Search

China, as the world's largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China's low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China's economic policies.

Understanding the imbalance of carbon emissions in the urban residential building (URB) sector is beneficial for equitable and effective emission reduction policies. However, carbon imbalance in URB and its major driving factors remain unclear. Therefore, according to the Kaya identity and Zenga index, this study aims to analyze the imbalance in carbon emissions and carbon emission unit area of URB from 2005 to 2019. The results represent the following: 1) Although the overall carbon emission unit area reached its peak value (36.17 kgCO2/m2) in 2011, the overall carbon emission of URB did not reach the peak value, arriving at 0.86 BtCO2 in 2019; 2) the obvious imbalance of carbon emission and carbon emission unit area was led by the population and energy consumption unit area, respectively; 3) Compared to the difference in economy, the difference in climate had a larger impact on inter-group imbalance of carbon emission unit area without heating. In summary, these results and provided policies facilitate future formulation of fair and effective provincial decarbonization responsibility and emission mitigation implementation policies.

Understanding the imbalance of carbon emissions in the urban residential building (URB) sector is beneficial for equitable and effective emission reduction policies. However, carbon imbalance in URB and its major driving factors remain unclear. Therefore, according to the Kaya identity and Zenga index, this study aims to analyze the imbalance in carbon emissions and carbon emission unit area of URB from 2005 to 2019. The results represent the following: 1) Although the overall carbon emission unit area reached its peak value (36.17 kgCO2/m2) in 2011, the overall carbon emission of URB did not reach the peak value, arriving at 0.86 BtCO2 in 2019; 2) the obvious imbalance of carbon emission and carbon emission unit area was led by the population and energy consumption unit area, respectively; 3) Compared to the difference in economy, the difference in climate had a larger impact on inter-group imbalance of carbon emission unit area without heating. In summary, these results and provided policies facilitate future formulation of fair and effective provincial decarbonization responsibility and emission mitigation implementation policies.

Nowadays, a very strong concern is coming from the fact that human intervention is heavily affecting the environment. In the past, the most harmful countries for the environment were the USA and Europe due to their development and level of industrialization. Today, the most impactful countries on the environment are the ones from across Asia, especially China and India. In order to interrupt these issues and to help prevent the further deterioration of the world, the UN redacted the 2030 Agenda. This presents a possible way in which countries might act against the effects of climate changes, reducing global warming and further world pollution. Being the most ambitious in this regard, the EU decided to implement the Green Deal. In our paper, based on the EU accomplishments in this direction, we try to build a scenario of how the world will look like if the three most polluting countries will apply the targets set by the EU. In this attempt, we used the Kaya Identity to measure the forecasted impact and arrived to the conclusion that, by applying this measures, energy consumption will be reduced, the consumption of renewable energy will increase, CO2 emissions will be reduced and the world can manage to come back to the level it had in 1990.

ASEAN is a region with high carbon dioxide (CO2) emissions, accompanied by an increase in population, gross domestic product (GDP) and energy consumption. Population, GDP, and energy consumption can be linked to CO2 emissions through an identity equation called the Rich Identity. This research is based on Kaya identity to describe CO2 emissions to calculate the impact of population, economic activity, energy intensity and carbon intensity on CO2 emissions in ASEAN and 8 ASEAN countries (i.e., Indonesia, Malaysia, Singapore, Thailand, Philippines, Vietnam, Myanmar and Brunei Darussalam) from 1990 to 2017. The method used is the Logarithmic Mean Division Index (LMDI). The data used are from the International Energy Agency (IEA) and the World Bank. Four effects measured and main findings showed that population, economic activity and carbon intensity factor increased by 293.02 MtCO2, 790.0 MtCO2, and 195.51 MtCO2, respectively. Meanwhile, energy intensity effect made ASEAN's CO2 emissions decrease by 283.13 MtCO2. Regarding contributions to the increase in CO2 emissions in all ASEAN countries, the population effect increases CO2 emissions in all countries in ASEAN and the economic activity effect is also the same, except in Brunei Darussalam which makes CO2 emissions in this country decreased by 1.07 MtCO2. Meanwhile, the effects of energy and carbon intensity are different. The effect of energy intensity causes CO2 emissions in lower-middle income countries to decrease, while in upper-middle and high-income countries, it increases carbon emissions. In contrast to the effect of carbon intensity, that actually makes CO2 emissions increase in lower-middle income countries and reduces carbon emissions in upper-middle and high-income countries.

