Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

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 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ 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 1990–2000, 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 2001–2010, 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 2011–2016, 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’s economic structure and transportation’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. View Full-Tex

Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach

The industrial sector dominates the global energy consumption and carbon emissions in end use sectors, and it faces challenges in emission reductions to reach the Paris Agreement goals. This paper analyzes and quantifies the relationship between industrialization, energy systems, and carbon emissions. Firstly, it forecasts the global and regional industrialization trends under Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway2 (SSP2) scenarios. Then, it projects the global and regional energy consumption that aligns with the industrialization trend, and optimizes the global energy supply system using the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model for the industrial sector. Moreover, it develops an expanded Kaya identity to comprehensively investigate the drivers of industrial carbon emissions. In addition, it employs a Logarithmic Mean Divisia Index (LMDI) approach to track the historical contributions of various drivers of carbon emissions, as well as predictions into the future. This paper finds that economic development and population growth are the two largest drivers for historical industrial CO2 emissions, and that carbon intensity and industry energy intensity are the top two drivers for the decrease of future industrial CO2 emissions. Finally, it proposes three modes, i.e., clean supply, electrification, and energy efficiency for industrial emission reduction

Regional development and carbon emissions in China

China announced at the Paris Climate Change Conference in 2015 that the country would reach peak carbon emissions around 2030. Since then, widespread attention has been devoted to determining when and how this goal will be achieved. This study aims to explore the role of China’s changing regional development patterns in the achievement of this goal. This study uses the logarithmic mean Divisia index (LMDI) to estimate seven socioeconomic drivers of the changes in CO2 emissions in China since 2000. The results show that China’s carbon emissions have plateaued since 2012 mainly because of energy efficiency gains and structural upgrading (i.e., industrial structure, energy mix and regional structure). Regional structure, measured by provincial economic growth shares, has drastically reduced CO2 emissions since 2012. The effects of these drivers on emissions changes varied across regions due to their different regional development patterns. Industrial structure and energy mix resulted in emissions growth in some regions, but these two drivers led to emissions reduction at the national level. For example, industrial structure reduced China’s CO2 emissions by 1.0% from 2013-2016; however, it increased CO2 emissions in the Northeast and Northwest regions by 1.7% and 0.9%, respectively. By studying China’s plateauing CO2 emissions in the new normal stage at the regional level, it is recommended that regions cooperate to improve development patterns

Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes:A case study for Shanghai (China)

Although investment and R&D activities can exert significant effects on energy-related industrial CO2 emissions (EICE), related factors have not been fairly uncovered in the existing index decomposition studies. This paper extends the previous logarithmic mean Divisia index (LMDI) decomposition model by introducing three novel factors (R&D intensity, investment intensity, and R&D efficiency). The extended model not only considers the conventional drivers of EICE, but also reflects the microeconomic effects of investment and R&D behaviors on EICE. Furthermore, taking Shanghai as an example, which is the economic center and leading CO2 emitter in China, we use the extended model to decompose and explain EICE changes. Also, we incorporate renewable energy sources into the proposed model to carry out an alternative decomposition analysis at Shanghais entire industrial level. The results show that among conventional (macroeconomic) factors, expanding output scale is mainly responsible for the increase in EICE, and industrial structure adjustment is the most significant factor in mitigating EICE. Regardless of renewable energy sources, the emission-reduction effect of energy intensity focused on by the Chinese government is less than the expected due to the rebound effect, but the introduction of renewable energy sources intensifies its mitigating effect, partly resulting from the transmission from the abating effect of industrial structure adjustment. The effect of energy structure is the weakest. Although all the three novel factors exert significant effects on EICE, they are more sensitive to policy interventions than conventional factors. R&D intensity presents an obvious mitigating effect, while investment intensity and R&D efficiency display an overall promotion effect with some volatility. The introduction of renewable energy sources intensifies the promotion effect of R&D efficiency as a result of the "green paradox" effect. Finally, we propose that CO2 mitigation efforts should be made by considering both macroeconomic and microeconomic factors in order to achieve a desirable emission-reduction effect

Factor Decomposition Analysis of Industrial Wastewater Discharge: A Case of China’s Jiangxi Province

