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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

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.

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.

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.

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.

The deployment of renewable energy sources (RES) is an essential strategic objective of sustainable energy development in Estonia, Latvia and Lithuania. Their growing contribution to the total primary energy supply can significantly facilitate the transition to a low-carbon economy. The paper provides findings from an in-depth comparative analysis of RES deployment trends during 2010–2019 in the Baltic countries in the context of energy policy documents of the European Union (EU). The dynamics of targeted RES indicators according to the Renewables Directive 2009/28/EC and National Programmes and the role of renewable energies in mitigating climate change are analysed. A key role of the heating and cooling sector in deploying RES is highlighted and a necessity to implement radical changes in the transport sector of the Baltic countries is revealed. The paper examines changes in energy-related greenhouse gas (GHG) emissions and the impact of driving factors in Estonia, Latvia, Lithuania and other countries of the Baltic Sea Region (BSR). The Kaya identity and the logarithmic mean Divisia index (LMDI) method are used for the decomposition analysis. Based on the analysis conducted, the impact of population change, economic growth, decline of energy intensity, RES deployment and reduction of emission intensity on change of GHG emissions in countries of the BSR and, on average, in the EU-27 during 2010–2019 is revealed. Keywords: Baltic states; Baltic sea region; renewable energy sources; GHG emissions; decomposition analysis.

Livestock cultivation is a significant source of greenhouse gas (GHG) emissions, accounting for 14.5% of the total anthropogenic emissions. China is responsible for a considerable share of the global livestock emissions, particularly caused by pork production. We used the Kaya identity and the logarithmic mean Divisia index (LMDI) to decompose the national annual GHG emissions from enteric fermentation and manure management in pig farming in China from 1976 to 2016. We decomposed the sources of the emissions into five driving factors: (1) technological progress (e.g., feed improvement); (2) structural adjustment in the livestock sector; (3) structural adjustment in agriculture; (4) affluence; and (5) population growth. The results showed that the net GHG emissions from the pig sector in China increased 16 million tons (Mt) of carbon dioxide equivalents (CO2eq) during the study period. The decomposition analysis revealed that structural adjustment in agriculture, growing affluence, and population growth contributed to an increase of the GHG emissions of pork production by 23, 41, and 13 Mt CO2eq, respectively. The technological progress and structural changes in animal husbandry mitigated emissions by –51 and –11 Mt CO2eq, respectively. Further technological progress in pig production and optimizing the economic structures are critical for further reducing GHG emissions in China’s pig industry. Our results highlight the dominant role of technological changes for emission reductions in the pig farming.

Understanding the factors driving CO2 emissions and their spatial behavior is vital to lessen natural and anthropogenic emissions. Lessen CO2 emissions can impact elderly health, especially those living in affected regions. The constant search for sustainable development motivated the elaboration of this work, whose purpose is to build a ranking of the most sustainable countries concerning CO2 emissions, according to the criteria established in the Kaya identity indicator. Furthermore, this study aims to push to enact policy for improving the population health, elderly ones. For the study, the economic block G20 was selected due to its representativeness in the world scenario, with the application of the multi-criteria method PROMETHEE II for the country order, whose data were obtained from the International Energy Agency. As a result, the five best positions in the ranking are represented by countries with excellent economic projections and energy efficiency according to the criteria.

We investigate the progress of the UK universities in greening their energy sources in line with the UK's goal of becoming a net-zero economy by 2050. Using the HESA estate management data for 116 universities over 2012-13 to 2018–19, we employ a Log Mean Divisa Index decomposition method within an extended Kaya identity framework to decouple the changes in total carbon emissions from a range of variables, with a special focus on the impact of different energy sources on energy use and carbon efficiency measures. Overall, between 2012-13 and 2018–19, universities have reduced emissions by 29% although their energy consumption remained mostly stable, implying that these reductions mostly stemmed from reductions in emission coefficient effect (which measures carbon efficiency of energy generation) by 24% and energy intensity effect by 25%. Consistently, estimated correlation coefficients confirm that emission coefficient, intensity, and affluence effects are major contributors behind the annual change in total emissions, with estimated correlation coefficients being 0.42, 0.66, and −0.24, respectively. The share of renewable energy sources was reduced by 2.2%, which is a major reason, in addition to increased number of students, behind the sector's overall failure achieve the 2020 goal of reducing emissions by 43% from the 2005 level. Finally, our results also expose considerable regional variations in mitigating and worsening factors behind emissions that calls for stronger coordination and supervision by policymakers.