The study investigates the effect of international tourist advents on carbon dioxide discharges in a panel set of hundred countries ranked in order of arrival of tourists over 1995 to 2014, under the background of EKC (Environmental Kuznets Curve) postulate. A multivariate model is adopted grounded on the “Kaya Identity” where the long-run relation concerning the arrival of tourists and carbon dioxide released into the atmosphere are examined through the linkages of economic growth (proxied through new night light data sets), energy intensity use, physical capital formation and human capital formation. The results based on the panel cointegration corroborates the long-run equilibrating association across the set of observations. The methodology of FMOLS, the mean group estimates, DOLS estimation and the Correlated Effects Mean Group estimate enumerate the long-run estimates. Further the results confirm the following causality relationships: tourism-driven emission; economic growth led emission; tourism-driven growth of the economy and gross capital in fixed terms leading the growth of the economy in the short run, built on the Granger causality Wald test. The paper concludes that proper policy direction towards sustainable tourism can reduce emission from tourism in the long run. R and D should focus on the development of green technology and cleaner technology in the tourism ancillary industries.

For decades, the subsidy had prompted excessive and wasteful while offering little motivation to boost energy efficiency or reduce domestic greenhouse gas emissions. This paper aimed to measure household subsidy energy by examining the relationship between the other ten variables. The Logarithmic Mean Divisia Index (LMDI) and decomposition index were deployed to recognize the determinant effects that drive household's subsidy energy consumption. This study also presented an ARDL model applied. The robustness of the Granger Causality, Long-run, and Short-run causality during 1990-2017 was assessed. Based on LMDI, we found out that Population, Income Per Capita, Ratio National Renewal Energy over Fuel Fossil, Gross Capital Stock, Urban Household Consumption, and Ratio Household Subsidy were the positive factors that aggravate the change in household energy subsidy. The negative sign of Ratio National Energy Intensity effect, Ratio Fossil Renewal Energy effect, Ratio Capital Labour substitution, and Ratio Household over Labour Force signified the decreasing significance of less household energy subsidy. On the panel ARD-ECM, we identified a negative sign speed-of-adjustment and significant at 1%. It implied that all the ten variable effects were converging in the long run after an experience shocks. The equation parameters were considered stable since the CUSUM gets inside the two critical lines. Additional RESET test of the stability to ascertain whether the estimated model was linear or correctly specified has been performed.

Global and national scale inventories of carbon dioxide (CO2) emissions are important tools as countries grapple with the need to reduce emissions to minimize the magnitude of changes in the global climate system. The longest time series dataset on global and national CO2 emissions, with consistency over all countries and all years since 1751, has long been the dataset generated by the Carbon Dioxide Information and Analysis Center (CDIAC), formerly housed at Oak Ridge National Laboratory. The CDIAC dataset estimates emissions from fossil-fuel combustion and cement manufacture, by fuel type, using the United Nations energy statistics and global cement production data from the United States Geological Survey. Recently, the maintenance of the CDIAC dataset has been transferred to Appalachian State University, and the dataset is now identified as CDIAC-FF. This paper describes the annual update of the time series of emissions with estimates through 2017; there is typically a 2 to 3 year time lag in the processing of the two primary datasets used for the estimation of CO2 emissions. We provide details on two changes to the approach to calculating CO2 emissions that have been implemented in the transition from CDIAC to CDAIC-FF: refinement in the treatment of changes in stocks at the global level, and changes in the procedure to calculate CO2 emissions from cement manufacture. We compare CDIAC-FF's estimates of CO2 emissions with other global and national datasets, and illustrate the trends in emissions (1990–2015) using a decomposition analysis of the Kaya Identity. The decompositions for the top 10 emitting countries show that, although similarities exist, countries have unique factors driving their patterns of emissions, suggesting the need for diverse strategies to mitigate carbon emissions to meditate anthropogenic climate change. The data for this particular version of CDIAC-FF is available at https://doi.org/10.5281/zenodo.4281271 (Gilfillan et al. 2020).

Energy has an important role in the economic growth of a country, the more energy a country has, the better the country's economy. This study tries to map the development of research published in the field of energy economics. The research was conducted using VOSViewer software. The data analyzed were in the form of scientific research related to energy economics in Islamic countries as many as 45 articles published in the last 10 years. The results showed that the number of publications on the development of research results in the field of energy economics continued to increase, with various research methods and countries of study objects. The network visualization shows that the energy economy research development map is divided into 5 clusters. Cluster 1 consists of 7 keywords, cluster 2 consists of 7 keywords, cluster 3 consists of 5 keywords, cluster 4 consists of 5 keywords and cluster 5 consists of 2 keywords. It was found that the most familiar keywords are Country, Renewable Energy, CO2 Emission. Other findings based on the results of text mining are the analysis of Kaya Identity in Islamic countries and solutions in the form of sustainable energy use.Keywords: Energy Economics, Kaya Identity, Bibliometrics.JEL Classifications: Q40, Q43, Q56DOI: https://doi.org/10.32479/ijeep.10763

