Energy Related CO2 Emissions before and after the Financial Crisis

Understanding the drivers of CO2 emissions changes is useful in supporting future mitigation. This study applies a log-mean divisia index decomposition to assess four drivers of CO2 emissions changes – population, income, energy intensity and carbon intensity – in 138 countries worldwide over the period 2000–2017. At the global level, income and population are the main drivers of increased emissions over time, with contributions of 116% and 60% to global CO2 emissions changes, respectively. Energy intensity is the key mitigation driver, with a contribution of −80%. Although carbon intensity increased CO2 emissions overall over the period 2000–2017 with a contribution of 4%, it has started to reduce emissions in recent years. China, the United States of America, the European Union, India and Russia are the five regions responsible for most changes in global emissions. The five regions together contribute −73% of the energy intensity effect, and China’s income contribution is 83% in relation to the total of 116%. At the national level, in 2017, CO2 emissions returned to below 2000 levels in 62% of Annex I (developed) countries but increased in 88% of non-Annex I (mostly developing) countries. Among the 35 countries realizing CO2 emissions reductions, 24 were driven primarily by energy intensity, six by carbon intensity, three by economic recession, and one by population decrease. Among the 103 countries with increasing CO2 emissions, 63 were driven primarily by income, 26 by population, nine by carbon intensity increase, and five by energy intensity increase. Our analysis emphasizes the necessity of considering differences in national development stages when formulating climate change mitigation policies. Key policy insights Over the period 2000–2017, at the global and national levels, CO2 emissions increases were driven mainly by economic development and population growth, and mitigation was driven mainly by energy intensity improvement. Improving energy intensity and carbon intensity is the key to mitigating CO2 emissions. Carbon intensity is expected to play an increasing role in the future. In over one-third of Annex I countries, CO2 emissions increased from 2000 to 2017. To meet the Paris Agreement goals, Annex I countries will need to enhance mitigation ambition by further tapping the mitigation potentials of energy and carbon intensity. In accordance with national circumstances, development needs and international support, non-Annex I countries should achieve low-carbon economic and energy transitions and peak CO2 emissions as early as possible.

The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis

This study investigates ways to effectively reduce carbon dioxide (CO2) emissions in Indonesia’s manufacturing industry, by firm characteristics. It is important to determine the firm characters that have the greatest potential to decrease CO2 emissions. The Logarithmic Mean Divisia Index (LMDI) method is used to decompose CO2 emissions into the key factors influencing changes in CO2 emissions, such as economic activity, industrial structure, energy intensity, energy structure, and emissions coefficient during the 2010–2018 period. The findings indicate that changes in CO2 emissions in industrial sub-sectors vary. High technology firms had the lowest average emissions compared to firms with other technology. Large-sized firms had the lowest emissions than small and medium firms. Foreign private firms had lower emissions than national private firms did. Firms in the Java–Bali location had, on average, highest emissions than those outside Java–Bali. Exporting firms had lower average emissions intensity compared to non-exporting firms. This study’s novelty is an analysis of the effect of components that affect changes in CO2 emissions in firm groups based on their characteristics so that policymakers can focus on the potential reduction in CO2 emissions in certain groups of firms, namely firms that use the most energy intensively, is inefficient, and uses low-quality energy. Comparative analysis using firm characteristics reveals that energy-intensive firms’ economic growth determines changes in CO2 emissions in Indonesia’s manufacturing industry.

Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry

Energy related CO2 emissions are important factors responsible for greenhouse effect. Unprecedented increase in anthropogenic gas emissions in the recent decades have led to climatic changes. This study was aimed to decompose the changes in CO2 emissions in Pakistan for the time periods of 1990–2017. The log mean Divisia index was employed to find out changes in CO2 emissions into five factors such as activity effect, structural effect, intensity effect, fuel-mix effect, and emissions factor effect. The analysis confirmed an upward trend of overall emissions of the country during the specified time period (1990–2017). Results of activity effect, structural effect and intensity effect were identified as the three major factors responsible for changes in overall CO2 emissions in the country. Among all effects, the activity effect was investigated as largest contributor to overall changes in CO2 emissions level. The structural effect is positively affecting CO2 emissions indicating a transition of economic activity towards more energy intensive sectors. However, intensity effect has negative relationship with emissions, which is a sign of energy efficiency gains. Energy mix of the country comprises of fossil fuel in excess of 80%. The findings suggest that policy makers should encourage the diversification of energy and output mix towards more energy efficient sub sectors of the economy.

