government fiscal expenditure etc. Ma Li, Sun Jingshui (2008) 9） analyzed the relationship between China’s urban and rural residents’ consumption and income using the first- order the autoregressive model (FAR) of spatial econometrics, the spatial autoregressive model (SAR) and spatial error model (SEM) and found that China’s PCCE level had significant spatial correlation. There may be a high order correlation besides the first order correlation. Li Qihua (2011) 10） analyzed the relationship between urban and rural residents’ consumptionspatial correlation and the convergence of urban and rural residents’ consumption in China through spatial correlation in spatial econometrics. It was found out that the spatial correlation of urban and rural residents’ consumption in China increased year by year. The level of urban and rural residents’ consumption and various types of consumer had different Convergence and Divergence. In summary, the scholars mostly conducted academic researches on spatial econometrics. But, there is still much more room for improvement in the setting of urbanresidents in the consumer spatialeconometric model and spatial aspects of weight determination. Therefore, based on C-D function, and combined with different spatial weight matrix, this paper analyses China’s urbanresidents’ consumption level through a spatial data analysis model and the impact of space effect on consumption levels of urbanresidents, which has important practical significance and academic value.
Wen Qixiang and Ran Jingfei (2005) believe that the optimization of con- sumption structure is the process of transforming mainstream consumer de- mand from low to high level, which can be expressed in two forms. One is that original commodity projects will still be consumed, but the original consumer projects will develop to a higher level. Consumers have higher requirements on basic and traditional consumption, and pay more attention to the quality of food, clothing and other consumer goods. The other is that new and higher-level consumption items have been added, the composition and proportion of con- sumption have changed, and the consumption structure has become more and more advanced. After consumers meet their basic consumer needs and tradi- tional consumer demand, they will increase more emerging consumption and potential consumption . The data released by the National Bureau of Statis- tics confirms this view: from 1993 to 2016, food consumption expenditures have always been the largest proportion of urbanresidents’ consumption expendi- tures, and have been on a downward trend. Cultural and educational entertain- ment consumer spending, consumer spending and transport and communica- tions during a significant increase the proportion of consumer spending trends, the fastest rise in the proportion of consumer spending, up from 6.6% in 1993 to 22.2% in 2016, traffic and communication from 3.8% in 1993 to increase the proportion of consumer spending in 2016 to 13.8%, and the cultural and educa- tional entertainment consumer spending rose from 9.2% in 1993 to 11.4% in 2016. Although the medical consumption expenditure has a rising trend, the change range is small, while the proportion of the clothing consumption ex- penditure, the proportion of the household equipment and supplies consump- tion expenditure and the proportion of other consumption expenditure have a small decline trend during the period (Figure 1).
Literature abounds on how macroeconomic and financial variables interact with housing prices. For example, Cho (2011) investigates the effect of house price changes on consumption using household level data from Korea. Empirical results show that housing price do not affect total household consumption in Korea. According to the author, this neutral effect is explained by the fact that a positive wealth effect of homeowners, associated with the increase in home prices, is offset by a negative wealth effect of non-homeowners related to high rental cost. Still on the importance of housing price on consumption, Dong at al. (2017) show that the effects of housing price on consumption in 35 major cities in china are asymmetric in that the wealth effect and the substitution effect depend on a specific threshold determined by the housing prices. Moreover, many studies have alluded to the interaction between housing price and monetary policy stance. For example, Amador-Torres et al. (2018) assess the determinants of housing price bubbles’ duration for a set of OECD countries between 1970 and 2015. The authors show that a prolonged domestic monetary policy easing increase the duration of housing price bubbles and the tightening of monetary policy contributes in accelerating the termination of a housing bubble in OECD countries. Hui et al. (2016) assess the relationship between housing price and mortgage lending in two housing sub-market of Hong Kong by distinguishing between mass housing market and the luxury housing market. The authors find a one-way relationship in that both types of housing markets affect mortgage lending, while the change in mortgage lending has no effect in the housing market in Hong-Kong.