The economic loss caused by frequent flood disasters poses a great threat to China’s economic prosperity. This study analyzes the driving factors of flood-related economic losses in China. We used the extended Kaya identity to establish a factor decomposition model and the logarithmic mean Divisia index decomposition method to identify five flood-related driving effects for economic loss: demographic effect, economic effect, flash flood disaster control effect, capital efficiency effect, and loss-rainfall effect. Among these factors, the flash flood disaster control effect most obviously reduced flood-related economic losses. Considering the weak foundation of flash flood disaster prevention and control in China, non-engineering measures for flash flood prevention and control have been implemented since 2010, achieving remarkable results. Influenced by these measures, the loss-rainfall effect also showed reduction output characteristics. The demographic, economic, and capital efficiency effects showed incremental effect characteristics. China’s current economic growth leads to an increase in flood control pressure, thus explaining the incremental effect of the economic effect. This study discusses the relationship between flood-related economic loss and flash flood disaster prevention and control in China, adding value for the adjustment and formulation of future flood disaster prevention policies.

STIRPAT (stochastic impact by regression on population, affluence, and technology) model is used to identify the influencing factors of agricultural carbon emissions in Jiangsu province. By referring to Kaya identity and combining with the actual situation of agricultural carbon emissions, four basic influencing factors were obtained: agricultural production efficiency, agricultural structure, agricultural economic development level, and agricultural population size. In addition, urbanization, mechanization, and natural disaster level were listed as influencing factors. The results demonstrated: (a) Urbanization was the first promoting factor of agricultural carbon emissions, indicating a 0.2510% increase in agricultural carbon emissions due to a 1% increase in urbanization. The other three positive factors were, respectively, agricultural mechanization, agricultural structure, and agricultural economic development and their influence indexes were 0.1481, 0.1163, and 0.0845, respectively. (b) Agricultural production efficiency was the most important factor to restrain agricultural carbon emissions. For every 1% increase in agricultural production efficiency, corresponding agricultural carbon emissions would be reduced by 0.3288%. Agricultural population size was also an important factor to reduce agricultural carbon emissions and its influence index was −0.045. Finally, we propose policy recommendations including implementation of orderly urbanization, dependence and development of low carbon technology, establishment of agricultural carbon compensation mechanism, etc.

An extended Kaya identity and the Logarithm Mean Divisia Index approach were applied in this paper to identify, quantify and explain the main factors behind Cameroon’s CO2 emissions before, during and after the economic crisis of the years 1980. The analyses covered the period from 1971 to 2014 and the results showed that: (1) Cameroon’s carbon intensity increased by 75 and 47% during the periods before (1971–1984) and after (1984–1994) the economic crisis, while it decreased significantly by − 135% during the crisis period (1994–2014). At the same time, the country’s emission factor increased by 30% between 2007 and 2014. (2) The effect of the demographic change was the main driver of Cameroon’s CO2 emissions during the periods 1984–1994 and 1994–2014, whereas the effect of economic activity was the main driver of the increase in these emissions between 1971 and 1984. (3) The energy intensity effect contributed to the increase in CO2 emissions during the period 1984–1994 in the same way as the effect of demographic change. However, this factor helped reduce CO2 emissions in the other two periods. (4) Although the effect of substitution of fossil fuels and the effect of renewable energy all contributed to reducing CO2 emissions during the period of this study, we found that the effect of renewable energy behind CO2 emissions remains insignificant compared to the renewable energy potential available in Cameroon. Finally, policy recommendations aimed at enabling Cameroon to achieve its objectives of reducing CO2 emissions were formulated in this article.

China has become the largest carbon-emitting country in the world since 2007. To achieve national environmental goals by 2030, the carbon emissions per unit of gross domestic product (GDP) will need to fall to 60-65% of 2005 levels. Such a dramatic decrease presents a challenge for a nation in adjusting its energy source and usage, but via monitoring of reductions, greater understanding can be gained of how carbon emitters are responding to national goals. We analyzed the change in carbon emissions from China's fossil energy consumption from population, per capita GDP, energy efficiency improvements and energy structure using a Kaya identity model and Logarithmic Mean Divisia Index (LMDI) factor decomposition method from 2006 to 2018. Results suggest that trends in carbon emissions from 2006 to 2018 can be broken down into four periods: a rapid increase period during 2006-2011, a slowdown increase period during 2011-2014, a consecutive decline period during 2014-2016 and a rebound during 2017-2018. Trends in carbon emissions were greatly affected by per capita GDP and energy efficiency. While per capita GDP increased carbon emissions, energy efficiency had a countering effect on carbon emissions. Our results suggests that China's measures in the past decade to reduce carbon emissions (i.e. carrying out carbon emissions trading on a fixed basis, readjusting the economic structure, optimizing the energy structure, improving energy efficiency and increasing forest carbon sinks) have helped to reduce carbon emissions. However, China should continue to actively respond to climate change while striving to achieve of economic sustainable development and social progress.

Complying with the goals of the Paris Agreement on climate protection will require a substantial decarbonization of the global energy system by the second half of the century. Developing countries and emerging economies already account for more than half of global energy related emissions. Strong economic and demographic growth in these countries will drive future power sector expansions. At the same time, solar photovoltaics has experienced massive cost reductions in recent years. Despite substantially lower demand growth for electricity and comparably lower solar potentials, eight out of ten countries with the largest additions of solar PV installations in the last decade are highly developed countries. In this paper, we analyze the perspective towards solar PV integration by country development level. We firstly apply Kaya decomposition analysis to analyze emission dynamics by country groups. We find renewable energies to play a minor role in countries of low human development. Combining financing data and country specific solar irradiation potentials, we find that for many countries in the Global South, comparably inferior financing conditions overcompensate the higher irradiation potentials, making capital intensive renewable energy projects unviable. Our results highlight the importance of access to affordable finance for capital intensive renewable energy technologies, such as solar PV, and suggest that climate policies in countries of low human development should more decidedly focus on reducing risk premiums, if these are to represent an economically viable option for climate mitigation.

This paper aims to analyse and compare the driving forces of the carbon dioxide emissions of the six highest emitters of the world, namely, China, the United States of America, the European Union, India, Russia, and Japan, which are responsible for more than the 67\% of the emissions, during the period 1990-2018. The analysis is based on an enlarged Kaya-LMDI decomposition, considering five driving forces and a Granger causality study. Both techniques allow us to disentangle the relationship among the different driving forces and how they change from country to country. The main conclusion from the Kaya-LMDI analysis is that economic growth has been the main driving force that increases CO$_2$ emissions, and to a much lesser extent, the increase in population in most of the six analysed economies. On the other hand, energy intensity is the main factor for reducing CO$_2$ emissions. Surprisingly enough, the end-use fuel-mix term seldom contributes to the decrease of the emissions, which proves that the use of renewable energy should still be actively promoted. It is worth highlighting the different behaviour observed between the four developed countries and the two most populous developing ones, China and India. The Granger-causality analysis suggests that energy intensity Granger causes GDP in the developed countries, energy intensity also Granger cause CO$_2$ emissions in half of the countries and, GDP Granger causes CO$_2$ emissions only in one case, Japan., This work has been partially supported by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Spain), under Group FQM-370 and by European Regional Development Fund (ERDF), ref. SOMM17/6105/UGR. Resources supporting this work were provided by the CEAFMC and Universidad de Huelva High-Performance Computer (HPC@UHU) funded by ERDF/MINECO project UNHU-15CE-2848.

Esta tesis tiene como objetivo principal analizar y comparar las fuerzas motrices que conforman las emisiones de dióxido de carbono en India, dada la especial relevancia mundial que se prevé tendrán dichas emisiones. Para ello, se han empleado distintas técnicas que se aplicarán, a modo de comparación, a los seis mayores emisores del mundo, a saber, China, los Estados Unidos de América, la Unión Europea, India, Rusia y Japón, responsables de más del 67% de las emisiones globales durante el periodo 1990-2018. El análisis se basa, por un lado, en una descomposición LMDI de una identidad Kaya ampliada, considerando cinco fuerzas motrices y, por otro lado, en un estudio de causalidad de Granger. Ambas técnicas nos permiten desentrañar la relación entre las diferentes fuerzas motrices y cómo cambian estas de un país a otro, facilitando la comprensión del caso indio. Por otra parte, se realizará una extrapolación para la India hasta 2030, dentro de unos escenarios propuestos, de las emisiones y el Producto Interior Bruto en el país para, de esta manera, poder prever el comportamiento a futuro de la intensidad energética, indicador propuesto por India para el cumplimiento de sus Compromisos en los Acuerdos de París. La principal conclusión del análisis Kaya-LMDI es que el crecimiento económico ha sido el principal motor que aumenta las emisiones de CO2 y, en mucha menor medida, el aumento en población, cosa que sucede en la mayoría de las seis economías analizadas. Por otro lado, la intensidad energética es el factor principal para reducir las emisiones de CO2. Sorprendentemente, el término de mezcla de combustible rara vez contribuye a la disminución de las emisiones, con las excepciones notables de los EEUU y la UE, lo que demuestra que el uso de energías renovables aún debe promoverse activamente. Cabe destacar el diferente comportamiento observado entre los cuatro países desarrollados y los dos países en desarrollo, que son además los de mayor población, China e India. El análisis de causalidad de Granger sugiere que la intensidad energética da lugar a causalidad de Granger con el PIB en los países desarrollados, la intensidad energética también da lugar a causalidad de Granger con las emisiones de CO2 en la mitad de los países y el PIB da lugar a causalidad de Granger con las emisiones de CO2 solo en un caso, Japón. La extrapolación de los datos, dentro de los escenarios propuestos, sugiere que el cumplimiento de la NDC india solo se puede llevar a cabo empleando todas las medidas propuestas por el país en su NDC, a saber, ampliación del uso de las energías renovables, crecimiento económico e implantación de tecnología supercrítica en el conjunto de las plantas de carbón para la generación eléctrica, con la consiguiente mejora en la eficiencia., The main objective of this thesis is to analyse and compare the driving forces that cause carbon dioxide emissions in India, given the special global relevance that these emissions are expected to have. To do this, different techniques have been used and are applied, as a matter of comparison, to the six largest emitters in the world, namely China, the United States of America, the European Union, India, Russia and Japan, responsible for more than 67% of global emissions during the period 1990-2018. The analysis is based on an LMDI decomposition procedure of an expanded Kaya identity, considering five driving forces and a Granger causality study. Both techniques allow us to unravel the relationship between the different driving forces and to know how they change from one country to another, facilitating the understanding of the Indian case. On the other hand, an extrapolation will be carried out, within some proposed scenarios, for the CO2 emissions and the Gross Domestic Product in the country, in order to be able to predict the future behaviour of energy intensity, an indicator proposed by India for the fulfilment of its Commitments in the Paris Agreements. The main conclusion of the Kaya-LMDI analysis is that economic growth has been the main driver for increasing CO2 emissions and, to a much lesser extent, population growth in most of the six economies analysed. On the other hand, energy intensity is the main factor to reduce CO2 emissions. Surprisingly, the term end-use fuel mix rarely contributes to emissions declines, showing that the use of renewable energy still needs to be actively promoted. It is worth noting the different behaviour observed between the four developed countries and the two developing ones, which are the most populated ones, China and India. Granger causality analysis suggests that energy intensity Granger causes GDP in developed countries, energy intensity also Granger also causes CO2 emissions in half of the countries, and GDP Granger causes CO2 emissions in only one case, Japan. Extrapolation of the data, within the proposed scenarios, suggests that compliance of the Indian NDC can only be achieved by employing all the measures proposed by India in its NDC, namely, renewable energy expansion, economic growth and deployment of supercritical technology in all coal plants for electricity generation, with the consequent improvement in efficiency.

It is important to effectively reduce carbon emissions and ensure the simultaneous adjustment of economic development and environmental protection. Therefore, we used Kaya identity to screen the factors influencing carbon emissions and conducted preliminary qualitative analyses, including grey relation analysis and linear regression analysis, on important variables to establish a vector autoregression (VAR) model based on their annual data to empirically analyze the influencing factors of carbon emissions. The results showed that economic growth effect, energy intensity effect and embodied carbon in foreign trade were the key factors affecting carbon emissions, among which the economic growth effect contributed the most. Accordingly, we propose countermeasures including technological innovation to reduce energy intensity, the development of new energy sources to improve energy structure, acceleration of industrial structure transfer, and optimization of trade structure.

Infrastructure provision is widely emphasised amid industrialisation, modernisation and urbanisation, for which plenty efforts have been devoted across countries globally. Nevertheless, it should be noted that the shortage or over-supply of urban infrastructures has already induced various urban symptoms, such as urban storm water runoff, urban heat island effect, and habitat destruction, and this is particularly the case in the context of some emerging economies. China, the largest-volume developing country worldwide, has witnessed an unparalleled urbanisation process since the post-reform era, whilst is simultaneously being challenged with critical urban problems triggered by the inappropriate expansion of urban infrastructures. These urban problems manifest more explicitly in mega cities. It is considered essential for these large cities to address properly the scale of urban infrastructures (UIS) by underpinning the understanding of the driving factors for UIS expansion, so that proper planning and construction strategies can be designed and implemented. Extant literature presents inherent limitations in duly exploring the driving factors of UIS expansion, and in filling this research gap, this study therefore aims to investigate the driving factors of UIS expansion in the context of 35 Chinese large cities. Kaya identity and Logarithmic Mean Divisia Index (LMDI) method are applied collectively for conducting the research. The findings from this study can be highlighted as follows: Utilisation efficiency (UE) is found inhibits UIS expansion in 77% (27/35) surveyed Chinese large cities; economic output (EO) and population (P) present significant role in stimulating UIS enlargement across all the surveyed large cities. Tailor-made policy recommendations have been synthesised by drawing upon the understanding of UIS expansion driving factors, which can facilitate local urban governors to address appropriately the scale of urban infrastructure construction and delivery, and further contribute to the sustainability improvement of urbanisation paradigm across the country of China.