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

Recent empirical studies often support the positive relationship between corporate environmental performance (CEP) in terms of carbon dioxide (CO2) and greenhouse gas (GHG) emissions and corporate financial performance (CFP). However, this depends on the measurements of CEP (the absolute and relative CEP) and CFP (accounting‐based and market‐based CFP). To understand the relationship structurally, based on the literature, this study proposes identity models that integrate CO2 and GHG emissions and financial factors. The models decompose CO2 (GHG) emissions into carbon intensity (GHG intensity), energy intensity, the cost‐to‐sales ratio, the total‐assets‐turnover ratio (TATR), leverage, and equity. The model of supply‐chain GHG emissions additionally adopts supply‐chain GHG intensity. As a decomposition method, this study uses the log‐mean Divisia index. As an application example of the CO2 model, this study targets Japanese manufacturing firms in 16 sectors from fiscal years (FY) 2011 to 2015. Results show that the change in CO2 emissions as of 2015 (−802.1 kilotonnes [kt]) is decomposed into 2922.5 kt for carbon intensity, −26036.3 kt for energy intensity, −6350.5 kt for the cost‐to‐sales ratio, −8495.6 kt for the TATR, −7912.3 kt for leverage, and 45070.1 kt for equity. Average values of relative contribution ratios are 20.6% for carbon intensity, 19.1% for energy intensity, and the remaining approximately 60% for financial factors. Among the 16 sectors, as of 2015, the change in total CO2 emission is statistically significantly positive for equity and significantly negative for the TATR and leverage.

The main objective of this paper is to identify and analyze the key drivers behind changes of CO2 emissions in the residential sectors of the emerging economies, China and India. For the analysis, we investigate to what extent changes in residential emissions are due to changes in energy emissions coefficients, energy consumption structure, energy intensity, household income, and population size. We decompose the changes in residential CO2 emissions in China and India into these five contributing factors from 1990 to 2011 by applying the Logarithmic Mean Divisia Index (LMDI) method. Our results show that the increase in per capita income level was the biggest contributor to the increase of residential CO2 emissions, while the energy intensity effect had the largest effect on CO2 emissions reduction in residential sectors in both countries. This implies that investments for energy savings, technological improvements, and energy efficiency policies were effective in mitigating CO2 emissions. Our results also depict that the change in CO2 emission coefficients for fuels which include both direct and indirect emission coefficients slowed down the increase of residential emissions. Finally, our results demonstrate that changes in the population and energy consumption structure drove the increase in CO2 emissions.

The main purpose of this paper was to identify the driving forces of change in energy-related CO2 emissions in the Polish iron and steel industry in 1990–2017. The analysis relied on the LMDI method used for both the entire study period and the seven 3-year sub-periods. Changes in energy-related CO2 emissions were considered in the context of four factors: the effect of the emission factor; the effect of the energy mix; the effect of energy consumption; and the effect of the production volume of steel. As shown by these analyses, CO2 emissions in the Polish iron and steel industry dropped by as much as over 60% during the study period. That process was primarily driven by a reduction in steel production volumes and in energy intensity of production. In 1990–2017, these factors contributed 48.0% and 50.7%, respectively, to total change in CO2 emissions. Other factors, i.e. emission intensity and energy mix, had a marginal impact. However, the opportunities for further reduction in CO2 emissions seem very limited in the Polish iron and steel industry. That sector is unable to incur the costs of decarbonization investments and requires financial support. Moreover, its continued existence depends on changes to the ETS which will promote low-emission production and will stop the shrinking of the steel market. Thirdly, the steel market needs to be protected against unfair imports, and requires the establishment of the same competition conditions for producers who are not charged with CO2 emission costs.Keywords: CO2 emission, energy use, LMDI decomposition, iron and steel industry, PolandJEL Classifications: Q42, Q43, Q53DOI: https://doi.org/10.32479/ijeep.9444

