Empirical research has investigated the impact of several factors on carbon dioxide emissions, such as industry, GDP, Manufacturing, foreign direct investment net, renewable energy consumption, Energy Consumption, and Renewable energy. The interrelationships among them will be examined in the following section.
Various studies have examined the factors that influence CO2 emissions in a particular country or set of countries, employing various econometric techniques. Several of these studies comprise:
The research conducted by (Chin et al., 2018) examined the factors that influence the release of CO2 in Malaysia by employing the Environmental Kuznets Curve and ARDL methodology. The variables considered in the analysis were CO2 emissions, real GDP, China's FDI outflow, and the value of vertical intra-industry trade in the manufacturing sector. The data covered the years 1997 to 2014. The findings demonstrated that economic expansion is the primary factor responsible for the release of CO2 into the atmosphere. Consequently, it was recommended that the Malaysian government closely oversee the execution of environmentally conscious growth strategies in order to promote sustainability while safeguarding the quality of the environment.
(Pao & Tsai, 2010) examined carbon dioxide (CO2) emissions in the BRICs countries (Brazil, Russia, India, and China) by employing a panel vector error correction model over the period spanning from 1978 to 2005. Their research uncovered a substantial two-way causal relationship between emissions and foreign direct investment (FDI), as well as between emissions and energy consumption, and energy consumption and FDI. The report advised BRIC countries to allocate resources towards developing infrastructure for energy efficiency and enhancing energy conservation programs in order to mitigate emissions without compromising economic growth.
The study conducted by (Jošić & Žmuk, 2022) employed a dynamic panel and GMM model to examine the primary factors influencing worldwide CO2 emissions between 1995 and 2015 in 115 nations. The variables encompassed in the analysis are GDP per capita, trade, foreign direct investment (FDI), energy consumption, urban population, total population, population density, gross capital formation, poverty headcount ratio, industrial value added, international tourism, renewable power generation, and electricity production. The research yielded practical implications for governments and businesses to enhance their comprehension of the economic ramifications of human activities on the environment.
(Wahyudi, 2024) investigated the correlation between renewable energy and CO2 emissions in Indonesia by employing VAR and VECM estimation methods. The study revealed that CO2 emissions exert a substantial and favorable influence in both the immediate and extended periods, but non-renewable energy plays a detrimental and noteworthy function in both the long and short term.
(Tan et al., 2024) investigated the interconnectedness of carbon emissions, energy consumption, financial development, and economic growth in SAARC nations using a panel methodology. The study found that financial development, energy consumption, exports of products and services, and economic expansion had a positive impact on CO2 emissions, indicating that these factors contribute to the increase in carbon emissions.
(Elmonshid et al., 2024) conducted a study to examine how financial efficiency and renewable energy use affect the decrease of CO2 emissions in economies of the Gulf Cooperation Council (GCC). They used a panel data quantile regression approach to evaluate data from 2001 to 2021. The results highlighted the significance of cultivating effectiveness in financial institutions, encouraging environmentally friendly innovation, and extending the use of renewable energy sources in order to decrease emissions.
(Abbas et al., 2023) investigated the impact of population aging, urbanization, and institutional quality on carbon dioxide (CO2) emissions in South Asia from 1996 to 2019 using the Coefficient-Stationary Autoregressive Distributed Lag (CS-ARDL) method. The study discovered that the process of population aging and urbanization led to an increase in carbon emissions, whereas the quality of institutions had a mitigating effect on emissions throughout the region.
(Tokpah et al., 2023) performed a comprehensive analysis of the relationship between economic growth and carbon emissions in 15 developing and developed countries from 1991 to 2019. The study utilized the PMG-ARDL methodology. The findings indicate that both foreign direct investment (FDI) and quadratic gross domestic product (GDP) have a significant and negative impact on carbon emissions in developed countries. Moreover, an increase in FDI leads to a reduction in emissions in these nations.
(Raihan, 2023) conducted a systematic analysis to examine the impact of economic growth, energy consumption, and agricultural value added on carbon dioxide (CO2) emissions in Vietnam. The study utilized advanced econometric techniques such as Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) to analyze data spanning from 1984 to 2020. The results revealed that economic expansion and energy consumption contribute to environmental degradation, but agricultural value-added enhances environmental quality by reducing CO2 emissions.