Reducing agricultural carbon emissions (ACE) is a key point to achieve green and sustainable development in agriculture. Based on the ACE statistics of Jilin Province in China from 1998 to 2018, this article considers the sources of ACE in depth, and fourteen different carbon sources are selected to calculate ACE. Besides, the paper explores the variation characteristics of ACE in Jilin Province, their structure, and the relationship between the intensity and density of the dynamic changes in ACE in the province in terms of time. Finally, this paper uses the Kaya identity and logarithmic mean Divisia index (LMDI) to analyze the influential factors in ACE. The results show the following: (1) During 1998&ndash, 2018, the amount of ACE in Jilin Province increased, with an average annual growth rate of 1.13%. However, the chain growth rate has been negative in recent years, which reflects that carbon emission reduction has been achieved to a certain extent. (2) The characteristics of ACE in Jilin Province during the years is that of the low-intensity, high density category. Furthermore, agricultural resource input is the main source of the planting industry&rsquo, s carbon emissions. From the perspective of animal husbandry, the proportion of CH4 decreased, while the proportion of N2O is relatively stable. (3) Based on the LMDI decomposition model, production efficiency, industrial structure, and labor are the three main factors that reduce ACE in Jilin Province. The economic level is the main factor of ACE, and it will be the most important factor leading to an increase in ACE in the short term. On the basis of comprehensive analysis, this article puts forward reasonable suggestions in terms of policy improvement, production mode and industrial structure adjustment, technological innovation, and talent introduction.

Knowledge about the driving forces behind greenhouse gasses (GHG) emissions is crucial for informed and evidence-based policy towards mitigation of GHG emission and changing production and consumption patterns. Both national and regional-level authorities are capable of addressing their actions more effectively if they have information about the spatial distribution of phenomena related to the policies they conduct. In this context, the main aim of this paper is to explain the regional differences in carbon intensity in Poland. The differences in carbon intensity between regions and the national average were analysed using index decomposition analysis (IDA). Aggregate carbon intensity for regional economies as well as the carbon intensity of households was investigated. For both levels of analysis: total emissions and emission from households economic development is the key factor responsible for the inter-regional differences in carbon emission per capita. In the case of total emissions, the second important factor influencing these differences is the structure of the national power system, i.e., its concentration and the production of energy from fossil fuels. For households, disposable income per capita is a key factor of differences in CO2 emission per capita between regions. Higher households’ incomes contribute to higher emission per capita, mostly due to the shift in consumption towards more energy- and material-intensive goods. The contribution of energy emissivity is quite low and not as varied as in the case of income. This suggests that policy instruments targeted at the consumption of fuels can be rather uniform across regions, while more developed regions should also be subject to measures supporting less energy-intensive consumption. On the other hand, policy in less developed regions should prevent them from following the path of per capita emissions growth.

Vietnam has witnessed a rapid increase in national-level CO2 emissions due to rising urbanization, economic expansion, export growth, and industrial development. Moreover, to support the ambitious economic growth targets, reliance on and consumption of fossil fuels are increasing by each passing year. With this circumstance, this study aims to analyze the key drivers of CO2 emissions in Vietnam from 1990 to 2016 using the Kaya identity and decomposition method. Following this approach, CO2 emissions have been decomposed into five effect categories comprising population, affluence, energy intensity, fuel mix, and emission intensity. As per the results, CO2 emissions in Vietnam were mainly driven by rising affluence (58.5%) and changing fuel mix (33.2%) which have resulted from improved living standards, rapid industrial development, and higher fossil fuel consumption. Moreover, population (13.8%) and emission intensity (3.1%) exhibited a relatively lower impact on CO2 emissions during 1990-2016. However, energy intensity (- 8.7%) was the only negative driver which has resulted in the slowdown of carbon emissions in Vietnam. Based on the analysis of energy policy development, the share of renewable energy resources was still quite low in the national energy mix with higher reliance on traditional fossil fuels (mainly coal and petroleum). Therefore, to make a transition towards low-carbon economic growth, significant improvements in energy efficiency and emission intensity are necessary together with national energy mix restructuring for low-carbon economic growth.

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990&ndash, 2016. Afterwards, we analyzed the contribution rate of sectors&rsquo, energy intensity effect and sectors&rsquo, economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990&ndash, 2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001&ndash, 2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011&ndash, 2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry&rsquo, s economic structure and transportation&rsquo, s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