A decomposition analysis of energy-related CO2 emissions in Chinese six high-energy intensive industries

The CO2 emissions trend and their reduction potential in the Nigerian manufacturing sector from 2010 to 2020 were studied. The Logarithmic Mean Divisia Index was applied to decompose the change in CO2 emissions into pre-set factors: carbon intensity effects, firm energy intensity effects, cost structure effects, asset-turnover effect, asset-to-equity effect, equity-funded production effect and productive capacity utilization. The results show that the change in emissions increased by 1668times {10}^{12} GJ between 2010 and 2020. Energy intensity and equity-funded production were the leading drivers of increased emissions, while productive capacity utilization reduced emissions. The CO2 emissions increased throughout the study, except for a few periods. Without a carbon tax policy, the results show that firm-level drivers increased CO2 emissions in the business-as-usual scenario. However, under the 5% carbon tax (CAT) policy scenario on energy consumption, there was a reduction in CO2 emissions between 2010 and 2020. Furthermore, a CAT policy of 5% on energy consumption reduced CO2 emissions by 22%. A further implication of CAT policy, given its interaction with firm-level drivers, resulted in lowering CO2 emissions in the interactional scenario. The findings indicate productive capacity utilization, equity-funded production, and CAT impacted CO2 emissions variation.

This work aims to create a robust causal regression model that can accurately measure the influence of many variables on greenhouse gas (GHG) emissions in ASEAN nations from 1971 to 2017. The study aims to identify the main factors contributing to emissions and provide valuable information for implementing effective reduction methods. This is important because balancing economic growth and environmental sustainability in the fast- changing ASEAN area is crucial. The research used the Logarithmic Mean Divisia Index (LMDI) decomposition method to examine the individual contributions of various causes to changes in CO2 emissions over time. In addition, a model called ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) is created to anticipate CO2 emissions and determine essential factors that influence them. The dependent variable is total GHG emissions measured in metric tons of CO2 equivalent, while independent variables include GDP, energy intensity, carbon intensity, and population growth. The study discovered that GDP, with varied degrees of impact, is the primary catalyst for CO2 emissions in ASEAN nations. The energy intensity is projected to decline, indicating increases in efficiency, while the influence of population growth is forecast to be positive but less substantial compared to economic reasons. The research is anticipated to uncover disparities in the decrease of carbon intensity and the efficacy of policies across ASEAN nations, with more advanced economies demonstrating more resilient systems. These results provide significant knowledge for governments and companies, aiding in creating focused efforts to reduce emissions and improve carbon accounting standards across the ASEAN region.

Exploring the driving forces and mitigation pathways of CO2 emissions in China’s petroleum refining and coking industry: 1995–2031

China’s extensive and growing CO2 emissions are linked to rapid economic development and advancing urbanization, posing serious concerns in the context of climate change. Decomposition analysis has been widely performed to identify drivers of China’s CO2 emissions. However, to date, no studies have examined the impacts of urbanization on CO2 emissions across all of its provinces. Using provincial statistical data and six key factors influencing CO2 emissions (carbon and energy intensity, residential consumption and consumption inhibition, and population urbanization and size), we applied the logarithmic mean Divisia index (LMDI) decomposition method to examine how urbanization affected CO2 emission changes across 30 provinces between 1990 and 2016. Our results indicated that while urbanization’s impacts on CO2 emissions increased in China as a whole during this period, they were regionally differentiated. The energy intensity effect was the main driver of reduced CO2 emissions, with carbon intensity exerting weaker effects in the 30 provinces, differentiated by their energy structures. The residential consumption effect, which is strongly linked to advancing urbanization, was the primary driver of increased CO2 emissions in all of the provinces. While the consumption inhibition and population urbanization effects were positive at the national level, they were negative in highly urbanized provinces and in highly industrial provinces. These findings highlight the need to promote environmentally friendly consumption and to design regionally differentiated policies and optimized energy structures tailored to particular urbanization contexts. Moreover, they can provide valuable inputs for other developing countries undergoing continuous urbanization, contributing to efforts to balance economic development and environmental sustainability.

Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors