Co2 is generally used as a measure of air pollution in the literature. The relationship between urbanization and air pollution generally turns into urbanization and Co2. Energy use and GDP are an indispensable variable in examining this relationship. The relationship between energy consumption and GDP is in the literature, so in accordance with the purpose of our study, studies involving urbanization and the Co2 relationship have been studied mainly. Kavi Kumar and Viswanathan (2013) have studied two groups in India: urban and rural residents. The impact of carbon-intensive energy consumption on environmental pollution has been examined for the period 2009-2010. In their work, they reached an inverse-u curve between income and pollution in the areas where the city is located. However, Li et al. (2018) in their study for China, they found a u-shaped format between urbanization and Co2 emissions. Cirilli and Veneri (2014) examined the relationship between transport and urbanization in the case of Italy. They concluded that transportation was one of the major causes of Co2 emissions in large settlements. They also stated that demographic characteristics are an important factor in Co2 emissions per capita. In another study, urbanization and Co2 emissions are examined according to geographical features. Wang Y. et al. (2019) in their study, China was divided into three groups: the central, eastern and western regions. According to the results of regression analysis, they stated that the eastern region of the country with the lowest positive effect on Co2 emissions was. Wang et al. (2019) used dynamic unrelated seemly regression (DSUR) method for the period of 1990-2014. The countries analyzed are APEC. Panel data study stated that urbanization, industrial development and economic development cause air pollution through Co2. Chikaraishi et al. (2015) they used the STIRPAT model in their work. In the study using panel data method, 140 countries were examined. 1980-2008 was selected as the observation period. They stated that urbanization would reduce air pollution if high income and service sectors share in the economy increased. Bai et al. (2019) in their study for China, they performed regression and analysis with the STIRPAT model. They found that urbanization had a positive impact on air pollution even at the point where the population rate reached 75% in cities. Zhang et al. (2018) in their studies, they analyzed population urbanization and land urbanization in two groups. They chose the period 2005-2014 as the observation range and examined China. Panel data study was applied with the STIRPAT model. According to the findings, the effect of population urbanization on Co2 was insignificant and land urbanization had significant and positive effect. Zhang et al. (2017) they conducted panel data studies involving 141 countries. They reached an inverse-u curve between Co2 and urbanization. They stated that the turning point was 73,80%.
Zhang et al. (2020) they examined China with a decomposition analysis. The authors have investigated how China can achieve its 2030 Co2 target. They have used urbanization, GDP and energy consumption variables in their work. The structural properties of energy consumption are associated with urbanization and other variables. They argued that urbanization can be achieved through the increase in GDP, technological progress and cultural development, and have argued that non-fossil fuel consumption will increase with the increase in urbanization. Not only did urbanization reduce Co2 demand, but they also needed a strong economic structure. Another study is Dong et al. (2019) they investigated the effect of urbanization and industrialization on Co2 emissions in developed countries with regression analysis. Urbanization, GDP, industrial development, GDP per capita and energy consumption have been used as variables. In the study, urbanization was grouped as low level and intermediate level. According to the results obtained, an important relationship between Co2 and urbanization could not be determined in the low-level urbanization group. However, in the mid-level urbanization group, a negative relationship between Co2 and urbanization was determined. They stated that the income level is critical in determining these threshold values. Lin et al. (2017) conducted a panel data study with 53 countries. In the study using the STIRPAT model, 1991-2013 period was investigated. Co2 emissions were analyzed by dividing them into 9 sub-factors. The Countries were analyzed in two groups as middle-upper income countries and middle-low income countries. They found that urbanization and revenue growth in the middle-low countries group would not increase Co2 emissions. However, in the middle-upper income group, urbanization had a low impact on Co2 emissions. Liu and Liu (2019) have found similar results for China. Accordingly, urbanization has a negative effect on Co2 emissions in the early stages, whereas in cases where urbanization is progressing, the negative effect turns positive and disappears over time.
Liu and Bae (2018) analyzed the relationship between Co2 emissions, urbanization and energy density with ARDL and VECM analysis in the case of China. According to the results of long-term analysis, Co2 emissions increase by 1% if urbanization increases by 1%. Co2 emissions increase by 1.1% if energy density increases by 1%. In long-term causality analysis, they determined that there was no relationship between urbanization and Co2, but that energy density had a relation between Co2 emissions and causality. Zhang and Lin (2012) stated that urbanization in China increases energy consumption and Co2 emissions. Liu (2009) in their study using ARDL and causality methods in the long-term causal relationship between urbanization, energy use, GDP for China, Krey et al. (2012) determined the same causal relationship for both China and India using an integrated assessment model. In the panel data study of 99 countries using the STIRPAT model, Poumanyvong and Kaneko (2010) stated that urbanization had a positive effect on carbon emissions and energy consumption.
York et al. (2003) using the STIRPAT model, they analyzed GDP, population, carbon emissions and energy consumption. The country groups are divided into tropical and non-tropical countries. GDP increase in the study conducted by OLS method also increases energy consumption and carbon emissions. On the other hand, according to the findings, the population has an equal effect on both direct energy consumption and CO2 emissions. They stated that Co2 emissions increased by 0.624% if urbanization increased by 1%. Variable urbanization, which mainly affects carbon emissions and energy consumption, has been identified. Shahbaz et al. (2015) analyzed the direct energy consumption and urbanization relationship for Malaysia. As a variable, GDP, trade clearance and capital stock variables were also used. They used STIRPAT and ARDL methods in their studies covering the period of 1971-2011. According to the findings, urbanization is the main reason affecting energy consumption. On the other hand, the direction of causality is from urbanization to energy consumption. Causality relationship between GDP and energy consumption has also been determined. Causality is from growth to energy consumption. Another similar study was conducted by Sharma (2011) with dynamic panel data analysis of 69 countries. In this study, factors affecting CO2 emissions were investigated for 1985-2005 period. In the study, countries were divided into three, high, medium and low income. Urbanization in all three groups negatively affects Co2 emissions. GDP and energy consumption have a positive effect on Co2 emissions. Urbanization has a negative impact on Co2 emissions as a result of aggregate analysis of all countries, while GDP and primary energy consumption alone have a positive impact on Co2 emissions. Another study examining urbanization and Co2 emissions, divided by income levels of country groups, was conducted by Martínez-Zarzoso (2008). 1975-2005 was selected as the period interval and FGLS method was selected as the analysis method. Urbanization and Co2 emissions flexibility in the high-income group were determined to be negative, while the flexibility coefficient in low income group countries was bigger than unity (2.8%). While GDP has a positive effect on carbon emissions in all groups, energy efficiency has a negative effect.