Retail Sales Of Consumer Goods Research Articles

Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method

PurposeSeasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.Design/methodology/approachFirst, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.FindingsThe data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.Practical implicationsIn this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.Originality/valueThis paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis

Eco-efficiency can effectively measure the relationship between economy, resources, environment, and development. Understanding eco-efficiency is of great practical significance for decision makers tasked with addressing and delivering sustainable socioeconomic development. Based on an “undesirable output Slacks-Based Measure models,” this study evaluated the eco-efficiency of 285 Chinese cities during the period 2004–2014, analyzing spatiotemporal dynamics and influencing factors using a Spatial Autocorrelation Panel Data Model. The main results are as follows: From the spatial perspective, eco-efficiency in Chinese cities generally evidenced an M-shaped trend (increasing-decreasing-increasing-decreasing), and imbalanced spatiotemporal dynamics. Furthermore, the urban eco-efficiency generally presented a distinct convergence of HH cluster and LL cluster clubs, with the latter being the most dominant. From the regional point of view, there existed a decreasing trend in the efficiencies of cities, from eastern China to central and western China. In addition, we identified significant differences in the eco-efficiency of different cities in China, and the coefficient of variation of eco-efficiency showed a general decrease. The results of our estimation of the factors affecting urban eco-efficiency showed that the economic development level, the industrial structure, import and export trade, and the information level all had significant positive influence, and local government expenditure, social retail sales of consumer goods, and infrastructure all had a negative effect on urban eco-efficiency. This paper puts forward numbers of suggestions for ways to promote social, ecological, and economic development in Chinese cities, based on our findings.

Econometric Analysis and Policy Suggestions on the Influencing Factors of Vegetable Industry Development in Maoming

Vegetables are one of the most important foods in people’s daily lives. It plays an important role in improving people’s living standards, resettling rural labor, increasing farmers’ income, and increasing foreign exchange. In order to promote the development of Maoming’s vegetable industry, this paper uses econometric methods to carry out a quantitative analysis of the influencing factors of Maoming’s vegetable industry development. In this paper, Maoming’s vegetable operating income is used as the explained variable and several influencing factors are used as the explanatory variables to establish a multiple linear regression model. The data from 1998 to 2012 were used for regression, and the model was tested for economic significance, statistical test, multicollinearity test, heteroscedasticity test, and autocorrelation test. The regression results show that: Maoming’s vegetable operating income has a positive correlation with rural households’ per capita net income, per capita cargo transportation volume, and per capita retail sales of consumer goods. Therefore, in order to promote the development of Maoming’s vegetable industry, we should vigorously increase farmers’ income, increase consumption, and increase the volume of goods transported.

The role of ports in the economic development of port cities: Panel evidence from China

Recently the driving force for economic growth in China's port cities is insufficient, and ports are suffering from the capacity surplus in the context of the global recession. To find out whether the problem could be relieved by adjusting port and city development strategy, the present study examined the relationship between economic indicators of the port city and the port throughput. Panel data regression models were estimated to examine the extent of impact between port throughput and city economy with the second-generation panel data analysis methods, with panel data for 16 port-city pairs in China over the period 2000–2016. Results suggest that port throughput imposes a non-negligible impact on GDP while it has an opposite effect on the amount of total retail sales of consumer goods (TRSCG). Regarding the economic structure indicators, the port throughput increases synchronously with the added value of the secondary industry (SI) but negatively with that of the primary industry (PI) and the tertiary industry (TI). The causality test confirms an interactive mechanism between the port city economy and the port throughput in the sampled city-port pairs. The underlying reasons were discussed, and several policy suggestions to respond to the research question were proposed.

Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods.

