Drivers Of Carbon Dioxide Emissions Research Articles

Is trade relevant to the macro drivers of carbon dioxide emissions? A study of high‐ and low‐trade openness economies

AbstractThe objective of this research is to systematically compare the impact of macro determinants on CO2 emissions, using the theoretical frameworks of trade, the Environmental Kuznets Curve, and the Pollution Haven Hypothesis. The study specifically focuses on high‐ and low‐trade‐openness economies from 1995 to 2020. Methodologies employed include stepwise regression, fully modified least squares, pooled ordinary least squares, and fixed effects models. Long‐run dynamics were assessed using Granger causality tests and Pedroni and Johansen cointegration tests. The results indicate both long‐term and short‐term relationships between CO2 emissions and the following variables: (i) gross capital formation (GCF), (ii) per capita income (PCI), (iii) population (POP), and (iv) trade openness. Trade openness has a positive and significant effect on CO2 emissions in highly trade openness economies, whereas it has a negative effect in low trade‐open economies. Highly open economies are more significantly impacted by GCF and POP on CO2 emissions compared with low‐ trade openness economies. Additionally, PCI positively and significantly influences CO2 emissions in low‐ trade openness countries, and this effect is greater than in high‐ trade openness economies. The study also identifies a bidirectional causal relationship between PCI, GCF, and CO2 emissions in both groups of economies, as well as a unidirectional relationship between trade openness, POP, and CO2 emissions in both high‐ and low‐ trade openness countries.

The relationship between CO2 emissions and macroeconomics indicators in low and high-income countries: using artificial intelligence

AbstractThis paper aims to unravel the driving forces behind carbon dioxide emissions in low- and high-income countries by applying gradient boosting and random forest algorithms. The study reveals that gradient boosting demonstrates superior accuracy over random forests in low-income countries, whereas the opposite pattern is observed in high-income countries. Additionally, the study demonstrates that, according to the gradient boosting algorithm-based feature selection, the major influencers of carbon dioxide emissions in low-income countries are the agriculture sector (49.9%), the industry sector (17%), the services sector (10.4%), population size (9.8%), gross domestic product growth (7%), and foreign direct investment inflow (5.3%). Furthermore, random forest algorithm-based feature selection reveals that, in high-income countries, the key drivers of carbon dioxide emissions are the services sector (30.8%), the agriculture sector (27.1%), the industry sector (21.5%), population size (19%), foreign direct investment inflow (1.2% - A different working methodology than low-income countries), and gross domestic product growth (0.4%). Moreover, the study corroborates that low carbon dioxide emissions in low-income countries correlate positively with industrial sector growth, foreign direct investment inflow, gross domestic product, and population size but negatively correlate with the agricultural and service sectors. In the case of high-income countries, carbon dioxide emissions positively correlate with foreign direct investment inflow, industrial and agricultural sector growth, and gross domestic product while exhibiting a negative correlation with population size and service sector growth.

Drivers of Carbon Dioxide (CO2) Emissions in Ghana: A Comparative Analysis on Consumption of Energy by the Industry, Agriculture, Residential and the Transport Sector (2000-2018)

The recent historical increases in energy-related CO2 emissions globally have put the Ghana’s transport, industry, residential and agriculture sector in the spotlight, courtesy of the significantly high pollutant emissions from these sectors. Taking a cue from this, this paper investigates into the drivers of carbon dioxide emissions in Ghana by examining the consumption of energy by the industry, agriculture, residential and the transport sector. In doing so, this study employs the Regression model as a statistical method to examine the relations between dependent variable (carbon dioxide emissions) and the four variables of interest (energy consumption by the industry, agriculture, residential and the transport sector). Decomposition Model was applied to identify the driving forces of the emission or resource utilization. From the analysis, the transport sector contributes to more release of carbon dioxide emissions (over 78%) as compared to the other sectors. This was highly driven partly by economic affluence and population. However, technological advancement played a major role in slowing the growth of Domestic Material Consumption (DMC). Among the drivers of DMC increase, the role of population was relatively less yet very significant as compared to affluence, highlighting large resource consumption due to expanding urban and social lifestyles. In general, this current study offers policy insights for the transportation, agriculture, residential and industry sectors of Ghana and other similar economies that replicate the same condition. Keywords: Carbon dioxide, energy consumption, decomposition analysis, regression analysis, Ghana. DOI: 10.7176/JETP/11-5-01 Publication date: October 31 st 2021