(Adebayo & Beton Kalmaz, 2021) conducted a study on the factors that influence carbon dioxide (CO2) emissions in Egypt. They employed the autoregressive distributed lag (ARDL) model to analyze data from 1971 to 2014. The analysis revealed a strong and meaningful correlation between energy consumption and CO2 emissions, while there was no significant association observed between urbanization or gross capital formation and CO2 emissions. The study revealed a strong correlation between GDP growth and CO2 emissions, emphasizing the need for policymakers to develop environmental strategies that encourage sustainable urbanization and the use of clean energy.
(Adebayo et al., 2020) conducted a study on the factors that influence CO2 emissions in MINT economies from 1980 to 2018, employing panel co-integration analysis. The study revealed a positive correlation between CO2 emissions and energy consumption, with urbanization exerting a positive influence on CO2 levels and trade showing a negative association with CO2.
(Appiah et al., 2018) examined the cause-and-effect connection between agricultural productivity and CO2 emissions in certain developing countries. They employed FMOLS and DOLS techniques to evaluate data from 1971 to 2013. The empirical findings suggest that higher levels of economic growth, agricultural production index, and livestock production index are positively associated with increased CO2 emissions. Conversely, higher levels of energy consumption and population are associated with environmental improvements.
(Islam et al., 2021) conducted a study to analyze the influence of globalization, foreign direct investment (FDI), and energy consumption on carbon dioxide (CO2) emissions in Bangladesh. The study included the period from 1972 to 2016 and used the autoregressive distributed lag (ARDL) model. The study revealed that globalization, foreign direct investment (FDI), and innovation have an adverse impact on CO2 emissions. Conversely, economic growth, trade, energy consumption, and urbanization have a favorable influence on CO2 emissions, hence contributing to environmental degradation. The report suggests promoting globalization, foreign direct investment (FDI), and innovation, while also ensuring the efficient use of income growth, trade opportunities, energy consumption, urbanization, and institutional quality to enhance environmental quality in Bangladesh.
By employing an ARDL strategy, (Hatmanu et al., 2022) investigated the factors influencing CO2 emissions in Bulgaria and Romania. From 1980 to 2019, the study looked at four key variables: carbon dioxide emissions, GDP, energy consumption, and urbanization rate. In both nations, the data showed that the determining factors had a lasting impact on CO2 emissions per capita. According to the results, energy consumption per capita is the main driver in the short term, but in the long run, changes in GDP per capita and energy consumption per capita both have a substantial influence on CO2 emissions per capita in Romania. Similarly, throughout the long and medium term, Bulgaria showed a positive link between CO₂ emissions per capita and energy use per capita. There is a positive long-term consequence for Romania from the fast urbanization in both nations, which has a major influence on CO2 emissions.
The place of this research with other studies
The majority of research on climate change has mostly concentrated on industrialized countries, with limited emphasis on developing countries. This overlooks the reality that African countries will face the greatest susceptibility to the impacts of climate change as a result of their economy's sensitivity to climate and their limited capacity for adaptation and mitigation technology. Our research will focus on Rwanda, an African country experiencing significant growth, with a growth rate of 7.5% in 2022, based on a review of past studies. There is a lack of available studies on the factors that influence CO2 emissions in this country like industry (including construction), value added (constant 2015 US$), GDP (constant 2015 US$), Manufacturing, value added (% of GDP), foreign direct investment net inflows (BoP, current US$), renewable energy consumption (% of total final energy consumption), world - Energy Consumption per capita - Million Btu per Person, Renewable energy consumption (% of total final energy consumption).
In addition, we will endeavor to incorporate some explanatory factors that could elucidate the causes for the rise in CO2 emissions in Rwanda, which amounted to 0.12 kg per 2015 US$ of GDP. Given this information, officials in this country can explore strategies to decrease CO2 emissions and promote the growth of clean industries. This study utilizes the ARDL model.
The structure of this study is as follows: Section 2 examines the correlation between CO2 emissions and many other parameters. Section 3 provides an overview of the process used to acquire data and the methods employed. The empirical findings are presented in Section 4. The study conclusion is in Section 5.