Along with the economic growth and the improvement of residents' living standards, the household electricity consumption continues to grow rapidly, accounting for an increasing proportion of the electricity consumption of the whole society. According to the data of electricity consumption , electricity consumption reached 5919.8 billion kWh, up by 5.0% year on year; Urban and rural residents consumed electricity of 805.4 billion kWh, up 10.8% year on year. The proportion of household electricity consumption on the total electricity consumption is only slightly more than 13%, while that of developed countries is about 20%. At the same time, through the horizontal comparison of the data of per capita household electricity consumption in various countries in 2015, the per capita household electricity consumption in most developed countries is 1000~4000 kWh, and the per capita household electricity consumption in the United States and Canada has reached 4486 kWh and 4617 kWh respectively. However, China's per capita household electricity consumption is 529 kWh, which is about 1/9 that of the United States and Canada and far lower than the level of developed countries. Through the lateral comparison of the data of per capita household electricity consumption in various provinces of China in 2015, Fujian ranked first with per capita household electricity consumption of 898.57 kWh; per capita household electricity consumption of other developed provinces and cities is more than 700 kWh, such as Beijing, Shanghai, Zhejiang and Guangdong; that is relatively low in most of the less developed provinces (such as Xinjiang, Qinghai, Ningxia and Gansu), which is under 400 kWh; that is 400~700 kWh basically in other provinces. That means there is still huge room for growth. So household electricity carbon emissions cannot be ignored in order to reduce carbon emissions. Income is one of the main driving factors of household electricity consumption, and the difference in household electricity consumption between different regions can be explained by the income gap between China and developed countries or among 30 provinces. Countries or regions with higher economic development tend to have higher per capita household electricity consumption. For developed economies, the per capita energy consumption basically shows an
include exposure variables which are frequently used in other assessments of this kind, such as land cover and surface temperature. Extreme urban heat is still not very common in Tibet except in a few locations such as Chengguan District of Lhasa and uneven spatial reso- lution of surface temperature datasets permits only intra-urban heat vulnerability assessments . Further- more, surface temperature and green spaces are not the only factors which determine air temperature and indoor temperatures. Unlike other studies, we did not include a measure of home air-conditioning (AC) although it is known access to air-conditioning may protect against heat-related illnesses and deaths [22, 34, 35]. The rea- sons are that, first, these data are not available at county-level and, second, air-conditioning at home is uncommon in Tibet (in 2008 it was estimated that only 3 % of households had AC) . Similarly, we did not include a measure of use of cooling fans, as this infor- mation is not available. Besides, although many studies have indicated that individuals in occupations that entail exposure to high temperatures outdoors are more likely to develop heat-induced diseases, we were unable to in- clude a measure of occupation in this study. Housing factors (e.g. dwelling age, the number of floors, construc- tion types) may also increase the likelihood of exposure to severe heat and influence heat-health risks. However, these data were not included in our analysis, as there are too many missing values for most of the remote coun- ties. Another important limitation related to data un- availability in this study is lack of sensitivity analysis using alternative variables.
material object of the farmer income, the real income gap may be 6–7 : 1. In recent years, the Party Central Committee and State Council puts a great emphasis on this phenomenon and 2004–2011, the central files’ key points are the problems about agriculture, rural areas and peasantry, thus reinforcing the fiscal and financial policies on rural development. On the one hand, government expenditures for agriculture continue to increase and the government is playing a more positive role to promote the rural reform and development. In 2010, the central expenditures for agriculture reached 857.97 billion yuan, an increase of 18.3%. On the other hand, financial support for agriculture is also increasing. At the end of 2010, agriculture-related loans arrived at 11.76801 trillion yuan accounting for 24.56% of the loan balance for the same period and the balance increased by 28.68%. Xing’s (2010) research offers the affirmation to the effects of the Chinese fiscal and financial policies supports for agriculture on increasing the farmers’ incomes and narrowing the income gap between urban and rural residents, but it also points out that the fiscal structure of agriculture is irrational and makes some relevant policy commendations, not only to increase the intensity of financial support, but also to put more emphasis on productive expenditures, capital expenditures, reasonable distribution of relief expenditures and funds of science and technology in rural areas. However, the status of the rural economic development makes fiscal and financial support for agriculture alone useless to solve the bottleneck of ru- ral economic funds, therefore, the fiscal and financial support for agriculture policies should be integrated to enhance the leverage effects of policies. Based on this, it will make sense to increase the funding and to enhance the overall support for agriculture significantly (Guanghe 2009).
