Extensive literature exists on the growth-energy-emission nexus (Al-Mulali et al. 2013; Alam et al. 2016; Andreoni and Galmarini 2016; Arouri et al. 2012). However, findings from these studies remain inconsistent due to the sensitivity of parameters, country-specific effects, and time interval of the study. On the other hand, there is a lack of extensive research on the dynamic relationship between domestic investment, economic growth, and carbon emission in Ghana. However, some is existing literature on the correlation between either domestic investment and economic growth, or the former and environmental degradation, albeit their inconclusive outcomes.
Domestic investment effect on economic growth and CO2 emissions
While many past studies have investigated the connection between domestic investment and economic growth globally, few studies have analyzed the linear effect of domestic investment and economic growth on the environment. Bouchoucha and Bakari (2021) empirically analyzed the individual impact of both domestic and foreign investment on Tunisia’s economic growth by employing the ARDL and bounds test technique. They concluded that, while domestic investment positively affects economic growth in the short run, both foreign and domestic investment has a negative effect on economic growth in the long run. An earlier study by Choe (2003) which adopted the panel VAR estimation method for 80 developing countries, also revealed a negative effect of domestic investment on economic growth. Adam (2009) employed the panel fixed effect and OLS technique to investigate the relationship between domestic investment, foreign direct investment, and economic growth in SSA between 1990 and 2003. His results from both the fixed effect and OLS estimation proved a positive effect of domestic investment on economic growth. Shabir et al. (2020) investigated the individual effect of domestic investment and foreign private investment on the economic growth of Pakistan from 1980 to 2017. The results from the ARDL estimation showed a positive relationship between domestic investment and economic growth in the long run. However, in the short run, foreign capital and domestic investment negatively affected economic growth. Ogunjinmi (2021) also made similar findings when he employed the ARDL model to investigate the role of domestic investment in Nigeria from 1981 to 2019. His study revealed a short run negative effect of domestic investment on economic growth. His results were corroborated by the findings of Saidu (2021), who also found a short run negative effect of domestic investment on economic growth in Nigeria between 1981 and 2018. Evidence from Roy (2022) corroborates the positive effect of domestic investment on economic growth. His study, which employed panel fixed effect, system GMM and OLS estimation for 40 European countries, revealed that, domestic investment has the capacity to mitigate the adverse effect of aging on economic growth.
On the other hand, Ajide and Ibrahim (2021) investigated the non-linear threshold effect of domestic investment on carbon emissions in G20 by adopting Hansen (1999) and the bootstrap method. Their study revealed a threshold of 3.086, after which domestic investment significantly increases carbon emissions. Similarly, Anwar and Elfaki (2021) investigated the effect of domestic investment, economic growth, and energy on carbon footprints in Indonesia between 1965 and 2018. Based on the FMOLS, DOLS, and canonical cointegration analysis revealed that gross fixed capital formation reduced environmental pollution whiles economic growth and energy consumption degraded the environment. An extensive study by Kobayakawa (2021) which adopted the structural decomposition analysis, tried to ascertain the effect of capital formation on carbon footprint in developing countries. He concluded that developing countries that tend to increase investment in carbon-intensive capital formation achieved faster economic growth; however, it had an adverse effect on the environment. He opined that proper environmental space for carbon footprint should be left for low-income countries as a means to faster economic growth.
Energy consumption, carbon emission, and economic growth
Globally, studies on the energy-emission-growth nexus have been extensively investigated, albeit their mixed findings. Salahuddin and Gow (2019) tested the effect of energy consumption and economic growth on environmental quality by employing the ARDL and Toda-Yamamoto causality checks. Their results indicated a negative effect of energy consumption on all three indicators of environmental quality (CO2, energy intensity, and adjusted national savings) in Qatar. They also recorded a bidirectional causality between economic growth and environmental pollution. An earlier work by Al-Mulali et al. (2013) employed the canonical cointegration regression technique to investigate the energy-emission-growth nexus for all Latin American and Caribbean countries. Their study reported that while a bidirectional long-run causality exists between energy consumption, carbon emissions, and economic growth in about 60% of the sampled countries, the remaining 40% exhibited mixed results. Nketiah et al. (2022) investigated the role of energy, economic growth and biocapacity on the ecological footprint in west Africa. The results from the FMOLS and DOLS estimation revealed a bidirectional causality between energy, economic growth, and ecological footprints. According to Huang et al. (2008), high-income group countries are likely to improve their environment through efficient energy use. Their study, which adopted the GMM-SYS approach, investigated the dynamic panel relationship between carbon emission and energy consumption for 82 countries. They revealed that while energy consumption drives economic growth in low-income countries, economic growth drives energy consumption in middle-income and high-income countries.