An Optimal Model based on Multifactors for Container Throughput Forecasting

Containerization plays an important role in international trade. Container throughput is a key indicator to measure the development level of a port. In this paper, Lianyungang Port and Shanghai Port are chosen to study the method for container throughput forecasting. Gray model, triple exponential smoothing model, multiple linear regression model, and backpropagation neural network model are established. Five factors are selected as influential factors. They are total retail sales of consumer goods, gross domestic product of the local city, import and export trade volume, total output value of the second industry and total fixed assets investment. The growth and the raw datasets are used in the prediction, respectively. The datasets from 1990 to 2011 are chosen to build models and the ones from 2012 to 2017 are used to assess the performance of the models. By comparison, the backpropagation neural network model is applicable to both Shanghai Port and Lianyungang Port for container throughput forecasting. The volume of container throughput at both ports from 2018 to 2020 is predicted.

Analysis of influencing factors of the CO2 emissions in China: Nonparametric additive regression approach

As the maximal carbon dioxide (CO2) contributor in world, China is embracing severe stress from emission reduction. It is increasingly important to study the factors affecting China's CO2 emissions. Many researches had extensively researched the driving forces of CO2 emissions of China. However, majority of the researches adopt a conventional linear method based on time-series or cross-section data for researching the CO2 emissions as well as nearly neglect nonlinear relationships. To surmount the limitations of extant investigations, this research adopts a data-driven nonparametric additive regression approach to examine primary influencing factors of China's CO2 emissions. The results manifest that the nonlinear influence of economy on CO2 emissions is same as the Environmental Kuznets Curve hypothesis. The household consumption level embodies the inverted “U-type” pattern. The industrialization also embodies the overturned “U-type” relationship. Aggregate retail sales of consumer goods present a positive “U-type” effect upon CO2 emissions. Similarly, the urbanization signifies a positive “U-type” nexus upon CO2 emissions. Energy intensity indicates a positive “U-type” nexus. The paper ought to exert more attention to the dynamic effects of the driving forces above in order to abate the CO2 emissions of China. This study will also propose corresponding policies and recommendations according to the dynamic effects.

The impact of housing price on non-housing consumption of the Chinese households: A general equilibrium analysis

In recent years, the absolute values of the housing price and the households’ non-housing consumption in China are both on the rise, but many documents studying on the impact that the ever-changing housing price exerts on Chinese households’ consumption consider that the rise of housing price represses the households’ consumption expenditure. This paper constructs a general equilibrium model to explain the impact of housing price changes on the consumption expenditure of urban residents. The result shows that the rise in housing prices has both crowding-out and wealth effects for households’ consumption. Further, the empirical test shows that during the period of “2006–2010”, for every 1% rise in housing prices, the total retail sales of consumer goods falls by 0.332%; and to the contrast, during the “2011–2015” period, for every 1% rise in housing price, the total retail sales of consumer goods are increased by 0.207%. The above results indicate that along with the increase in housing holding areas, the impact of housing price changes on households’ consumption has gradually shifted from the crowding-out effect to the wealth effect. We also find out that external speculative demand makes the impact of housing price rise on local residents’ consumption manifested as the crowding-out effect. However, in the regions with weaker external speculative demand, the impact of housing price changes on households’ consumption is mainly shown as the wealth effect.

An Empirical Analysis of the Total Retail Sales of Consumer Goods by Using Time Series Model

With the continuous improvement of living standards, the total amount of social consumer goods in China’s economic development has occupied an important position. Its fluctuations can indirectly reflect the demand and purchasing power of commodities, thus affecting the state’s macroeconomic regulation and control. This paper selects the total amount of social consumer goods in China from August 2005 to February 2019. Using EViews 7.2 software, this paper makes use of the correlation analysis of sequence fluctuation in econometrics and financial time series, and finds the best fitting EGARCH (1,1) model based on ARMA (1,0) to conduct an empirical analysis of the total amount of social consumer goods in China, and concludes that the total amount of social consumer goods in China has leverage effect.