In recent years, with the rapid increase of energy consumption in China, the problem of air pollution caused by energy consumption has also intensified, which has led many scholars to pay attention to the problem of air pollution. Brunt, H et al (2016) found that air pollution, poverty and health issues are inseparable. If the local air pollution problems and solutions are considered in the context of broader health determinants, greater health benefits can be achieved (Reduce health risks and inequality)  。 Dong K. et al (2019) found that respirable suspended particles (PM10) are a typical component of particulate matter, which can lead to increased morbidity and mortality of respiratory and cardiovascular diseases. The findings of Signoretta et al （ 2019 ） show that the perception of major air pollution problems and worse mental wellbeing go hand in hand only in partial and established environmental States.Gu, H et al (2019) nested household registration data of the 2014 China Floating Population Dynamics Survey with urban feature data and pollution data, and found that an increase in air pollution concentration significantly reduced residents’ health. Men and urbanresidents are more sensitive to air pollution and are more adversely affected.
However, further analysis after introducing consumption propensity factors shows that the change of urban-rural consumption inequality is not equal to the change of urban-rural income inequality, and they may be just similar in form but different in the variation range and turning direction. The “inverted-U shaped” curve of urban-rural consumption inequality may be higher or lower than the urban-rural income inequality in the process of economic development, and the turning point of their respective changes will not be completely consistent. The main reason for such differences is that the variation of consumption inequality is not only affected by the same factors as income inequality, but also affected by the influence of consumption propensity including the average propensity to consume and the marginal propensity to consume. This paper focuses on the impact of average propensity to consume. The average propensity to consume refers to the proportion of consumption in one's income. Due to restrictions of various reasons, the proportion of consumption in urban and rural residents' income cannot be the same, so it will inevitably lead to different heights and turning points of the urban-rural consumption inequality curve and the urban-rural income inequality curve.
The empirical data of this paper comes from the data of China Family Tracking Survey (CFPS 2014, CFPS 2012). Because the area of the family in this data can only be precise to the provincial level, the house price in this paper refers to the level of the house price of the family in the provincial level, and takes logarithms. The house price data of each province in 2012 and 2014 come from the average sale price of the house in China Statistical Yearbook. The CPI data are derived from China Statistical Yearbook. In addition, the CPFS survey data provide the variables of “the total market price of the existing residential house” and “the building area of your existing residential house”. According to these two va- riables, we can estimate the house price level faced by the family. After conver- sion with CPI, we can get the real house price level corresponding to the family, because families with higher consumption level tend to have higher income, while families with higher income tend to buy more expensive houses, therefore, using estimated house prices to study the impact of house prices on household consumption structure may have endogenous problems. Therefore, in our em- pirical analysis, we still use the real housing prices at the provincial level, because the choice of household consumption items can not be considered to affect the prices of the provinces, thus avoiding endogenous problems.
This research fills the gaps in the theories about the point-in-time and spatial pattern characteristics on urban integration in China. First of all, the research consolidates and measures 13 all the implementation cases of urban integration areas across the country. Then, the research names three points-in-time during the process of urban integration: the germinate point, the start point, and the grow point. When a single city enters the stage of late industrialization, or all cities enter the stage of mid-industrialization, it means that the area reaches the germinate point. When all cities are in the stage of late industrialization, the start point can only be reached. When an individual city enters the stage of post-industrialization, while other cities at least enter the stage of late industrialization, the area can reach grow point. Thirdly, according to the characteristics of spatial pattern, there are 3 types of urban integration: concentrated-spread type, multi-group fragmentation type, and halfway-point growth type. The first type includes 4 samples; Wulumuqi-Changji, Changsha-Zhuzhou-Xiangtan, Xi’an - Xianyang, and Guangzhou-Foshan. The second type includes 5 samples; Shenyang-Fushun, Nanjing-Zhenjiang - Yangzhou, Xiamen-Zhangzhou-Quanzhou, Shenzhe n -Huizhou, and Shenzhen-HongKong. The third type includes 4 samples; Zhengzhou-Kaifeng, Taiyuan-Jincheng, Chengdu -Deyang, and Wuhan-Ezhou. Finally, the research makes 3 depth discussions. First of all, the number of subjects changes from multiple to single, while the mechanical direction changes from single to multiple. Next, actual points-in-time of the process depend on the level of comprehensive economic development; the balance between cities is significant. At last, the formation of spatial pattern is influenced by the balance of comprehensive economic development among cities, unbalance leads to the appearance of new growth pole, while it’s not affected by the mechanical effect, which is different from that of a single city.
