.002 (.020)	-.056 (.037)	-.010 (.035)
	Urban population growth	-.002 (.002)	.011 (.027)	-.011 (.025)
	Average Temperatures	.017 * * (.007)	.061 (.110)	.178 * (.103)
	Total rainfalls, yearly	.003 (.002)	.001 (.003)	.001 * * (.000)
	Political stability	-.005 (.003)	-.043 (.052)	-.002 (.048)
	Riots incidence	-.002 (.004)	.001 (.001)	.0004 (.001)
	Poverty gap, at 1.9$	-.001 * * * (.000)	.005 (.004)	-.012 * * * (.003)
	Access to electricity	.004 * (.002)	.012 * * * (.003)	.005 (.003)
	Mean years of education	.011 * * * (.004)	-.137 * * (.067)	-.169 * * * (.061)
	Intercept	-.593 * * * (.167)	11.75 * * * (3.086)	-5.815 * * (2.450)
	Origin of the colonial power (categorical, ref. = not colonized):
	Belgian	-.043 (.032)	-2.178 * * * (.438)	-1.461 * * (.576)
	British	-.078 * * * (.018)	-.637 * * (.294)	-.076 (.265)
	French	-.181 * * * (.059)	-2.245 * * (.941)	-1.825 * (.928)
	Portuguese	-.086 * * * (.028)	-.675 (.448)	.016 (.459)
	Country dummy
	Observations	184	184	184
	F-stat (p-value)	84.564 * * *	4.809 * * *	7.170 * * *
	Adj. R-Squared	0.918 (.000)	0.390 (.000)	0.488 (.000)

* * * ', ' * * ' and ' * ' respectively stand for significance level at 1, 5 and 10%.

Table A .

 A 2. Summary of existing empirical studies on human development, energy and GHG emissions

	Authors, year

Regarding GDP p.c., Eq. Guinea, Gabon and Botswana are respectively 60 th , 72 nd and 77 th in a total of 185 countries,

They occupy the 144 th , 115 th and the 94 th rank in HDI (Worldometers and UNDP (2019)).

The UNDP (2019) defines HDI as a measure of average achievement in key dimensions of human development. Also, it considers that HDI 'should be the ultimate criteria for assessing the development of a country, not economic growth alone.'

This because the HDI appears to be a better indicator of human development and should be the ultimate criteria for assessing development (UNDP, 2019).

Specifically, the resource harvest function states that harvesting mineral resource requires economic resources and efforts in terms of labour. The semi-logarithmic energy-development function[START_REF] Pasternak | Global energy futures and human development: A framework for analysis[END_REF][START_REF] Steinberger | From constraint to sufficiency: The decoupling of energy and carbon from human needs, 1975-2005[END_REF] states: HDI = a + b log EU

The data used in this paper are available in csv-file to those who wish to replicate our results

The matrix ω n×n is based on contiguity principle. See TableA.3 in Supplementary Materials for details.

The Hausman test compares fixed-effects to random-effects specification for panel data models.

Though the Hausman test suggests a random-effects specification for FE III (see TableA.1), we estimate a fixed-effects model, since the latter remains consistent. The results discussions are all based on FE IV-ii

The pollution haven hypothesis states that environmental regulations will move polluting activities (of multinationals) for tradeable products to poorer countries[START_REF] Eskeland | Moving to greener pastures? Multinationals and the pollution haven hypothesis[END_REF].

 10 The resource curse hypothesis refers to the paradox that economies abundant endowment in natural resources tend to show low-level of economic development(Ross,

2015).11 We thank an anonymous reviewer for drawing our attention to this specific relationship.

The WDI (2019) defines the household final consumption expenditure (private consumption) as 'the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households'.

Note: The test evaluates the consistency of the fixed-effects (FE) estimator when compared to a randomeffects (RE) specification. Under the null-hypothesis there should be no systematic difference between the two estimators.

Supplementary Materials