Alam et al. (2016) tested the prevalence of the EKC hypothesis in Brazil, China, India, and Indonesia by adopting the ARDL bounds cointegration technique. They demonstrated that while China, Indonesia, and Brazil’s income appreciation mitigate CO2 emissions, India’s income growth significantly worsens its carbon emissions. This study is contextually different from our current study in the sense that while they adopted a panel technique that fails to ascertain the country-specific dynamics, our study adopts time series data to analyze the situation for one specific country. Andreoni and Galmarini (2016) found support for the work of Alam et al. (2016) in their study, which investigated the main drivers of CO2 emissions for 33 world economies. They opined that, economic growth is the main driving force of global carbon emissions, especially in the case of China and India, which play an invaluable role in the global economic panorama. Munir and Riaz (2020) estimated the asymmetric impact of energy consumption on environmental pollution in Australia, China, and the USA by using the Non-linear ARDL model. It was revealed that fossil energy consumption worsened environmental quality by increasing CO2 emissions in Australia, China, and the USA. While Oztek and Simba (2020) in Tanzania found agreeing evidence for the negative impact of energy consumption on GHG emissions, Tamba et al. (2017); Menyah and Wolde-Rufael (2010) found strong support for the energy-led-growth assumption in Nigeria and South Africa respectively. Asumadu-Sarkodie and Owusu (2017) found similar evidence in Senegal. Their study, which employed the non-linear partial least squares technique, concluded that industrialization increased growth with its high energy demand. However, urbanization and output decreased CO2 emissions in Senegal. However, their surprising outcome regarding effect of urbanization on pollution may be attributed to the strong collinearity found in their explanatory variables.
Energy-Growth-Emission dynamics in Ghana
Studies on the energy-growth-emission nexus in Ghana have also produced inconclusive outcomes. For instance, by using ARDL and Structural time series models, Ackah (2014) found that Ghana's productivity growth reduced carbon emission in the short run, while renewable energy had the same effect in the long run. Another observation from the study was the simultaneous effect of forest depletion on GHG emissions. Kwakwa et al. (2022) adopted the ARDL bounds testing technique to investigate the existing relationship between output, industrialization, carbon emission, and capital. They revealed that industrialization and CO2 emissions curtailed agricultural output while financial development, and capital drive output. In assessing the situation of the EKC assumption in Ghana's manufacturing sector by utilizing time-series data from 1971to 2014, Abokyi et al. (2021) found results contrary to the U-shape EKC assertion in Ghana's manufacturing sector. However, the ARDL bounds test validated energy consumption-led carbon emissions in the sector. Their findings were supported by a recent study by Kwakwa et al. (2022), which opined that between 1971 and 2018, Ghana's population and industrialization drive had been significant contributors to the country's carbon emissions. In an earlier study, however, Abokyi et al. (2019) also found no validation for the EKC assumption in Ghana's industrial sector. Their results from the ARDL bounds test and Granger causality checks revealed that fossil fuel, which is pivotal in Ghana's industrial output, contributed to Ghana's GHGs emissions. Their findings were corroborated by Mensah et al. (2021), who analyzed the direct impact of Ghana's one-district-one-factory initiative on the environment by employing ARDL, FMOL, and Johansen cointegration techniques to test the EKC hypothesis. Although they found no validation for the EKC hypothesis in Ghana's industrial sector, they revealed that the ostentatious industrialization initiative would only benefit Ghana's environment and growth if cleaner energy were utilized. Their findings were supported by Minlah and Zhang (2021), who also failed to validate the EKC hypothesis in Ghana. However, they found a significant feedback effect between growth and emissions in Ghana between 1960 and 2014.
As evidenced in the reviewed literature, research on the role of energy or economic growth on carbon emission has been rigorous and extensive. However, results on the magnitude of effect or direction of causality remain an empirical issue that is mostly determined by choice of variable and sample region or country. Moreover, while extensive research has established divergent views on the effect of domestic investment on either environmental degradation or economic growth, few studies have analyzed the linear effect of domestic investment, economic growth, and energy on environmental degradation and the direction of causalities among them. The situation is even worse when it comes to Africa or Ghana. This study seeks to fulfill this gap by utilizing gross fixed capital formation as a proxy for domestic investment to investigate its effect on Ghana’s energy, carbon emission, and economic growth. There are several studies that applied ARDL bound test (Sabir et al. 2020; Salahuddin and Gow 2019; Kwakwa et al. 2021). However, to the best of our knowledge, this is the first study to utilize the ARDL bounds test to ascertain these dynamics in the context of Ghana. Based on our analysis, the discussed outcomes and policy recommendations would be a major contribution to the energy-emission-growth discourse in Ghana.