The present study has been conducted in the year 2008 to assess the determinants of gas energy consumption in Pakistan during 1971-2006 using econometric techniques. Time series data ranging from 1971 to 2006 has been taken from Economic Survey of Pakistan (Statistical Supplement, 2006-07). Augmented Dickey Fuller (ADF) test has been used for checking the stationarity of the data. The Akaike Information Criterion (AIC) has been used to select the optimum ADF lag. Variables which were non-stationary at level have been made stationary after taking first difference and second difference. Furthermore, the Johenson Co-integration test has been used to detect the long-term relationship among the series. To this end, the Likelihood Ratio (LR) statistic is used. To assess the determinants of gas energy consumption in Pakistan, the following model was estimated using the method of ordinary least square.
Electricity consumption brings about the concept of capacity utilization in the manufacturing sector of the Nigerian economy. Energy supplies to all sectors of the Nigerian economy have been very unreliable over the years. For example, most manufacturing, industrial and communication companies operating in Nigeria have in place active power generation facilities to compliment the very unreliable power supplies from the national grid. As such, manufacturing companies operating in the country have had to channel a significant portion of investible funds available to them to provide onsite standby power supplies, thus diverting the resources needed to fund their core manufacturing businesses. This has negative ramifications for domestic productivity, not to talk of the ability to compete internationally in a continuously changing global market (Iyoha, 2005).
These remarks provide good reasons to "scale up" the analysis to a higher spatial level: the spatial units of analysis are districts instead of houses. The total number of observations is thus reduced from 36,615 to only 309. A drawback of this approach is that much information on the characteristics of each individual house is lost: restrictive assumptions must be made regarding the homogeneity of the houses within a certain area. Since Moran's I will be used in this section to detect large-scale spatial autocorrelation patterns this approach will be followed here. For each district the median transaction price of the houses, which have been sold in 1996 is determined. In terms of Moran's I the dependent variable p contains these median prices. The 309 × 309 spatial link matrix S expresses the spatial relations between districts. We first consider the variation of the median prices around the overall mean. Table 1 contains values of the I 0 , the sample version of Moran's I. See the technical appendix for more information on coding schemes.
However, while the impact of this profile variable is relatively small, its level of significance reveals shortcomings in the first model. The variables contained in the model are unable to explain the difference between the two city profiles. We then considered the variables in the model with the profile in order to identify the effects which are specific to the profiles. So as to retain only the most influential of the 11 variables contained in this new model 8 and exclude those which repeat the same information, the stepwise econometric technique has been applied. In this method, variables are included or excluded on the basis of their level of significance in the regression. This third model reveals that fuel price and parking supply have specific effects in the case of the intensive profile (Table 2, column 4). The coefficient of determination R² is equal to 0.91 (adjusted R² = 0.89). The signs of the coefficients are identical to those in the previous models. The coefficients that apply to the two groups as a whole are once again similar. The combined effects indicate a reduction
A growing body of literatures has dealt with issues concerning REC in China. Hirshchhause and Adres (2000) have analyzed the relationship between GDP and REC. Lu (2006) has evaluated the effect of improving energy-efficiency of refrigerators. Zhang and Asano (2003) have investigated whether temperature can explain REC. However none of these studies have well-considered the income gap in China. It is widely known that while coastal provinces enjoy benefits of the recent economic reform, inland provinces still face economic stagnation. In addition, intra-provincial income gaps are also large between urban and rural areas. In order to achieve more accurate understanding of REC in China, we must take into account these income gaps. So, we estimated REC by province and also for urban and rural categories.
