This paper calculates per capita household carbon emissions (HCEs) based on IPCC’s reference approach and Input-output analysis (IOA) of different categories of carbon emissions in China from 1997 to 2012. Its driving factors are also analyzed with the Spatial Error Model (SEM) and the Spatial Lag Model (SLM). The main purpose of this work is to emphasize the characteristics of household carbon emissions based on temporal scale and spatial scale. The results show that: 1) Based on different carbon sources, HCEs can be divided into direct and indirect emissions; based on different human needs, HCEs can be classified as basic and development emissions; based on different consumers’ behaviors, HCEs can be divided into transportation, housing, food, goods and service emissions. 2) At the time scale, both direct and indirect per capita HCEs, basic and development per capita HCEs, and each item of per capita HCEs based on behaviors exhibit the increasing tendency. 3) From the spatial perspective, there is a common pattern in spatial distributions of per capita HCEs. The cluster effect of per capita HCEs is stable. 4) From the space point of view, the per capita HCEs in China shows a decreasing tendency from east to west in 2012. 5) Based on the spatial analysis model, the proportion of basic HCEs per capita in the whole is the main driving factor. Meanwhile, per capita income and per capita GDP are also affecting per capita HCEs. On the basis of analyzing the spatial-temporal patterns and driving factors of per capita household carbon emissions, we provide scientific evidences and put forward effective suggestions for carbon emissions reduction measures and policies.
With the rapid development of real estate market, the continued increasing housing price has become the focus of national macro-control. To stabilize the fast increasing housing prices, State Council stated a series of policies and measures which covers all aspects of land, finance and banking. In 2010, Ministry of Land and Resources issued a trial notice, state-owned construction land supply planning norms, guiding relevant departments to prepare for the land supply planning. Since then, the land supply planning has become an important tool of land supply policy to regulate and control the housing prices. The purpose of this paper is to explore the interrelation between land supply planning and housing prices, and the transmission mechanism of land supply planning on housing prices.In this paper, we use four-quadrant model which was firstly built by Denise Dipasquale and William C. Wheaton, and introduce a series of relevant variables such as the amount of land, the price of land, the housing stock, and so on. With the modified four-quadrant model, we analyze how land supply planning affects housing prices by conducting intermediate variables. Through theoretical analysis, the land supply structure and completion rate are two factors on housing prices. On the basis of theoretical analysis, we employ the proportion of affordable housing land to residential land and the actual land supply to the plan land supply to represent the land supply planning. In this paper, we use the panel data of 14 cities in Hunan Province from year 2010 to 2013. We also construct a stock-flow model in housing market to investigate the transmission mechanism of the land supply planning on housing prices. It turns out that the implementation of land supply planning affected the expectation of property developers which induced their speculative behaviors, and thus had a significant negative impact on housing prices. Seen from the regression result, the proportion of residential land use for indemnificatory housing and the implementation rate of the land supply plan both have significant negative effects on housing price while in different intensities. The effect of the land supply structure is greater than that of implementation rate of the plan. The smaller the proportion of land for indemnificatory housing or the higher implementation rate of the plan is, the faster housing price drops. This conclusion can provide important decision making reference for relevant departments to make scientific and feasible plan. The result shows that the expectation of developers has greater effect than the expectation of consumers, so it’s critical to take measures to regulate developer’s behavior. The study also shows that demand factors exert greater influence on housing prices than supply factors, especially per capita disposable income. Demand factors are the main factors that affect housing prices. And the housing stock of last year pushes the housing prices higher. This conclusion is different from Peng and Wheaton’s, which exactly reflects the contradiction of current vacant housing stock and increasing new housing supply.