Drivers of CO2-Emissions in Fossil Fuel Abundant Settings: (Pooled) Mean Group and Nonparametric Panel Analyses

The present inquiry addresses the income-environment relationship in oil-producing countries and scrutinizes the further drivers of atmospheric pollution in the respective settings. The existing literature that tests the environmental Kuznets curve hypothesis within the framework of the black-box approaches provides only a bird’s-eye perspective on the long-run income-environment relationship. The aspiration behind this study is making the first step toward the disentanglement of the sources of carbon dioxide emissions, which could be employed in the pollution mitigation policies of this group of countries. Based on the combination of two strands of literature, the environmental Kuznets curve conjecture and the resource curse, the paper at hand proposes an augmented theoretical framework of this inquiry. To approach the research questions empirically, the study employs advanced panel cointegration techniques. To avoid econometric misspecification, the study also employs for the first time a nonparametric time-varying coefficient panel data estimator with fixed effects (NPFE) for the dataset of 37 oil-producing countries in the time interval spanning between 1989 and 2019. The empirical analysis identifies the level of per capita income, the magnitude of oil rents, the share of fossil fuel-based electricity generation in the energy mix, and the share of the manufacturing sector in GDP as essential drivers of carbon dioxide emissions in the oil-rich countries. Tertiarization, on the contrary, leads to a substantial reduction of emissions. Another striking result of this study is that level of political rights and civil liberties are negatively associated with per capita carbon emissions in this group of countries. Furthermore, the study decisively rejects an inverted U-shaped income-emission relationship and validates the monotonically or exponentially increasing impact of average income on carbon dioxide emissions.

The influence of trade openness on environmental pollution in EU-18 countries.

Trade openness is one of the main channels of globalization and technological transfers. In environmental economic literature, the implications of trade openness remain controversial and still could be potential drivers of carbon dioxide emissions. This study therefore explores the effect of trade openness in developed countries using EU-18 economies. We employed an econometric approach that accounts for cross-section dependence among study variables. The panel CIPS and CADF unit root show that the variables are stationary and the long-run relationship was confirmed in Westerlund cointegration tests. The mean group (MG) and augmented mean group (AMG) results show that trade openness increases CO2-emissions in EU-18. Again, energy consumption and urbanization escalate emissions. The study confirmed the environmental Kuznets curve. Finally, pollution halo and pollution haven hypothesis were confirmed at both estimation methods. The Dumetriscu-Hurlin Granger causality test results confirmed bidirectional causality between trade openness and energy consumption and between trade openness and economic growth. Again, unidirectional Granger causality is running from trade openness and CO2 emissions. Policy recommendations are further proposed.

Drivers of carbon dioxide emissions: an empirical investigation using hierarchical and non-hierarchical clustering methods

Supplementary Material for the manuscript "Drivers of carbon dioxide emissions: An empirical investigation using hierarchical and non- hierarchical clustering methods"

Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models

Identifying the drivers of carbon dioxide emissions in the manufacturing industry is vital for developing effective environmental policies. This study adopts provincial panel data from 2000 to 2013 and uses nonparametric additive regression models to analyze the drivers of CO2 emissions in the industry. The results show that the nonlinear effect of economic growth on CO2 emissions supports the Environmental Kuznets Curve (EKC) hypothesis. Energy structure has an inverted “U-shape” effect owing to massive coal consumption in the early stages and the optimization of energy structure in the later stage. The inverted “U-shaped” impact of industrialization may be due to the priority development of heavy industry in the early stages and the optimization of industrial structure in the later stages. The impact of urbanization also exhibits an inverted “U-shaped” pattern because of mass consumption of steel and cement products in the early stages and the advancement in clean energy technologies at the later stages. However, specific energy consumption has a positive “U-shaped” impact because of the difference in the speed of technological progress at different times. Thus, the differential effects of these indicators at different times should be taken into consideration when discussing reduction of CO2 emissions in China's manufacturing industry.

Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO2) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyzetheinfluencingfactors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions.Urbanization also has significant effect on CO2 emissions because of mass urban infrastructure and real estate construction. Economic growthhas more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developingappropriateenergy policy and planning for the iron and steel industry.

Analysis of Carbon Dioxide Emissions of Buildings in Different Regions of China Based on STIRPAT Model

This article focuses on CO2 emissions of buildings in China, the drivers of carbon dioxide emissions of buildings performing stage under the different climatic conditions have been studied by using the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. Five factors were selected, including per capita floor space of residential buildings, floor space of buildings, household consumption level, output value of tertiary industry and unit building area of carbon dioxide emissions. According to the climatic conditions, the buildings of four climate regions are taken into account, which are Severe Cold (SC), Cold (C), Hot Summer Cold Winter (HSCW) and Hot Summer Warm Winter (HSWW), respectively. The factors are quantified through ridge regression. Empirical results indicate that all the factors can cause an increase in CO2 emissions among the four climate regions, although they have different degree influence on CO2 emissions from buildings in different climate regions.

Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions

There has been extensive analysis of the drivers of carbon dioxide emissions from fossil fuel combustion and cement production, which constituted only 55% of global greenhouse gas (GHG) emissions in 1970 and 65% in 2010. But there has been much less analysis of the drivers of greenhouse gases in general and especially of emissions of greenhouse gases from agriculture, forestry, and other land uses, which we call non-industrial emissions in this paper, that constituted 24% of total emissions in 2010. We statistically analyse the relationship between both industrial and non-industrial greenhouse gas emissions and economic growth and other potential drivers for 129 countries over the period from 1971 to 2010. Our analysis combines the three main approaches in the literature to investigating the evolution of emissions and income. We find that economic growth is a driver of both industrial and non-industrial emissions, though growth has twice the effect on industrial emissions. Both sources of emissions decline over time though this effect is larger for non-industrial emissions. There is also convergence in emissions intensity for both types of emissions but given these other effects there is no evidence for an environmental Kuznets curve.

Averting catastrophic climate change: confronting wealth

In the context of potential catastrophic climate change, it is vital that fundamental drivers of carbon dioxide emissions are pinpointed and addressed. One of the key drivers of these emissions is shown to be increased economic wealth. This paper highlights that links between wealth and carbon dioxide must be addressed, and that although highly debated, the links are not well considered, especially at the detailed level of what drives consumption preferences of the global wealthy. In the context of increasing wealth concentration and poverty gaps, this subject is central to issues of social justice as well as environmental integrity.

Measuring National Carbon Dioxide Emissions from Tourism as a Key Step Towards Achieving Sustainable Tourism

Most tourism-related activities require energy directly in the form of fossil fuels or indirectly in the form of electricity often generated from petroleum, coal or gas. This consumption leads to the emission of greenhouse gases, mainly carbon dioxide. Tourism is not a traditional sector in the System of National Accounts and as a result no country possesses comprehensive national statistics on the energy demand or emissions specifically resulting from tourism. This paper suggests two approaches for accounting for carbon dioxide emissions from tourism: a bottom-up analysis involving industry and tourist analyses, and a top-down analysis using environmental accounting. Using the case study of New Zealand, we demonstrate that both approaches result in similar estimates of the degree to which tourism contributes to national carbon dioxide emissions. The bottom-up analysis provides detailed information on energy end-uses and the main drivers of carbon dioxide emissions. These results can be used for the development of targeted industry-based greenhouse gas reduction strategies. The top-down analysis allows assessment of tourism as a sector within the wider economy, for example with the purpose of comparing tourism’s eco-efficiency with other sectors, or the impact of macroeconomic instruments such as carbon charges.