Shifting from fossil-fuel (FF) to renewable energy systems, a process known as energy transition (ET), is crucial for developing countries as the ET brings new opportunities to accomplish leapfrog development for achieving carbon neutrality. Parallel to climate change, developing countries face energy security and independence issues with extra pressures such as the Covid-19 pandemic and increasing inequality. Taking Mexico as a case, four ET scenarios under “gradual” or “rapid” narratives are developed with the Kaya Identity. Gradual Transition Scenarios (GTS) present a business-as-usual scenario with natural gas as the most significant fuel in the supply mix due to economic and social concerns. Rapid Transition Scenarios (RTS) propose a combination of FF and renewables or a full deployment of Renewable Energy Sources (RES). The results show that GTS reach 491∼ 501 Mt CO2 emissions in 2050, risking a carbon lock-in, stranded assets and economic losses. RTS reach 87∼103 Mt CO2 in 2050 with RES prioritization, energy efficiency improvements and coal phase-out. In particular, following GTS would mean failing to join global efforts to limit warming temperature to 1.5 °C. Alternatively, following RTS brings Mexico closer to achieve carbon neutrality. Several strategies are proposed, including reducing the carbon intensity of the energy supply mix, increasing energy efficiency, prioritizing environmental protection, improving actions based on science, and promoting environmental education and awareness. This paper presents a consistent methodology to encourage ET in developing countries where the role of science is still not fully exploited under the goal of carbon neutrality.

Measuring carbon abatement in China's commercial buildings (CACCB) has been recognized as a path to evaluate the energy conservation work (ECW) in China's commercial building sector. This study first presents a bottom-up model for measuring the CACCB values based on decomposing the extended Kaya identity via the Logarithmic Mean Divisia Index (LMDI) method. The results indicate that (1) mainly three types of drivers (f, d, and K) contributed negatively to the carbon intensity of commercial buildings from 2000 to 2015, and the comprehensive effects were quantified as the intensity values of CACCB. The CACCB values in the three Five-Year Plan periods were 383.41 (2001–2005), 591.09 (2006–2010), and 621.54 MtCO2 (2011–2015). (2) A comparative analysis of the contribution rate elasticity of the drivers assessed by the LMDI method and ridge regression effectively examined the robustness of the CACCB measurement model. Meanwhile, the performance of the measurement model was also evaluated. (3) More significant CACCB effects observed in recent years can be attributed to significant improvements made in ECW. To sum up, we believe that our approach covers the research gap of CACCB measurement, and our efforts constitute significant guidance for developing future ECW in China's commercial building sector.

In this paper, we model three layers of transportation disruption – first electrification, then autonomy, and finally sharing and pooling – in order to project transportation electricity demand and greenhouse gas emissions in the United States to 2050. Using an expanded kaya identity framework, we model vehicle stock, energy intensity, and vehicle miles traveled, progressively considering the effects of each of these three disruptions. We find that electricity use from light duty vehicle transport will likely be in the 570–1140 TWh range, 13–26%, respectively, of total electricity demand in 2050. Depending on the pace at which the electric sector decarbonizes, this increase in electric demand could correspond to a decrease in LDV greenhouse gas emissions of up to 80%. In the near term, rapid and complete transport electrification with a carbon-free grid should remain the cornerstones of transport decarbonization policy. However, long-term policy should also aim to mitigate autonomous vehicles’ potential to increase driving mileage, urban and suburban sprawl, and traffic congestion while incentivizing potential energy efficiency improvements through both better system management and the lightweighting of an accident-free vehicle fleet.

The disparity of carbon dioxide emission abatement (CDEA) has been impeding more balanced improvement of energy efficiency worldwide. Energy service companies (ESCOs), which are present to varying degrees, have come to play an increasingly key role in reducing CO2 emission via energy efficiency retrofit projects. China, as the main contributor to carbon dioxide (CO2) emissions, has been promoting ESCOs for decades. This study aims to identify the factors influencing the spatial differences for CDEA and assess the provincial-level potentials for reducing CO2 emissions through ESCOs. Data from 3225 ESCO projects in Mainland China spanning from 2011 to 2015 were examined to map out the spatial patterns of CDEA using local Moran’s I index and local indicator spatial autocorrelation. Influencing factors were identified through spatial analysis based on Kaya identity and ordinary least squares. The results indicate that the national progress of CDEA can be attributed to the high efficiency of ESCO projects in coastal and northern China. It was observed that population, consumption of coal, and research & development input have a positive influence on CDEA, whereas per-capita GDP, energy industry investment, and industry value added have an inhibitory effect on CDEA.

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 (LMDI). As an application example of the carbon dioxide 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, and it is not significantly correlated to the carbon intensity, the energy intensity, and the cost-to-sales ratio.

The Internet Plus concept has deeply affected the development mode of the industrial system. To achieve a low-carbon transition, it is essential to examine how Internet Plus effects the industrial system's carbon intensity. In the existing literature, few studies focus on the influence of Internet usage on the low-carbon development of industries. Based on the Kaya identity, this study explores the relationship and interaction mechanism between Internet Plus, output structure, energy intensity, carbon density, and carbon intensity involved in industrial systems. Based on threshold and dynamic panel models, the Internet effect on carbon intensity, energy intensity, and carbon density of China's industrial system from 2000 to 2015 is studied empirically. Results indicate that the Internet encourages decreased carbon intensity, energy intensity, and carbon density of China's industrial system to various degrees. Specifically, regarding its contribution, the Internet's reducing effect on energy intensity is obvious, whereas that on carbon density is less notable. Further, different threshold effects of the Internet are exerted on carbon intensity, energy intensity, and carbon density.