Industrial wastewater discharge is a serious problem for the environmental management of water in Jiangxi, China. In order to analyze the drivers of industrial wastewater discharge in 2004-2015, we used a Logarithmic Mean Divisia Index (LMDI) method to examine four effects including scale , structure , discharge intensity, and technology on the discharges. In 2004-2015, the wastewater discharge from Jiangxi’s industrial sectors increased by 2348.918 ×104 tons. The expansions of production scale and discharge intensity were the main factors leading to increases in industrial wastewater discharges, by1.392-fold and 1.028-fold, respectively. Improvements in abatement technology and adjustment of industrial structure was most important role in decreasing industrial wastewater discharge by 0.659-fold and 0.996-fold, respectively. The main industries that led to increased wastewater discharge during 2004-2015 were WC, MPNFMO, PFAP, Mte, MPPP, MRCMCP, MM, and MCCOEE. We showed that if control of wastewater discharge in Jiangxi’s industrial sectors is to be achieved in future, the industrial sectors should continue to rely on advanced technology to improve efficiency of water use in the short term, and should simultaneously limit expansion of energy-intensive and highly polluting industries. This may help accelerate restructuring and upgrading of the industrial sector in China in the long run. Keywords: decomposition factors, wastewater discharge, Jiangxi, industrial sectors, LMD

Multitemporal LMDI index decomposition analysis to explain the changes of ACI by the power sector in Latin America and the Caribbean between 1990-2017

This paper analyzes the drivers behind the changes of the Aggregate Carbon Intensity (ACI) of Latin America and the Caribbean (LAC) power sector in five periods between 1990 and 2017. Since 1990 the carbon intensity of the world has been reduced almost 8.8% whereas the carbon intensity of LAC countries only decreased 0.8%. Even though by 2017 the regional carbon intensity is very similar to the one observed by 1990, this index has showed high variability, mainly in the last three years when the ACI of LAC fell from 285 gCO2/kWh in 2015 to 257.7 gCO2/kWh. To understand what happened with the evolution of the carbon intensity in the region, in this paper a Logarithmic Mean Divisia for Index Decomposition Analysis (IDA-LMDI) is carried out to identify the accelerating and attenuating drivers of the ACI behavior along five periods. The proposal outperforms existing studies previously applied to LAC based upon a single period of analysis. Key contributions are introduced by considering the type of fuel used in power plants as well as specific time-series of energy flows and CO2 emissions by country. Results reveal structural reasons for the increase of the ACI in 1995-2003 and 2008-2015, and intensity reasons for the decrease of the ACI in 1990-1995, 2003-2008 and 2015-2017

The role of renewable energy sources as a compensating factor of CO2 emissions in Spain

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.Junta de Andalucía SEJ 132Ministerio de Economía y Competitividad ECO2014-56399-

A structural decomposition analysis of CO2 main drivers for the spanish economy

The aim of this paper is the analysis of structural decomposition of changes in CO2 emissions in Spain by using an enhanced Structural Decomposition Analysis (SDA) supported by detailed Input–Output tables from the World Input–Output Database (2013) (WIOD) for the period 1995–2009. The decomposition of changes in CO2 emissions at sectoral level are broken down into six effects: carbonization, energy intensity, technology, structural demand, consumption pattern and scale. The results are interesting, not only for researchers but also for utility companies and policy-makers as soon as past and current political mitigation measures are analyzed in line with such results. The results allow us to conclude that the implementation of the Kyoto Protocol together with European Directives related to the promotion of RES seem to have a positive impact on CO2 emissions trends in Spain. After reviewing the current mitigation measures in Spain, one policy recommendation is suggested to avoid the rebound effect and to enhance the fight against Climate Change that is tax benefits for those companies that prove reductions in their energy intensity ratios.Junta de Andalucía SEJ 132Ministerio de Economía y Competitividad ECO2014-56399-RMinisterio de Educación (Chile) 018/FONDECYT/1

Study on China's Regional Carbon Emission Factors: The case of Chongqing City

AbstractLow-carbon development has become a hot issue concerned by the whole world. Governments respectively introduced the country's low-carbon strategy and action. China has put forward a series of obligatory targets and has issued a provincial area “twelfth five-year” carbon intensity reduction target. How to coordinate the contradiction between economic growth and environmental constraints, and formulating corresponding low-carbon development path and the supporting measures, has become one of the problems to be solved. Considering the regional resource endowment, stage of economic development, energy structure, industrial structure, technical development level and other factors, this article constructs the model of regional carbon emission factors, the paper takes Chongqing of China as an example. The research results show that: The major contribution of elements, in turn, is the improvement of technology energy efficiency, the optimization of energy structure, and the adjustment of industrial structure. Based on this, this paper puts forward the corresponding low-carbon development policies according to the results of analysis, from the aspects such as energy structure, industrial structure, and technological progress and so on