Analyzing the Factors Affecting CO2 Emissions Using Nonparametric Time Varying Coefficient Panel Data Models
Selahattin Güriş, Sevcan ÇağlayanCarbon dioxide (CO2) emissions and other greenhouse gases are one of the reasons for climate change, global warming, and environmental degradation. CO2 is distributed throughout the Earth’s surface by human activity and is often harmful to the environment by causing climate change (i.e., global warming). Therefore, the literature has examined many economic and noneconomic social factors that affect CO2 emissions. However, most studies, have ignored the economic uncertainty regarding CO2 emissions effects on main factors such as economic growth and energy use. This study, discusses the relationships per capita CO2 emissions has with other variables, especially economic uncertainty as measured by the World Uncertainty Index. This article examines the relationship by utilizing the data for 14 OECD countries between 2000-2021 using a non-parametric panel data model with time-varying coefficients. By estimating trend functions and nonparametric coefficient functions, the study’s results show nonparametric coefficient functions on CO2 emissions to vary with time. In terms of signs and significance, the nonparametric coefficient function for economic uncertainty had a significant negative effect over the 2000-2021 period. In addition, economic uncertainty sustained a negative impact over time. The signs for nonparametric coefficients regarding GDP per capita, population, and renewable energy were seen to fluctuate, alternating between negative and positive values over time. Although trade was insignificant during the early 2000s, it became a significant variable between 2009-2013, while having no significant sustained effect in 2021.
Co2 Emisyonlarını Etkileyen Faktörlerin Zamanla Değişen Katsayılı Parametrik Olmayan Panel Veri Modelleri ile Analizi
Selahattin Güriş, Sevcan Çağlayanİklim değişikliğinin, küresel ısınmanın ve çevresel bozulmanın bir nedeni olarak karbondioksit (CO2) emisyonlarından oluşan sera gazları gösterilmektedir. CO2 yeryüzüne insan faaliyetleri nedeniyle yayılarak genellikle çevreye zarar verir ve iklim değişikliğine veya küresel ısınmaya yol açar. Bu sebeple CO2 emisyonlarını etkileyen ekonomik ve ekonomik olmayan birçok sosyal faktör araştırmalara konu olmuştur. Ancak çoğu araştırmada CO2 emisyonları konusunda hem ekonomik büyüme hem de enerji kullanımı gibi ana faktörleri de etkileyen ekonomik belirsizlik dikkate alınmamıştır. Bu çalışmada kişi başına düşen CO2 emisyonları ve başta ekonomik belirsizlik (dünya belirsizlik indeksi ile ölçülen) olmak üzere diğer değişkenler arasındaki ilişki ele alınmıştır. 14 OECD ülkesinin 2000-2021 yılları arasındaki verileri zamanla değişen katsayılı parametrik olmayan panel veri modeliyle incelenmiştir. Trend fonksiyonları ve parametrik olmayan katsayı fonksiyonları tahmin edilerek doğrusal olmayan sonuçlara ulaşılmıştır. Parametrik olmayan katsayı fonksiyonlarının CO2 emisyonları üzerinde etkisi zaman içerisinde değişiklik göstermektedir. Sonuçlarımıza göre ele alınan dönem boyunca ekonomik belirsizlik ile CO2 emisyonları arasında ters yönlü bir ilişki bulunmaktadır ve zaman içerisinde düşüş eğiliminde devam etmektedir. Kişi başına düşen GSYH, nüfus ve yenilenebilir enerjinin parametrik olmayan katsayıları zaman içerisinde negatif ve pozitif olarak değişmektedir. Ticaret 2000’li yıllarında başında anlamsız bir değişken olmasına rağmen 2009-2013 yılları arasında anlamlı bir değişkendir, ancak 2021 yılında etkisi tekrar anlamsızlaşmıştır.
Problems such as environmental pollution, climate change, and global warming are among the leading issues that harm the sustainable economic performance of developed and developing countries. The increase in carbon dioxide emissions (CO2) and other greenhouse gases is considered one of the reasons for these problems. Greenhouse gases contribute to global warming and change the climate on a global scale. Therefore, factors affecting CO2 emissions have been investigated, with some policies having been suggested. The literature emphasizes main factors such as growth, population, and energy use for countries or country groups. Income (GDP per capita and economic growth) in particular has significant effects on the environment. The reason for the effects from higher economic growth (i.e., per capita income) is that it increases CO2 emissions. The environmental Kuznets curve (EKC) has been used to model this effect. Namely, the EKC model shows GDP per capita to have an inverted U-shaped effect on CO2 emissions. In addition, high population density and energy consumption positively affect emissions. However, economic uncertainty, which affects main factors such as economic growth and energy use, has not been considered with regard to CO2 emissions. Economic policy uncertainty damages the decisions of countries, firms, and individuals pertaining to renewable energy and energy efficiency, thus leading to a continued dependency on greenhouse gas emissions. Therefore, this study investigates the relationship between the Economic Policy Uncertainty (EPU) and CO2 emissions.
This study aims to examine the relationship per capita CO2 emissions have with the as measured by the World Uncertainty Index (WUI), GDP per capita, population, renewable energy consumption, and trade. Because the is only calculated for developed countries, the WUI is also used to measure the index. The annual data of 14 OECD countries between 2000-2021 have been analyzed using a nonparametric panel data model with time-varying coefficients. The model includes trend and coefficient functions, and the local linear dummy variable (LLDVE) method is used to estimate how the functions change over time. First, the time-varying coefficient functions are estimated to explain the relationship between the independent variables and CO2 emissions. Second, the common trend function is estimated for the whole panel data. Third, the model is extended to include country-specific trend functions. In addition, a cross-country comparison is made by estimating the common trend function and country-specific trend functions for the entire panel data. The study’s results show the relationship CO2 emissions have with other variables in the model change over time.
According to the study’s results that were estimated using the LLDVE estimation method, WUI exhibited a significant negative effect on CO2 emissions from 2000-2021, with the negative impact being ongoing since 2010. Hence, higher WUI reduces CO2 emissions and vice versa. The effects from per capita GDP and population on CO2 emissions was found to vary with time. The results show per capita GDP to have significant signs that vary between positive and negative over time. Although per capita GDP had a positive effect from 2000-2013, the impact between 2013-2018 was negative and return topositive after 2018. Similarly, population affected CO2 emissions both positively and negatively over time and increased CO2 emissions after 2015 in particular. Renewable energy consumption has a significant negative effect, decreasing CO2 emissions between 2000-2020. The estimated results on renewable energy confirm the need to use renewable energy and to recommend policies. However, the negative effect became positive in 2020, and whether this effect remains positive over time should be investigated. Finally, no significant relationship was found between CO2 emissions and trade. In addition to country-specific trend functions, the individual trends of CO2 emissions for the US, Germany, Canada, and Japan exceed the common trend. This result indicates that countries contribute more to emissions.