Recovering the landfill gas from municipal solid wastes for power generation is one feasible approach to respond both climate change and resource depletion issues. Under such a circumstance, it is important to evaluate the overall performance of its production process. This study proposes an integrated evaluation method by integrating emergy analysis and eco-indicator 99 method. Key impact factors influencing the sustainability are uncovered by adopting Kaya identity and index decomposition analysis (IDA). One MSW treatment plant in Yunnan province of China was selected as one case study site. The results indicate that the overall performance of the MSW treatment plant has been improved after operating a power generation system. The emissions' impact has been reduced by 56.85% and 2.44gCO2eq has been offset when generating 109sej of electricity. However, the low economic benefit, the increase of nonrenewable empower density and the low investment effectiveness are still key factors that hinder its sustainability. In order to address these issues, several suggestions are raised by considering the local realities.

The impact of energy use on the planet due to related CO2 emissions is continuously increasing, despite the adoption of efficiency and decarbonisation policies and widespread environmental awareness. Climate change mitigation will only succeed if the driving forces of consumption and emissions are deeply analysed, and effective means are provided to reverse their trends. To this aim, the Kaya Identity framework is revisited to classify indicators and decomposition studies in the literature. A comprehensive pyramid approach is proposed for the progressive disaggregation and discussion of energy and emissions changes. The approach is applied to the OECD and non-OECD to provide meaningful regional analysis of past trends and future projections according to stated policy intentions. Results show that a hopeful change has already begun in the developed region due to a sustained decrease of the energy intensity and a promising reduction of the carbon intensity. Emerging economies follow the performance of developed nations since 2013, held back by later economic development. Activity slowdown, energy conservation, renewable electrification, efficient power plants and coal phase out appear as the keystones for decarbonisation. As a result, emissions stabilisation could have already been achieved as rises in emerging countries are offset by drops in developed nations. However, more stringent climate policies, especially targeting carbon drivers, are urgently needed to enable emissions reductions compatible with a global temperature increase of 1.5°C.

In December 2015, China joined 190 plus nations at Paris in committing to the goal of limiting the rise in global average temperature to ‘well below’ 2°C. Carbon budget analysis indicates that goal...

According to China's Economic and Social Development Statistical Bulletin of 2010 , the energy intensive industries include six highest energy intensive sub-industries. Because China is still in the process of urbanization and industrialization, it requires the products of energy intensive industries. In this study, we review the main method of doing decomposition analysis on CO 2 emissions, investigate the main factors affecting CO 2 emissions in China's energy intensive industries using Kaya identity and Logarithmic Mean Divisia Index (LMDI) method and then adopt cointegration theory to construct the long-term relationship among CO 2 emissions and the main factors. Finally, we estimate the reduction potential of CO 2 emissions in China's energy intensive industries in the future. The results show that industrial scale and labor productivity are the main factors increasing CO 2 emissions while energy intensity is negative to emissions. If moderate measures are taken, the emission reduction potential will be 1.98 billion tons and 9.34 billion tons in 2020 and 2030 respectively. If aggressive measures are taken, the emission reduction potential will be 3.33 billion tons and 14.12 billion tons in 2020 and 2030 respectively. The results indicate that emission reduction potentials are substantial. Some related policy suggestions are proposed to support emissions reduction.

To reduce carbon emissions effectively in Shandong province in New Normal economy, this paper presents a new way to reflect the situation of carbon emissions accurately from both time-perspective and spatial perspective. The characteristics of carbon emissions in Shandong province are presented during 2005–2014, and this paper explores the spatial autocorrelation of carbon emissions branches in 17 cities. The results show that the total amount of carbon emissions in Shandong province presented a steady growth, whereas the growth rate was low, and the proportion of coal consumption in energy structure was significantly higher than the national average. In addition, the spatial pattern of the carbon emissions in 17 cities presented obvious regionally distinction. Furthermore, the affecting factors governing carbon emissions are decomposed based on the Kaya identity. The results reveal that economic scale was the key factor to increase the carbon emissions, whereas energy efficiency was the main factor of carbon emissions reduction. Therefore, this paper draws government should promote carbon emissions reduction given priority to some regions virtually, while encouraging emission reduction in others.