Long-Term Atmospheric Visibility Trends and Their Relations to Socioeconomic Factors in Xiamen City, China

Atmospheric visibility (AV), one of the most concerning environmental issues, has shown a continuous decline in China’s urban areas, especially in Southeastern China. Existing studies have shown that AV is affected by air pollutants and climate change, which are always caused by human activities that are linked to socioeconomic factors, such as urban size, residents’ activities, industrial activities, and urban greening. However, the contribution of socioeconomic factors to AV is still not well understood, especially from a long-term perspective, which sometimes leads to ineffective policies. In this study, we used the structural equation model (SEM) in order to quantify the contribution of socioeconomic factors on AV change in Xiamen City, China, between 1987–2016. The results showed that the annual average AV of Xiamen between 1987–2016 was 12.00 km, with a change rate of −0.315 km/year. Urban size, industrial activities, and residents’ activities were found to have a negative impact on AV, while the impact of urban greening on the AV was modest. Among all of the indicators, the number of resident’s vehicles, total retail sales of consumer goods, and household electricity consumption were found to have the highest negative direct impact on the AV. The resident population, urban built-up area, and secondary industry gross domestic product (GDP) were the most important indirect impact factors. Based on our results, we evaluated the existing environmental regulations and policies of Xiamen City.

Prediction of the National Total Retail Sales of Consumer Goods Based on ARIMA Model

This article selects the data of Chinese Total Retail Sales of Consumer Goods from 1952 to 2016 and then established ARIMA (2,1,2) model after the comparative test. It is found that the relative error between actual numbers and predictive numbers from 2011 to 2016 is smaller than 3%. Therefore, it is feasible to use the ARIMA (2,1,2) model to predict the short-term data of Chinese Total Retail Sales of Consumer Goods. The prediction is that consumer markets will increase by more than 10% with little volatility.

Administrative restructuring and land-use intensity—A spatial explicit perspective

BackgroundThe rapid industrialization, urbanization, and population explosion have ushered forth the era focusing on the efficient use of land resources to achieve sustainable development. “Intensive Land-Use Plan” was therefore proposed in the context of shifting the focus from “incremental planning” to “inventory planning” in rapidly developing countries, particularly China. MethodsWe develop our framework based on the hypothesis that administrative status has a spatial effect on intensive land use and explore this effect dynamically. In the first step, we summarized the different degrees of land-use intensity based on different administrative status (i.e., urban district, suburban district, county, county-level city, and district) and make categorizations. We also devised spatial models in multiple strategies embedded with different spatial weight matrices to the underlying driving forces using Wuhan agglomeration as the case. ResultsResults show that 1) urbanization, industrial structure, and retail sales of consumer goods are highly correlated with land-use intensity change, with all factors being positive; 2) the influences of different administrative status exist and strengthened in the past decade; the spatial interaction is greater when urban district is adjacent to district in the prefecture city in 2005 and 2015. In turn, this finding affirms the phenomenon that the administrative power has gradually strengthened, and the city centers are the most vibrant areas to compete for higher land-use efficiency. RecommendationThe emergence of diversified types of counties, such as urban districts and county-level cities, has further pressed the necessity to embed administrative influence in spatial regression. These findings provide theoretical basis for understanding the administrative dimension in new urbanization and have important policy implications on administrative adjustment and intensive land use.

Fitting and Prediction of Dynamic Coupling Effect Between Online and Offline Total Retail Sales of Consumer Goods

Fitting and Prediction of Dynamic Coupling Effect Between Online and Offline Total Retail Sales of Consumer Goods

Land Use/Cover Change from 2001 to 2010 and its Socioeconomic Determinants in Guangdong Province, A Rapid Urbanization Area of China

Rapid economic and population growth exert profound effects on land use/cover change, especially in rapid economically developing areas, such as Guangdong Province-a pioneer of economic growth and urbanization in China. To elucidate the changes and mutual conversions of land-use types and their determinants in Guangdong Province, land-use maps of Guangdong Province from 2001 to 2010 were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B images with 500 m resolution by using the supervised classification combined support vector machine (SVM) algorithm with a decision tree. The results indicate that during the studied time period, the area of urban land and orchards increased by 4186 km2 and 3615.5 km2, respectively, while the percentage of cropland decreased from 11.46% to 10.14%. Massive urbanization and cropland loss have occurred in the region. The conversion from wasteland to urban land, and the mutual transformation between wasteland and cropland were the most prevalent in Guangdong Province. This was especially obvious from 2007 to 2010, with an annual changing rate of 5.13% and -19.33%, respectively. Several socio-economic factors were also obtained to investigate their impacts on land-use changes in Guangdong Province. It is shown that marked increases in gross domestic product (GDP), total investment in fixed assets, total retail sales of consumer goods, and the increasing development of modern service industries have led to large-scale migration, and industrial structure adjustment and improvement; consequently dramatically rapid expansion of cities and cropland loss have occurred.

