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.

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.

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.

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.

This paper applied the Tapio and LMDI methods based on an extended Kaya identity, to evaluate the decoupling and factors that influenced the relationship between economic growth and CO2 emissions in Cameroon from 1990 to 2015. The analysis covered the two main sectors of Cameroon's economic activity, and the decoupling indicators were broken down into five factors. The results showed that Cameroon performed weak decoupling throughout this study period. A weak negative decoupling and a strong negative decoupling appeared during the periods 1990-1992 and 1993-1994, whereas a strong decoupling occurred only from 1994 to 1995 and between 2004 and 2006. From a general point of view, we found that demographic change, followed by energy intensity and economic activity, played a negative role in decoupling, while economic structure and emission factors had promoted the development of decoupling. A comparative analysis of the industrial and tertiary sectors revealed that industrial growth determined the degree of decoupling of CO2 emissions from Cameroon's economic growth. The policy will need to optimize and significantly adjust the energy and industrial structures if Cameroon's 2035 emission reduction targets are to be reached. This requires a change in Cameroon's energy consumption and economic development pathways for optimal outcomes.

Emissions of CO2 from the EU can be estimated using different system boundaries, depending on the policy question. We analyze and compare the trends in territorial emissions (1990–2016) and consumption-based emissions and emissions embodied in trade (1990–2014). We find the Global Financial Crisis (GFC) in 2008 was an important turning point. Territorial emissions were roughly stable in the years before the GFC but have since declined. Consumption-based emissions rose from 2000 to the GFC but then declined in concert with territorial emissions. A Kaya identity decomposition suggests that the main factor pushing the EU’s territorial emissions up before the GFC was a growth in GDP, balanced by constant improvements in energy and carbon intensity. The large increase in consumption-based emissions up to the GFC was mainly due to emissions from the production of imported manufactured goods, particularly from China. After the GFC, the Kaya identity decomposition suggests that lower GDP growth facilitated a sustained decrease in territorial emissions. The decline in consumption-based emissions since the GFC was partly due to decreasing territorial emissions but accelerated due to a decrease in the emissions from the production of imported products from China. Preliminary data indicates that EU CO2 emissions have increased from 2014 to 2017, with the Kaya identity decomposition suggesting the increase is due to a return to stronger GDP growth.

Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused–110.90 Mt CO2 emissions with a contribution rate of–43.94%, followed by the energy carbon structure effect resulting in–18.76 Mt CO2 emissions with a contribution rate of–7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.

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.

Regional carbon emissions research is necessary and helpful for China in realizing reduction targets. The LMDI I (Logarithmic Mean Divisia Index I) technique based on an extended Kaya identity was conducted to uncover the main five driving forces for energy-related carbon emissions in Xinjiang, an important energy base in China. Decomposition results show that the affluence effect and the population effect are the two most important contributors to increased carbon emissions. The energy intensity effect had a positive influence on carbon emissions during the pre-reform period, and then became the dominant factor in curbing carbon emissions after 1978. The renewable energy penetration effect and the emission coefficient effect showed important negative but relatively minor effects on carbon emissions. Based on the local realities, a comprehensive suite of mitigation policies are raised by considering all of these influencing factors. Mitigation policies will need to significantly reduce energy intensity and pay more attention to the regional economic development path. Fossil fuel substitution should be considered seriously. Renewable energy should be increased in the energy mix. All of these policy recommendations, if implemented by the central and local government, should make great contributions to energy saving and emission reduction in Xinjiang.

Discussion on the establishment of an ASEAN Community has intensified in the last 2 decades. The prospect for its establishment is seemingly inevitable with the signing of the so-called Bali Concord II. And so, analytical research on the economic challenges of this political decision is urgently needed. This study addresses issues of the ASEAN integration from an energy policy perspective. It strives to understand the challenges that will be faced by individual members when the ASEAN community is established. The study uses Kaya’s identity method of decomposition to analyze the energy patterns among ASEAN member countries by which energy consumption patterns can be examined, including energy intensity, energy sources, and sectoral energy demands. Analyzing these patterns is needed since a region-wide policy in the energy sector would be futile if it is implemented across the board without country-specific considerations. Cooperation in the energy sector requires great awareness and flexibility if it is to support the goal of an integrated ASEAN.

Energy is a key requirement for a healthy, productive life and a major driver of the emissions leading to an increasingly warm planet. The implications of a doubling and redoubling of per capita incomes over the remainder of this century for energy use are a critical input into understanding the magnitude of the carbon management problem. A substantial controversy about how the Special Report on Emissions Scenarios (SRES) measured income and the potential implications of how income was measured for long term levels of energy use is revisited again in the McKibbin, Pearce and Stegman article appearing elsewhere in this issue. The recent release of a new set of purchasing power estimates of national income, and the preparations for creating new scenarios to support the IPCC’s fifth assessment highlight the importance of the issues which have arisen surrounding income and energy use. Comparing the 1993 and 2005 ICP results on Purchasing Power Parity (PPP) based measures of income reveals that not only do the 2005 ICP estimates share the same issue of common growth rates for real income as measured by PPP and US $, but the lack of coherence in the estimates of PPP incomes, especially for developing countries raises yet another obstacle to resolving the best way to measure income. Further, the common use of an income term to mediate energy demand (as in the Kaya identity) obscures an underlying reality about per capita energy demands, leading to unreasonable estimates of the impact of changing income measures and of the recent high GDP growth rates in India and China. Significant new research is required to create both a reasonable set of GDP growth rates and long term levels of energy use.

This study analyzes the role of energy intensity improvement in the short term (to the year 2020) and midterm (to the year 2050) in the context of long-term greenhouse gases (GHG) stabilization scenarios. The data come from the latest Emissions Scenarios Database and were reviewed in the Fourth Assessment Report (AR4) by the Intergovernmental Panel on Climate Change. In this study, quantitative decomposition analyses using the extended Kaya identity are applied to the stabilization scenarios in Categories I to IV of Table SPM.5 in the AR4. Furthermore, quantitative decomposition analyses of Category IV scenarios are conducted for major GHG-emitting countries, such as the USA, Western Europe, China, and India, by utilizing the large number of reports in the database. This study provides in-depth analyses of the relationship between energy intensity improvement and other major indicators. One finding is that energy intensity improvement plays an important role in the short term, and the rate of energy intensity improvement is assumed to be around 2% per year as a median value across Categories I–III in the midterm on the global scale. However, achieving stringent stabilization levels requires various other measures regarding the use of less-carbon intensive fossil fuels, the shift to non-fossil fuel energies, and advanced technologies such as carbon capture and storage.

This article assesses emissions scenarios in the literature, originally documented in the scenario database that was developed more than 7 years ago. The original scenario assessment and literature review has been used, among other things, as the basis for the quantification of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) reference scenarios and the IPCC Third Assessment Report (TAR) stabilization scenarios. In the meantime, a large number of new emissions scenarios have been developed and published. We have collected the relevant information about these new scenarios with the objective to assess the more recent perspectives about future global emissions and to assess the changes in the perspectives about future emissions and their driving forces that may have occurred since the publication of SRES and TAR scenarios. Our analysis goes beyond mere comparisons of emissions ranges. In particular, we explore the underlying drivers of emissions using the so-called IPAT identity (Impacts are proportional to the product of Population, Affluence, and Technology). When IPAT analysis refers to carbon emissions it is called the Kaya identity, where carbon dioxide (CO2) emissions are assumed to correspond to the product of population, per capita income, energy intensity of gross domestic product (GDP), and CO2 intensity of energy. Comparing the recent scenario literature with the scenarios developed before TAR shows that there are strong similarities for the main underlying tendencies in many of the scenario’s driving forces and results.