Today, most global economic production depends on energy produced from burning fossil fuels, which emit carbon dioxide as a byproduct. Although the costs of carbon-free energy such as wind and solar have come down dramatically over recent decades, there are substantial challenges to completely decarbonizing our electricity system, and even greater challenges to completely decarbonizing the transportation and industrial sectors (1). Thus, economic activity is projected to produce greenhouse gas emissions throughout this century. These emissions of greenhouse gases are causing Earth to warm, and, in aggregate, the effects of global warming are expected to be deleterious. These deleterious effects are expected to harm global welfare and diminish economic productivity. This diminution of production, other things being equal, would lead to a reduction in greenhouse gas emissions and, thus, would lessen the anticipated warming. In PNAS, Woodard et al. (2) find that the reduction in emissions through damage to economic activity is roughly the same magnitude as, but opposite in sign to, natural carbon cycle feedbacks that are projected to increase carbon dioxide levels relative to a world without carbon–climate feedbacks. The net effect of the socioeconomic carbon–climate feedbacks is estimated to be about the same magnitude as, but opposite in sign to, natural biogeophysical carbon–climate feedbacks. As a result, the level of greenhouse gases in the atmosphere in 2100 is projected to be about the same as if neither feedback (socioeconomic or natural biogeophysical) were active. Woodard et al. (2) analyze various influences on carbon dioxide emissions using the Kaya identity, which represents these emissions as the product of population, per-capita productivity, energy used per unit of production [energy intensity of gross domestic production (GDP)], and carbon emitted per unit of energy used (carbon intensity of energy). Reduced per-capita economic productivity due to climate change is projected to be … [↵][1]1To whom correspondence should be addressed. Email: kcaldeira{at}carnegiescience.edu. [1]: #xref-corresp-1-1

The research focuses on evaluating the impact of large manufacturing enterprises both on country's economy and on environment from the perspective of energy intensity. The case study is based on the situation in Latvia. The research uses the Kaya identity to show the situation that is necessary to obtain in order to achieve the carbon emissions goals of the European Union. The research also provides an analysis on coincidences between Latvia's GDP, energy intensity and carbon intensity, as well as studies the input of the largest industries in Latvia on the country's energy intensity and provides suggestions for improving the manufacturing industry from the perspective of energy savings.

This paper had two main purposes. One was to estimate annual total aviation CO2 emissions from/among all key urban agglomerations (UAs) in China and its changes patterns from 2007 to 2014. The second one was to visualize the aviation carbon footprints among the UAs by using a chord diagram plot. This study also used Kaya identity to decompose the contribution of potential driving forces behind the aviation CO2 emissions using Kaya identity. Especially, it decomposed factor CO2/gross domestic product (GDP), which is wildly used in Kaya identity analysis, into factor CO2/value-added (VA) and factor VA/GDP. Here, VA represents the tourism value added of the corresponding flights. The main results were: (1) The UAs developed a much bigger and stronger carbon network among themselves. (2) There was also an expanding of the flows to less densely populated or less developed UAs. However, the regional disparity increased significantly. (3) Compared with the driving factor of population, the GDP per capita impacted the emission amount more significantly. Our contribution had two folds. First, it advances current knowledge by fulfilling the research gap between transport emissions and UA relationship. Second, it provides a new approach to visualizing the aviation carbon footprints as well as the relationships among UAs.

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003-2005), the 11th five-year plan period (2005-2010), and the 12th five-year plan period (2010-2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

Decoupling the trends of energy-related carbon emissions from economic growth is an essential precondition and condition for cost-effective climate stabilization. Using an extended Kaya identity and the Tapio decoupling definitions, this study explored the role and magnitude of drivers associated with carbon emissions and economic growth decoupling across 134 countries. The retrospective (1995–2015) decomposition analysis was complemented by exploring the future decoupling projections using assumptions of the Shared Socio-economic Pathways (SSP) framework. The results indicate that less mature developing economies are carbonizing their energy systems at faster rates (about 1–4%/year) compared to developed economies. While emerging developing economies are progressively transitioning towards weak decoupling. Overall the deterioration of structural and intensity technology drivers, including conversion efficiency, emission factor, and fossil fuel mix, are the major pinch points to achieving absolute decoupling among developing economies. The prospective analysis of the SSP assumptions also indicate that - developing economies exhibit very minimal signs of decoupling and decarbonization relative to developed economies. The study provides instructive insights about the urgency of addressing global decarbonization, especially among developing economies.