Land use/cover change from 2001 to 2010 and its socioeconomic determinants in Guangdong Province, a rapid urbanization area of China

Rapid economic and population growth exert profound effects on land use/cover change, especially in rapid economically developing areas, such as Guangdong Province-a pioneer of economic growth and urbanization in China. To elucidate the changes and mutual conversions of land-use types and their determinants in Guangdong Province, land-use maps of Guangdong Province from 2001 to 2010 were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B images with 500 m resolution by using the supervised classification combined support vector machine (SVM) algorithm with a decision tree. The results indicate that during the studied time period, the area of urban land and orchards increased by 4186 km2 and 3615.5 km2, respectively, while the percentage of cropland decreased from 11.46% to 10.14%. Massive urbanization and cropland loss have occurred in the region. The conversion from wasteland to urban land, and the mutual transformation between wasteland and cropland were the most prevalent in Guangdong Province. This was especially obvious from 2007 to 2010, with an annual changing rate of 5.13% and -19.33%, respectively. Several socio-economic factors were also obtained to investigate their impacts on land-use changes in Guangdong Province. It is shown that marked increases in gross domestic product (GDP), total investment in fixed assets, total retail sales of consumer goods, and the increasing development of modern service industries have led to large-scale migration, and industrial structure adjustment and improvement; consequently dramatically rapid expansion of cities and cropland loss have occurred.

Chinese household consumption: how much is enough?

As per currently accepted methodology of national economic accounting, China’s household consumption rate is considered relatively low. This paper discusses the evolvement of China’s consumption over time. Employing two different measures of consumption, I compare the growth rate of China’s household consumption with the growth rates of China’s economy, of its disposable income and of other nations’ consumption. The results of the analysis demonstrate that China’s household consumption is growing vigorously in terms of both real growth rates of per capita household consumption and in total retail sales of consumer goods. Despite its current low level to GDP, China’s household consumption has experienced a significant growth in recent years.

Understanding China’s official statistics

In response to the three influential doubts about government statistics, this article expounds the differences between relevant common economic statistical indicators and the national account indicators that reflect demand structure and national income distribution structure. The comparisons include the following aspects: the differences between household consumption expenditure from household survey and from expenditure-based GDP; the differences between total retail sales of consumer goods and final consumption expenditure from expenditure-based GDP; the differences between government expenditure from government fiscal statistics and government consumption expenditure from expenditure-based GDP; the differences between total investment in fixed assets from investment statistics and gross fixed capital formation from expenditure-based GDP; the differences between inventories and change in inventories from expenditure-based GDP; the differences between foreign trade balance from customs statistics and net exports of goods and services from expenditure-based GDP; and the differences between household disposable income from the household survey and from the Flow of Funds Table, etc. In addition, this article answers the questions proposed by the three doubts correspondingly.

Study on Sustainable Development of Highway Traffic in China

Based on sustainable development,the Data Envelopment Analysis (DEA) with eight input indexes and four output indexes, was applied to evaluate the highway traffic sustainable development of 31 provinces in China. The result shows that the input and output of highway construction has reached the best combination at Beijing, Tianjin, Shanghai, and Tibet. GDP(Group Domestic Product) and social total retail sales of consumer goods can be improved by increasing input in eleven provinces. From the perspective of traffic safety,accident rates will be no major increase when the government enlarge investment of highway building in our country,These results imply that China is making strong progress in the road traffic environment.