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Rural residents have unique lifestyle characteristics, energy consumption methods, energy-saving behaviors, and awareness. And the direct carbon emission from rural residents is based on the combined effect of multiple factors. In order to address the complexity of factors affecting the direct carbon emissions from rural household, this study used a structural equation model to examine the effect of multi-factor variables on direct carbon emissions from rural households in central China. Data were collected using questionnaires and surveys in six cities in central China to reflect the daily reality of rural residents. The results show that quality of life and awareness of energy conservation can affect the direct carbon emissions of rural residents. Family characteristics and awareness of energy conservation affected carbon emissions indirectly by affecting the daily behaviors and quality of life of the residents; consumption characteristics, energy-saving behaviors, and energy conservation policies are not the main factors contributing to the direct carbon emissions of the residents. Based on the results, future studies can focus on energy conservation education, improvement of the living habits, coal energy use efficiency, and energy consumption structure of residences.

The paper summarizes results of the China Energy Modeling Forum's (CEMF) first study. Carbon emissions peaking scenarios, consistent with China's Paris commitment, have been simulated with seven national and industry-level energy models and compared. The CO2 emission trends in the considered scenarios peak from 2015 to 2030 at the level of 9–11 Gt. Sector-level analysis suggests that total emissions pathways before 2030 will be determined mainly by dynamics of emissions in the electric power industry and transportation sector. Both sectors will experience significant increase in demand, but have low-carbon alternative options for development. Based on a side-by-side comparison of modeling input and results, conclusions have been drawn regarding the sources of emissions projections differences, which include data, views on economic perspectives, or models' structure and theoretical framework. Some suggestions have been made regarding energy models' development priorities for further research. Keywords: Carbon emissions projections, Climate change, CO2 emissions peak, China's Paris commitment, Top-Down energy models, Bottom-Up energy models, Multi model comparative study, China Energy Modeling Forum (CEMF)

This paper analyzes Chinese household CO2 emissions in 1994–2012 based on the Logarithmic Mean Divisia Index (LMDI) structure decomposition model, and discusses the relationship between household CO2 emissions and economic growth based on a decoupling indicator. The results show that in 1994–2012, household CO2 emissions grew in general and displayed an accelerated growth trend during the early 21st century. Economic growth leading to an increase in energy consumption is the main driving factor of CO2 emission growth (an increase of 1.078 Gt CO2) with cumulative contribution rate of 55.92%, while the decline in energy intensity is the main cause of CO2 emission growth inhibition (0.723 Gt CO2 emission reduction) with cumulative contribution rate of 38.27%. Meanwhile, household CO2 emissions are in a weak state of decoupling in general. The change in CO2 emissions caused by population and economic growth shows a weak decoupling and expansive decoupling state, respectively. The CO2 emission change caused by energy intensity is in a state of strong decoupling, and the change caused by energy consumption structure fluctuates between a weak and a strong decoupling state.

To achieve the goals of national sustainable development, the peaking control of CO2 emissions is pivotal, as well as other pollutants. In this paper, we build a Chinese inter-regional CGE model and simulate 13 policies and their combinations. By analyzing the energy consumptions, coal consumptions, relating emissions and their impacts on GDP, we found that with the structure adjustment policy, the proportion of coal in primary fossil fuels in 2030 will decrease from 53% to 48% and CO2 emissions will decrease by 11.3%–22.8% compared to the baseline scenario. With the energy intensity reduction policy, CO2 emissions will decrease by 33.3% in 2030 and 47.8% in 2050 than baseline scenario. Other pollutants will also be controlled as synergetic effects. In this study we also find that although the earlier the peaking time the better for emission amounts control, the economic costs can not be ignored. The GDP will decrease by 2.96%–8.23% under different scenarios. Therefore, integrated policy solutions are needed for realizing the peaks package and more targeted measures are required to achieve the peaks of other pollutants earlier.