This paper aims to complete and complement the information available on the past trends of thermal energy consumption in residential buildings and its drivers for Europe, as a continent and for the different countries. This paper follows the drivers identified in a previous one for the heating and cooling energy consumption, decomposing this energy demand into key drivers based on a Kaya identity approach: number of households, persons per household, floor space per capita and specific energy consumption for residential heating and cooling. Results show that all drivers did follow a consistent trend at global, regional and country level during the studied period of time, but the heating and cooling energy consumption did not follow the same trend if it was considered at global, regional or country level, showing that the energy consumption is very much influenced by all its drivers and does not follow the same trend as the specific energy consumption. Moreover, similar trends for each indicator can be found when evaluated at country or regional level, therefore as expected when aggregating data trends can be seen but details and particularities (both for the different countries and for the different studied years) are lost. The work is partially funded by the Spanish government (ENE2015-64117-C5-1-R (MINECO/FEDER)). The authors would like to thank the Catalan Government for the quality accreditation given to their research group GREA (2014 SGR 123) and research group DIOPMA (2014 SGR 1543). Dr. Camila Barreneche would like to thank Ministerio de Economia y Competitividad de España for Grant Juan de la Cierva, FJCI-2014-22886.

The Kaya identity relates the greenhouse gas (GHG) emissions to energy use, economic growth and population growth. Therefore, it can be applied to decompose GHG emissions with respect to the latter terms. European Union (EU) has established the main targets of sustainable energy development in Energy and Climate package 20–20–20. EU member states conformed to achieve in 2020 GHG emission reduction by 20% comparing with year 1990 level, to achieve 20% of renewable energy sources (RES) in final energy consumption and to reduce energy intensity by 20% compared to year 2005 level. As all of these targets are interrelated and have to be achieved together, it is very important to track the changes of GHG emissions in EU member states alongside the changes in energy intensity and increase in the share of renewables in final energy consumption. The Kaya identity and decomposition analysis allow the linking of changes in GHG emissions to changes in energy intensity and carbon intensity of energy which directly reflects the increase of the share of renewables in final energy consumption. The impact of GDP growth on GHG emissions is also assessed by Kaya identity. The paper applies a novel approach in tracking progress achieved in implementing 20–20–20 targets by employing Kaya identity and Shapley value for decomposition analysis. The conducted analysis showed that policies to increase energy efficiency are the most important drivers of GHG emission reduction and achieving EU 20–20–20 targets in Baltic States. Though the share of RES has increased significantly in Baltic States its impact on GHG emissions reduction per capita in Baltic States was insignificant therefore more emphasis on energy efficiency policies is necessary as they are the most effective measures for achieving GHG emission reduction targets.

Since 1970, global agricultural production has more than doubled with agriculture and land-use change now responsible for ∼1/4 of greenhouse gas emissions from human activities. Yet, while greenhouse gas (GHG) emissions per unit of agricultural product have been reduced at a global level, trends in world regions have been quantified less thoroughly. The KPI (Kaya-Porter Identity) is a novel framework for analysing trends in agricultural production and land-use change and related GHG emissions. We apply this to assess trends and differences in nine world regions over the period 1970–2007. We use a deconstructed analysis of emissions from the mix of multiple sources, and show how each is changing in terms of absolute emissions on a per area and per produced unit basis, and how the change of emissions from each source contributes to the change in total emissions over time. The doubling of global agricultural production has mainly been delivered by developing and transitional countries, and this has been mirrored by increased GHG emissions. The decoupling of emissions from production shows vast regional differences. Our estimates show that emissions per unit crop (as kg CO2-equivalents per Giga Joule crop product), in Oceania, have been reduced by 94% from 1093 to 69; in Central & South America by 57% from 849 to 362; in sub-Saharan Africa by 27% from 421 to 309, and in Europe by 56% from 86 to 38. Emissions per unit livestock (as kg CO2-eq. GJ−1 livestock product) have reduced; in sub-Saharan Africa by 24% from 6001 to 4580; in Central & South America by 61% from 3742 to 1448; in Central & Eastern Asia by 82% from 3,205 to 591, and; in North America by 28% from 878 to 632. In general, intensive and industrialised systems show the lowest emissions per unit of agricultural production.

One major drawback of climate change agreements including the recent 21st Conference of the Parties is focusing mainly on countries or regions with very high contribution to global emission rise but neglecting potential emitters. With Sub-Saharan Africa (SSA) been one such potential region, this paper applies the extended Kaya Identity Decomposition to investigate the dynamic impact of energy-related driving factors on carbon dioxide (CO2) emissions and its future trend in SSA. Employing the Autoregressive distributed lag model analysis for the period 1980 to 2014 reveals carbon intensity and “transition from biomass to fossil fuel energy” as the major long run and short run drivers of CO2 emissions increase respectively while “potential of penetration of clean energy in the energy consumption mix” is the only driver of both short and long run CO2 emissions decrease. Energy intensity and per capita GDP (supported by substantial household self-consumption of fuel) weakly and positively impact CO2 emissions in SSA. The dynamic forecasting analysis reveals possible low carbon energy path towards 2030. We propose transport sector energy efficiency improvement, use of natural gas and renewables to improve the region’s high energy poverty, improvement of household-self consumption paying attention to cleaner and safer energy and others.