The G7 and Brazil, Russia, India, China and South Africa (BRICS) nations have committed to greenhouse gas emission reduction according to targets set out in the Paris Agreement and Copenhagen Accord. The objective of this paper is to develop an index decomposition framework and apply it for comparison of the drivers behind carbon dioxide emission. The impact of economic and technological development on greenhouse gas emissions is assessed by applying the Kaya identity. The index decomposition analysis is carried out by applying the Shapley value. In this paper, we seek to compare the two groups of countries—G7 and BRICS—in regard to the underlying trends in the energy‐related carbon dioxide emission during 1990–2015. The comparison of these two groups for countries draws some light on the pathways of decarbonization of the economies in developing and developed countries. The time period covered spans over the sub‐periods of economic growth and decline. Energy intensity appeared as the major factor pushing the carbon dioxide emissions down in the developed countries (i.e., G7 group). The latter effect was much lower for the developing countries (i.e., BRICS group) and was offset by the affluence effect. Further development of the renewable energy is important for the developing countries in order to start exploiting the carbon factor effect.

The rapid global economic development over the past two decades has been accompanied by rising energy demand and CO2 emission rates. Understanding the driving forces of CO2 emissions is necessary for future energy planning and policy formulation. This paper examines the driving factors behind the increase in CO2 emissions in Malaysia with special focus on the manufacturing, electricity and transportation sectors. The paper extends the Kaya identity by incorporating energy mix, investment efficiency, capital-labor substitution, population-to-employment, urbanization rates, and per capita CO2, along with the standard variables including economic activity, economic structure, and energy intensity. The paper also evaluates if any macroeconomic instability (economic crisis) in the country has led to reduction in emission rates. The study utilizes the latest country data, covering the period 2002–2016. The result shows that, while economic activity is always associated with CO2 emissions, more efficient energy use would help restrain the rise in emission rates without hampering economic growth regardless of economic structure. Emission reduction targets in Malaysia should be reinforced with an appropriate economic restructuring that restrains the economic structure effect. Due to the extensive energy use in the transportation and electricity generation, future emission control should focus more on these sectors.

Under the background of global climate change, China has been confronted with the dual pressure of CO2 emissions reduction and PM2.5 pollution control. This research aims to explore the mechanism of changes in PM2.5 emissions, which are the key airborne pollutants causing haze. Furthermore, it quantifies the impacts of CO2 emissions reduction activities on PM2.5 emissions reduction. This study takes aggregate PM2.5 emissions instead of PM2.5 concentration index as the research object. Based on an extended kaya identity, LMDI approach is first performed to decompose the changes of PM2.5 emissions during 1998–2014, taking into consideration the synergistic effect of carbon emissions on PM2.5 emissions. Furthermore, following LMDI decomposition, this study adopts the econometric methods to quantify the synergistic effect of CO2 emissions reduction on PM2.5 emissions reduction over the period 1999–2014. The empirical results are as follows: (1) the LMDI decomposition results specify that the synergistic emissions reduction accounts for the most of the reduction in PM2.5 emissions. In addition, energy intensity changes also contribute to the reduction in PM2.5 emissions; (2) by contrast, it is found that the economic development effect is the main factor resulting in the increase of PM2.5 emissions, while the contributions of the energy emission intensity effect and population effect to PM2.5 emissions changes are relatively little; (3) all the models show CO2 emissions reduction activities will significantly contribute to PM2.5 emissions reduction; (4) for every 10,000 t increase in CO2 emissions reduction, PM2.5 emissions reduction will increase by 3.3 t, and the potential for synergistic emissions reduction of PM2.5 differs distinctly among different provinces; (5) technological progress and population density positively influence PM2.5 emissions reduction, while coal consumption rate has a negative impact on PM2.5 emissions reduction, in addition, there is an inverted U-shaped curve relationship between per capita GDP and PM2.5 emissions reduction.

