Study On Approaches To Energy- Carbon Emissions Modeling Under The Backgroud Of Urbanizationand Carbon Reduction Target

Taking global warming as the main characteristics, climate change increasingly becomes a common concern of countries round the world. The problems of global warming and carbon emissions caused by energy consumption become increasingly prominent. Focused on the background of urbanization and carbon reduction target, this dissertation analyzed deeply two urgent aspects in the field of energy consumption and carbon emissions. On the one hand, under the accelerated urbanization process in China, residential sector will be a key field of energy consumption and carbon emissions growth in the future. Therefore, it was necessary that not only to analyze the impact factors of household emissions, but also to predict the future trend of household emissions. On the other hand, in response to growing pressure of carbon reduction, it was in badly need of in-depth analysis on the future trend of regional carbon emissions, carbon emissions peak and regulation mechanism, in order to formulate the development path.We focus on modeling and applications related to the following aspects: energy consumption and carbon emission in residential sector, carbon emissions peak and its regulation mechanism in order to extend and improve research thinking and model methods in the field of energy consumption and carbon emissions. Meanwhile, we intend to make up the lack or missing of related research.Under the background of urbanization, the analysis of modeling and applications on energy consumption and carbon emission in residential sector mainly includes two following aspects:(1) Based on statistical data from 1993 to 2013 in China, we accounted household energy consumption and carbon emissions including direct and indirect consumption. Then we built LMDI model to disassemble household energy consumption and carbon emissions.The results showed that economic growth and population were positively to drive effect on household energy consumption and carbon emissions. Consumer spending was fluctuant negative effect, and household energy intensity effect declined on the whole research stage. Energy emissions drive effect was unstable. Further, we applied Monte Carlo model for dynamic simulation ofthe household carbon emissions in 2030. The results showed that the largest probability of household carbon emissions was 6.253 billion tons, and cumulative probability of carbon emissions more than 75% was between 5.558 to 7.389 billion tons. This dissertation gives attention to the dynamic simulation and prediction of household carbon emissions to make up for the lack of related research.(2) We combined threshold model with STIRPAT model to get the threshold-extended STIRPAT model. Urbanization rate was treated as the threshold variable. The energy structure, citizen consumption rate and industrial structure were regarded as explaining variables. In this dissertation, threshold-extended STIRPAT model was built from various perspectives. The data of 30 areas in China from 1995-2012(provinces, municipalities directly under the central government and autonomous regions) was used to calculate the threshold values based on the urbanization rate. We could conclude the effect differences of household carbon emissions at different urbanization levels. The research indicate when the urbanization rate is close to the threshold values(0.250, 0.325 and 0.457), the influence on household carbon emissions show periodical changing. When the urbanization rate is lower than 0.250, it shows negative elasticity relation between household carbon emissions and the factors(energy structure, domestic comsupation rate and industrial structure). More specifically, they are-0.688,-0.570 and-0.570 respectively. When the urbanization rate is lower than 0.457, the negative elasticity relation still exist and weaken significantly. When the urbanztion rate is over 0.457, the relation will gradually turnning into positive elasticity relation.Under the background of carbon reduction target, the analysis of modeling and applications on regional carbon emissions peak and its regulation mechanism mainly includes three following aspects:(3) Based on extended-STIRPAT model, we took the stage between carbon intensity peak and per capita carbon emissions pre-peak as the research stage. Denmark and Ireland were the research objects. We deeply studied on the influence on carbon emissions. The result shows that the impact of various factors on carbon emissions and per capita carbon emissions were similar, but there are significant impact differences among impact factors. Per capita GDP was the most important driving force. Besides, scale effect of urbanization would appear untill urbanization reaches a high level. The result also shows that carbon transfer of international trade export was more significant gradually. Denmark was benefit from international trade export due to the trade deficit in recent years.The decrease of carbon intensity appears more significant impact for reducing carbon emissions.(4) Based on the study of chapter 5, we intend to further explore the impact of macro factors on regional carbon emissions peak. Taking Jilin province as an example, based on different stages of low-carbon development, we set four scenarios(Low-carbon Scenario, Energy-saving to Low-carbon Scenario, Energy-saving Scenario, and Business as Usual Scenario) to forecast carbon emissions based on extended-STIRPAT model. The carbon emissions peak of four scenarios were 264.0Mt、356.2Mt、430.0Mt and 477.3Mt, and the peak time were 2029, 2036, 2040, 2045. On this basis, we proceed with controllability study to discuss the impact of various factors on carbon emissions peak. The result shows that Population and urbanization rate only affect the peak value, but per capita GDP、carbon intensity and the proportion of secondary industry impact the peak value and time. Per capita GDP is the most significant factor, followed by the second industry proportion and carbon intensity, urbanization rate and population.(5) Based on the research in Chapter 6, we applied Long-range Energy Alternatives Planning System(LEAP) with scenario analysis to deeply analyze the effects on the carbon emissions peak caused by the adjustment of energy and industrial structures. The impact on the carbon emissions peak between independent and cooperative adjustment scenarios were compared in order to find out the regulatory mechanism on the carbon emissions peak and to obtain the optimal scenario for future development. The results indicated that the adjustment of industrial and energy structures could significantly lower the carbon emissions peak value without changing the carbon emissions peak-arrival time. The results also indicated that cooperative adjustment of energy and industrial structures could reduce CO2 emissions more significantly than independent adjustment. However the carbon reduction contributed by cooperative adjustment might be less than the sum of the carbon reduction contributed by corresponding independent adjustment with the same mode. Considering both the carbon emissions peak value and the reduction efficiency overall, SA III scenario was proved as the optimal scenario of future development in Jilin Province. Based on the results, we proposed some recommendations for optimal adjustment of energy and industrial structures to mitigate climate change.