Nowadays, India is the third-largest CO2 emitter and energy consumer in the world, and, it is soon expected to surpass China as the most populated country. Therefore, it is of great interest to analyse how India is developing its energy transition to a lower-carbon economy. This work analyses the evolution of the main driving forces of CO2 emissions in India during the period 1990-2016 through the use of an enlarged version of the Kaya identity, which establishes a link between CO2 emissions, types of energy sources (16), size of the economic sectors (3) and value of the Gross Domestic Product. India's CO2 emissions increased by 276% in the period under study, due to the rapid economic growth of India, which has been the dominating driving force contributing to the increase in CO2 emissions by 241%, while the energy intensity has been the main one reducing them by approximately -47%. So far, the use of coal has supported the rapid economic growth and the contribution of renewable energy, although significant, is still short compared to the total amount of energy employed. Remarkably, the estimated value of the emission intensity for 2020 supposes a 26% reduction concerning the value in 2005. According to this result, India is on the right pathway to fulfil its Nationally Determined Contribution but not to reduce its net CO2 emissions.

Electric power plays an important role in the economic development of a nation and in improving human life. Since China is the world’s largest electricity consumer, an understanding of the factors influencing the growth in the consumption of electricity would help Chinese leaders design appropriate energy savings strategies and economic policies. Using the Logarithmic Mean Divisia Index (LMDI) method based on the Kaya identity, this study takes China’s non-residential power consumption from 2007 to 2016 as its research subject and decomposes the influencing factors into population size, economic development, regional economic structure, regional industrial structure, and electricity consumption intensity. We find that economic growth is the main factor influencing the growth of non-residential power consumption, mostly in the eastern region; moreover, the increased intensity of power consumption inhibits the growth of non-residential power consumption, with the suppression effect being more significant in the secondary industry. Population growth plays a small role in promoting electricity consumption. Regional economic structure has a negative effect on power consumption, showing a significant difference between provinces. From these results, regional differences and industrial structure are factors to be considered for the development and management of electricity. Therefore, technological innovation and management need to be strengthened, with focus on industrial structure adjustment and promotion of energy conservation technology in the central and western regions.

Given the rapid industrialization and urbanization of China, environmental problems have gradually become major constraints that hinder its sustainable economic development. Moreover, China's pollution abatement and reduction of greenhouse gas emissions have been severely affected by pressures coming from domestic environmental appeals and international environmental diplomacy. By using integrated data from the Chinese Industrial Enterprise and the Chinese Enterprise Environmental Survey and Reporting databases, this study constructs comprehensive indicators of pollutant discharge intensity and carbon emissions index at the enterprise level and uses the panel fixed effect model, Kaya identity, and mediation effect model to assess the effects of environmental regulations on pollution abatement and collaborative emissions reduction from the micro-perspective. Results show that these regulations can abate the pollution emissions of Chinese industrial enterprises and verify the effectiveness of environmental policies. These regulations can also efficiently reduce the carbon dioxide emissions of enterprises through pollution abatement. In other words, environmental regulations facilitate a collaborative emissions reduction of pollutant and greenhouse gas emissions from enterprises. Such collaborative emissions reduction effect is also influenced by the energy structure and consumption of enterprises. This paper presents empirical evidence and policy basis for further improving China's environmental regulation policy system and achieving coordinated progress in China's economic development and environmental governance.

Anthropogenic carbon emission gives rise to a situation where global warming is becoming serious. China is paying for reducing carbon emissions. The concept of carbon curse suggests that countries rich in fossil fuels tend to be closely linked to high carbon emissions, but this is not absolute, which reminds policymakers that the policies implemented are positivelycorrelateswith carbon emission reduction. This study is also aimed at this, hoping to provide some proposals about reducing CO2 emissions to policy-makers by decomposing and analyzing the important factors. To achieve this target, this paper employs the extended the Kaya identity, combines the LMDI method to analyze the impact factors of carbon emissions in China from 1996 to 2016 and discusses the effects and causes of each factor according to the actual situation. It is found that the economic activity is the greatest driving force to promote carbon emissions, while on the contrary, energy intensity is the biggest suppressor. Optimizing industrial structure, improving the structure of energy and export-import trade and intensifying the development of clean energy can effectively restrain the growth of carbon emissions. In addition, the relative innovation point in this study is to analyze carbon emissions with the combination of electricity trading and discusses that increasing imported electricity is also a strategy to reduce carbon emissions.