As one of the energy-intensive industries, the CO2 emissions of China's petroleum refining and coking industry (PRCI) put significant pressure on China's emission reduction target. Considering this, the main purpose of this paper is to clarify the determinants of emission growth in China's PRCI and predict the emissions change trend in future. Based on an extended Kaya identity, this paper dynamically analyzed the impacts of investment scale, investment efficiency, energy intensity, energy structure and carbon dioxide emission factor on CO2 emissions from China's PRCI. Then, the possible CO2 emissions of China's PRCI in 2030 were dynamically predicted based on the application of probabilistic scenario analysis. The main findings include: (1) during 2000–2016, China's PRCI suffered 130.82 million tons CO2 emissions growth, investment scale expansion was the principal contributor stimulating CO2 emissions. Fortunately, the promoting effects exerted from this factor showed a downtrend along with the decreasing fixed-asset investment growth rate since China's economy has entered a new normal. (2) During the entire study period, energy structure adjustment brought about a mild increase in CO2 emissions rather than taking an active role in emission abatement. (3) Investment efficiency and energy intensity were two crucial inhibitors, followed by the carbon dioxide emission factor. However, the inhibiting effects resulting from these factors cannot completely counteract the promoting effects in most cases. (4). Scenario analysis indicates that the mitigation pathway of CO2 emissions in China's PRCI include properly curbing the fixed-asset investment, increasing the green investment for technology and equipment, paying more attention to the R&D of energy saving technology in PRCI and its downstream industry, and facilitating the use of clean electricity.

In order to respond to climate change, China has committed to reduce national carbon intensity by 40–45% in 2020 and 60–65% in 2030, relative to 2005. Given that energy-intensive industries represent ~80% of total CO2 emissions in China and that China is a large and diverse country, this paper aims to investigate the potential contribution of regional convergence in energy-intensive industries to CO2 emissions reduction and to meeting China's emissions goals. To the best of our knowledge this matter has never been explored before. Using panel data from 2001 to 2015, we build three scenarios of future carbon intensities: business as usual (BAU), frontier (based on the directional distance function, in which all regions reach the efficiency frontier) and best available technology (BAT, in which all regions adopt the lowest-emitting technology). The frontier and BAT scenarios represent a weak and a strong form of regional convergence, respectively, and the BAU assumes that it develops following historical patterns. We then use the Kaya identity to estimate CO2 emissions up to 2030 under the three scenarios. Our results are as follows: (1) Under BAU, the CO2 emissions of energy-intensive industries increase from 7382.8 Mt in 2015 to 8127.6 Mt in 2030. Under the frontier scenario the emissions in 2030 are 44.23% lower than under business as usual, while under the BAT scenario this value becomes 84.81%. Electricity and ferrous metals are the sectors that most contribute to the reduction potential. (2) Even under BAU the carbon intensity of energy-intensive industries as a whole and all of its constituent sub-sectors except for electricity will decrease by more than the nationally-mandated averages. (3) Regional convergence could help the energy-intensive industries peak its CO2 emissions before 2030, while under BAU the absolute emissions of the energy-intensive industries keep increasing.

To effectively combat global warming, an enormous reduction in CO2 emissions is required. Cameroon, which is currently the largest emitter of CO2 in the CEMAC subregion, has committed to reducing its greenhouse gas emissions by 32% by 2035. However, previous studies in Cameroon have only addressed the relationship between economic growth, energy consumption, and CO2 emissions without estimating all causal relationships at the same time. Moreover, no study has yet decomposed this country's CO2 emissions to date. To fill these research gaps and further assess the determinants of these CO2 emissions, an extended Kaya identity and the Logarithm Mean Divisia Index (LMDI I) have been applied in this paper to identify, quantify, and explain the main drivers of Cameroon's CO2 emissions from 2007 to 2014. Seven effects were measured and the main findings show that carbon intensity and the emission factor increased by 0.57% and 107.50% respectively. Regarding contributions to the increase of CO2 emissions, the population effect was the most positive followed by the activity effect, whereas the energy intensity, the substitution of fossil fuels and the penetration of renewable energies have contributed to reduce the CO2 emission. To enable Cameroon to not only achieve the goals of its vision but also develop a low-carbon economy, this paper provides some proposed avenues that should be considered by policymakers.

