By taking the panel data of 31 provinces and cities in China from 2000 to 2012 as the samples, this article explores the spatial distribution characteristics of industrial waste gas emission by applying the method of exploratory spatial data analysis including the global Moran’s I, Moran scatter plot and local indicators of spatial association. The results of Moran’s I statistics and Moran scatter plot show that, industrial waste gas emission of China’s provinces and cities (excluding Hong Kong, Macau and Taiwan) exist significantly spatial autocorrelation and spatial agglomeration effect from 2000 to 2012. Meanwhile, in the light of LISA cluster map of China’s provincial industrial waste gas emission, as a whole the eastern and western regions of China mainly display the spatial agglomeration characteristics. The provinces and cities with HH(high-high) agglomeration pattern are basically concentrated in the eastern district, but the provinces and cities with LL(low-low) agglomeration effect are largely located in the west of China. Besides, from 2000 to 2012 the significance of high-high cluster is gradually strengthened and the significant regions are appearing an enhanced tendency with the time. By means of the analysis on spatial characteristics of Chinese provincial industrial waste gas emission, the spatial autocorrelation effect of industrial waste gas emission is confirmed and the spatial econometric model can be established to study the influencing factors of industrial waste gas emission in China. Based on the STIRPAT model, this article constructs a spatial econometric model to analyze the effect of economic development, population, industrial structure, scientific and technological progress and national policy to industrial waste gas emission in China. Through the study on principal influencing factors of China’s industrial waste gas emission, the experience support to reducing industrial waste gas emission and developing coordinately with economy and environment can be provided. The spatial econometric results demonstrate that industrial waste gas emission of 31 provinces and cities in China present the evident spatial dependence and positive spillover effects. The economic development and industrial structure are positively and significantly correlated with industrial waste gas emission of China. Technical progress and national policy have preventing abilities to industrial waste gas emission in China. However, population factor does not have a significant effect on China’s industrial waste gas emission. In the future, it is still necessary to continuously improve the level of industrial science and technology, adjust industry structure, enhance and promote the regional cooperation mechanism, and so on.
LlorcaM, MeunieA.SO2 Emissions and the Environmental Kuznets Curve:the Case of Chinese Provinces[J]. , 2009, 7(1): 1-16./s?wd=paperuri%3A%28f43b09f1ecd17a0663bef7ce31a4c7e8%29&filter=sc_long_sign&sc_ks_para=q%3DSO%202%20emissions%20and%20the%20environmental%20Kuznets%20curve%3A%20the%20case%20of%20Chinese%20provinces&sc_us=8148692552006619758&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8
The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%鈥45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China's carbon intensity from energy consumption in 1997鈥2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP(11.72%). Secondly, the trend of Moran's I indicated that China's carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China's carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.
[ChengSheqing, WangZheye, YeXinyueet al. Spatiotemporal Dynamics of Carbon Intensity From Energy Consumption in China. , 2014, 24(4): 631-650.]
[National Bureau ofStatistics.[M].Beijing: China Statistics Press ,2001-2013.]
[National Bureau ofStatistics, Ministry of EnvironmentalProtection. [M].Beijing: China Statistics Press ,2001-2013.]
[LiZinai, YeAzhong. [M].Beijing: Tsinghua University Press,2012.]
Rey SJ.Spatial Empirics for Economic Growth and Convergence[J]. , 2001, 33(3): 195-214.http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2001.tb00444.x/full
This paper suggests some new empirical strategies for analyzing the evolution of regional income distributions over time and space. These approaches are based on extensions to the classical Markov transition matrices that allow for a more comprehensive analysis of the geographical dimensions of the transitional dynamics. This is achieved by integrating some recently developed local spatial statistics within a Markov framework. Insights to not only the frequency with which one economy may transition across different classes in the income distribution, but also how those transitions may or may not be spatially dependent are provided by these new measures. A number of indices are suggested as ways to characterize the space-time dynamics and are illustrated in a case study of U. S. regional income dynamics over the 1929鈥1994 period.
AnselinL.Local Indicators of Spatial Association Analysis-LISA[J]. , 1995, 27(2): 93-115.http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1995.tb00338.x/full
The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the G i and G* i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
[HeXiaogang, ZhangYaohui.Influence factors and environmental kuznets curve relink effect of Chinese industry's carbon emission--empirical research based on STIRPAT Model with industrial dynamic panel data. , 2012,(1): 26-35.]
Elhorst JP.Specification and Estimation of Spatial Panel Data Models[J]. , 2003, 26(3): 244-268.http://www.researchgate.net/publication/255573268_Specification_and_Estimation_of_Spatial_Panel_Data_Models
This article provides a survey of the specification and estimation of spatial panel data models. These models include spatial error autocorrelation, or the specification is extended with a spatially lagged dependent variable. In particular, the author focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the fixed coefficients model, and the random coefficients model. The survey discusses the asymptotic properties of the estimators and provides guidance with respect to the estimation procedures, which should be useful for practitioners.