Carbon dioxide emissions in developing countries are closely tied to their economy and play a crucial role in the world’s future emissions. In this paper, we put forward an alternative energy use paradigm shift of low-carbon emissions from operational, governance, institutional, and cultural viewpoints (OGIC). An urbanization factor is introduced into the Kaya identity, and three simulations are conducted to forecast the carbon footprint and to explore the effects of the energy use paradigm shift policy. The simulation results show that, in the context of the energy use paradigm shift, the years 2015 and 2024 are the two inflection points that separate the carbon footprint into three periods of extensive consumption (2000–2015), early energy transition (2016–2023), and late energy transition (2024–2030). Overall, the peak carbon emission value is forecasted to appear during the third stage. The findings are expected to demonstrate the effects of the energy use paradigm shift on carbon emissions and assist policy makers formulate a scientific policy framework for low carbon development.

Highlights • GFI model is used to analyze the factors of transportation carbon emissions in Beijing. • Economic development, energy intensity and population size are positive drivers. • The effect of output value of per unit traffic turnover appears as a flat “M”. • Transportation intensity is the main inhibition factor. • Energy structure has little effect on transportation carbon emissions., In the process of rapid development and urbanization in Beijing, identifying the potential factors of carbon emissions in the transportation sector is an important prerequisite to controlling carbon emissions. Based on the expanded Kaya identity, we built a multivariate generalized Fisher index (GFI) decomposition model to measure the influence of the energy structure, energy intensity, output value of per unit traffic turnover, transportation intensity, economic growth and population size on carbon emissions from 1995 to 2012 in the transportation sector of Beijing. Compared to most methods used in previous studies, the GFI model possesses the advantage of eliminating decomposition residuals, which enables it to display better decomposition characteristics (Ang et al., 2004). The results show: (i) The primary positive drivers of carbon emissions in the transportation sector include the economic growth, energy intensity and population size. The cumulative contribution of economic growth to transportation carbon emissions reaches 334.5%. (ii) The negative drivers are the transportation intensity and energy structure, while the transportation intensity is the main factor that restrains transportation carbon emissions. The energy structure displays a certain inhibition effect, but its inhibition is not obvious. (iii) The contribution rate of the output value of per unit traffic turnover on transportation carbon emissions appears as a flat “M”. To suppress the growth of carbon emissions in transportation further, the government of Beijing should take the measures of promoting the development of new energy vehicles, limiting private vehicles’ increase and promoting public transportation, evacuating non-core functions of Beijing and continuingly controlling population size.

Within the general context of Greenhouse Gas (GHG) emissions reduction, decomposition analysis allows the quantification of the contribution of different factors to changes in emissions as well as the assessment of the effectiveness of policy and technology measures. The Kaya identity has been widely used for that purpose in order to disaggregate carbon emissions into various driving forces. In this paper, it is applied for the analysis of emissions resulting from energy use at three different scales. First, a decomposition analysis of the carbon emissions for the complete Swiss energy system is presented using the future projections from the Swiss Energy Strategy 2050. The Kaya identity is then applied to the Swiss building sector after it is adapted with factors that are more relatable to building parameters, such as floor area instead of Gross Domestic Product (GDP). Finally, the last level of analysis is a small scale community energy system for a unique Swiss village that aims to significantly reduce its emissions. An energy strategy is developed and its effectiveness is assessed with the adapted Kaya identity and benchmarked against the Swiss average values. The presented method demonstrates how the performance of buildings under various retrofitting scenarios can be benchmarked against future emission targets.

The paper analyses possibilities for CO 2 emission reduction into the centralized district heating system by using the index decomposition analysis: Kaya identity equation. The classical Kaya equation is complemented with an energy efficiency indicator of a district heating system. The paper focuses on the impact of the Kaya equation components on the CO 2 emission reduction. The elaborated methodology is tested in order to analyze possibilities for CO 2 emission reduction at district heating systems in Latvia. 3 scenarios were formed for the forecast of the CO 2 emission reduction. In case the current model for the state development is continued (scenario1) CO 2 emissions will be reduced for 13%; however, in the scenario of orderly and balanced DH systems they will reduce for 29% by 2020 as compared to 2012.

An extended version of the IPAT model and the ‘Kaya identity’ is used to assess the contribution of drivers of CO2 emissions for the 1995–2009 period. The paper carries out a multisector analysis based on the Log-Mean Divisia Index Method (LMDI I). The decomposition factors used are the Carbon Intensity factor (CI), the Energy Intensity factor (EI), the structural composition of Spain׳s economy (Economy Structure, ES), the Economic Activity factor (EA) and Population (P), respectively. Data came from the World Input–Output Database (WIOD) and determined the period under consideration. The paper focuses on the 35 productive sectors included in the WIOD. Major findings show that RES acted in detriment to the drivers of CO2 emissions. This may be stated for the last few years under consideration. The positive trend for the share of RES in Spain׳s energy matrix, together with the negative tendency in the use of fossil fuels, leads us to be optimistic.