The Greenhouse gas emissions one of the hot topics all around the world which are causing climate change. In the face of anthropogenic climate change, increasing the carbon dioxide emissions as well as the development of Turkey seems to be a serious challenge for the success of global carbon dioxide emission reduction efforts. A decomposition analysis of historical and projected carbon dioxide emissions from fossil fuel combustion of Turkey has been investigated by using the Logarithmic Mean Divisia Index technique by considering in particular carbon intensity, energy mix, energy intensity, affluence and population effects. The projected data have been obtained from International Panel on Climate Change based sustainable emission scenario. The results show that the all effects on carbon dioxide emissions were positive for historical evaluation. On the other hand, carbon intensity, energy mix and energy intensity effects on carbon dioxide emissions were negative while affluence and population effects were still positive for projected evaluations. Therefore, decision makers should be reconsider the carbon dioxide emissions reduction targets and some related policies.

Environmental problems are escalating with rapid economic development; the Chinese government actively follows a low-carbon development pattern and has made CO2 emissions reduction commitments for 2030. In the future, how to realize the 2030 CO2 emissions peak target and intensity reduction target has become an urgent problem for China to solve. This study aims to predict China's CO2 emissions trends by 2030 under different scenarios and to calculate the reduction potential of each sector and the impact factors separately. A comprehensive forecasting model was developed in this study based on the Kaya identity, Log-Mean Decomposition Index (LMDI) model and Scenario Analysis methods. According to this model, China's CO2 emissions will grow to 112.89 million tons in 2030 but not reach its peak under the target scenario (TS), while the CO2 emissions intensity will be 0.40 ton per 10,000 RMB, achieving the “Paris Commitment” for CO2 intensity reduction targets. In terms of emissions reduction factors, such as economic growth, energy technology progress and energy structure optimization are the main factors to achieve CO2 reduction targets, with the annual average mitigation potential of 8.83 million tons, 9.41 million tons and 5.80 million tons, respectively. In terms of China's six economic sectors, the 2nd sector(industrial sector) will have the largest reduction potential during 2016–2030, with an average annual reduction of 23.24 million tons, while the reduction from energy technology advancements contributes the most, followed by economic growth and energy structure optimization, with the annual average reduction rate of 44.35%, 30.92% and 19.64%, respectively. The key pathways for reducing China's CO2 emissions in the future are to adjust the economic development model in the 2nd, 4th, and 6th sectors, to improve the energy efficiency of the 2nd sector, and to optimize the energy consumption structure of the 1st, 2nd, and 6th sectors. Finally, we suggest that China should construct mutually compatible mitigation policies networks and focus on the superposition effect of various subsystem policy objectives to realize the "Paris commitment" for China's 2030 CO2 mitigation targets.

Decoupling economic development from carbon emissions generated from China's commercial buildings (CECCB) is regarded as an important indicator for evaluating the energy efficiency of the commercial building sector in China. Our study is the first to propose a decoupling method based on a Logarithmic Mean Divisia Index (LMDI) decomposition analysis with the Kaya identity to analyze the relationship between economic development in China's Tertiary Industry and the CECCB growth at both national and municipal levels. The following three main results are found. (1) At the national level, commercial building sector decoupling from 2001 to 2015 is limited. Only four decoupling stages are observed at the municipal level with the ordering of municipal decoupling measured as follows: Tianjin > Beijing > Shanghai > Chongqing (2001-2010), Chongqing > Beijing > Tianjin > Shanghai (2011-2015). (2) Two extended versions of Environmental Kuznets Curves further show what drives different decoupling levels in the four municipalities. (3) More significant decoupling effects observed in recent years can be attributed to significant improvements made in the energy efficiency work of China's commercial building sector. Overall, our approach successfully covers a research gap relevant to the decoupling of the relationship between CECCB growth and the economic development of China's Tertiary Industry. Furthermore, we believe our results can be used to guide the evaluation of energy efficiency work in China's commercial building sector, and such efforts can also enrich diverse research conducted on China's low carbon economic systems.

China produced the largest volume of carbon emission in the world over the last few years. The annual carbon emissions from the construction land (CECL) of Shanghai soared by 15.40 million tons at a growth rate of 3.70% from 1999 to 2015, whereas the area of construction land expanded by 1120.57 km2, increasing by 2.71% annually. Using Kaya identity and the logarithmic mean divisia index method (LMDI), we calculate the contributions of energy structure, energy intensity, industrial structure, land economic output, population density, and area of construction land area to CECL in Shanghai, and confirmed land economic output as the main driving factor of CECL increase. Then, we examined the decoupling degree of CECL from economic growth driven by the six factors using Tapio elastic decoupling index. Strong decoupling of CECL from economic growth occurred in 2002, 2012, 2014, and 2015, whereas weak decoupling occurred in the rest 13 years. Decoupling is mainly driven by land economic output and energy intensity. Ultimately, we eliminate the influences of land economic output and measure the effort of the five factors on decoupling CECL from economic growth, and indicates the area of construction land as a viable policy tool.