Most of countries in the world have experienced substantial increases in private car ownership over the recent decades. Taking China for example, according to a report stated by the Ministry of Public Security, China, 23.85 million new cars were registered in China, taking car ownership up to 172 million in 2015 (http://news.xinhuanet.com/english/2016-01/25/c_135043964.htm). For every 100 households there are 31 private cars, the statement said, adding that in big cities such as Beijing and Shenzhen, the number may be up to 60. This rapid development on car ownership raises an important question: how does the urbanspatial structure changes accordingly? The answer to this question may have significant implication in the sustainable urban development.
For a long time social sciences scholars from different fields have devoted their attention to identifying the causes leading to commit criminal offences and recently lots of studies have included the analysis of spatial effects. Respect to the Italian crime phenomenon some stylized facts exist: high spatial and time variability and presence of “organised crime” (e.g. Mafia and Camorra) deep-seated in some local territorial areas. Using explanatory spatial data analysis, the paper firstly explores the spatial structure and distribution of four different typologies of crimes (murders, thefts, frauds, and squeezes) in Italian provinces in two years, 1999 and 2003. ESDA allows us to detect some important geographical dimensions and to distinguish crucial macro- and micro- territorial aspects of offences. Further, on the basis of Becker-Ehrlich model, a spatial cross-sectional model including deterrence, economic and socio-demographic variables has been performed to investigate the determinants of Italian crime for 1999 and 2003 and its “neighbouring” effects, measured in terms of geographical and relational proximity. The empirical results obtained by using different spatial weights matrices highlighted that socioeconomic variables have a relevant impact on crime activities, but their role changes enormously respect to crimes against person (murders) or against property (thefts, frauds and squeezes). It is worthy to notice that severity does not show the expected sign: its significant and positive sign should suggest that inflicting more severe punishments does not always constitute a deterrence to commit crime, but it works on the opposite direction.
This paper introduces the capital space and a capital connectivity matrix to the field of corporate governance. Although owners are usually seen as solely seeking highest returns on their investments, maximizing profits, it was therefore concluded that ownership is not an important factor. In this paper we provided empirical evidence that there is a non-negligible effect of ownership on corporate wage policy. We believe that past difficulties in providing such empirical evidence lies in the unavailability of detailed ownership structure data and a lack of appropriate methods. In this paper we used ownership data on individual level and spatialeconometric techniques to show that ownership does matter in the case of corporate wage setting. It has been found that both ownership concentration and excess of cash-flow rights with regards to control rights have a detrimental effect on wages. Not only that, it has been found that identity of owners is also an important factor as firms with same owners similarly affect average labour costs even after controlling for standard factors such as labour productivity, employment, capital intensity and profitability. A surprising finding was a relationship between wages and profitability. Results indicate that firms with higher profits (ceteris paribus) pay lower wages. We argue that this effect could be explained by highly indebted owners, seeking higher returns on their investment by reducing labour costs.
Over the past several decades, the global process of urbanization has progressed dramatically rapid, thus gave rise to many problems for the urban environment and climate , e.g., a phenomenon known as urban heat island (UHI). UHI leads to raising atmospheric and surface temperatures in urban areas significantly warmer than in surrounding non-urbanized areas due to urbanization. UHI effects develop when a large fraction of the natural land-cover in an area are replaced by built surfaces that trap incoming solar radiation during the day and then re-radiate it at night . UHI effects are exacerbated by the anthropogenic heat generated by
materials (Jin et al. 2011). Consequently, urban soils often are compacted and structurally degraded, alkaline, and water deficient (Jim 1998a), which makes them less suitable for growing plants. Second, existing soil patterns are the prod- uct of both human disturbances and management-induced amelioration and therefore the effects of urbanization on soil nutrients. For example, some researcher demonstrated that urban soil had lower nutrients (Jim 1998b; Baxter et al. 2002), while other studies pointed out that soil nutrients were higher in urban area (Kaye et al. 2008) and had suffi- cient nutrient levels to support plant growth (Pouyat et al. 2007). Third, pollutants from industrial activities (Shang et al. 2012), heavy traffic (Chen et al. 2010a; Hamzeh et al. 2011), and house wastes (Schwarz et al. 2012; Szolnoki et al. 2013) all contribute to soil contamination by heavy metals, which is likely to have adverse effects on human health.