Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.     

CO2 emissions and their bearing on China＇s economic development： the long view

Greenhouse-gas (GHG) emissions in China have aroused much interest, and not least in recent evidence of their reduction. Our intent is to place that reduction in a larger context, that of the process of industrialization. A lengthy time perspective is combined with a cross-sectional approachChina plus five other countries-and addressed through two general models. The findings are salutary. First, they suggest that a diversified economic structure is consistent with diminished intensity in energy use. Secondly, and the obverse of the first, they imply that a diversified energy structure promotes reductions in CO2 emissions. Finally, one is led inevitably to the conclusion that, together, the findings point to a path for countries to transform their economies while at the same time undertaking to drastically moderate their energy use, switching from a pattern of heavy carbon emissions to one in which lighter carbon emissions prevail. The implications of such findings for environmental management are enormous.     

中国旅游业能源消耗与二氧化碳排放量估算(英文)

In 2009, nearly 900 million international tourist arrivals were counted worldwide. A global activity of this scale can be assumed to have a substantial impact on the environment. In this contribution, five major aspects such as the change of LUCC and the use of energy and its associated impacts had been recognized. Recently, the impact of tourism on environment and climate attracts the attention of international organizations and societies in pace with rapid development of tourism industry. Energy consumption and CO2 emissions in tourism sector are becoming a hot spot of international tourism research in recent five years. The use of energy for tourism can be divided according to transport-related purposes (travel to, from and at the destination) and destination-related purposes excluding transports (accommodation, food, tourist activities, etc.). In addition, the transports, accommodation and foods are related to many other industries which are dependent on energy. Thus, the estimations of energy consumption and CO2 emissions in tourism sector have become a worldwide concern. Tourism in China grows rapidly, and the number of domestic tourists was 1902 million in 2009. Energy use and its impact on the environment increase synchronously with China’s tourism. It is necessary to examine the relationship between energy use and CO2 emissions. In this article, a preliminary attempt was applied to estimate the energy consumption and CO2 emissions from China’s tourism sector in 2008. Bottom-up approach, literature research and mathematical statistics technology were also adopted. According to the calculations, Chinese tourism-related may have consumed approximately 428.30 PJ of energy in 2008, or about 0.51% of the total energy consumptions in China. It is estimated that CO2 emissions from tourism sector amounted to 51.34 Mt, accounting for 0.86% of the total in China. The results show that tourism is a low-carbon industry and also a pillar industry coping with global climate change, energy-saving and CO2 emission reduction. Based on this, the authors suggested that tourism should become an important field in low-carbon economic development.     

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素分析（英文）

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.     

Urban carbon footprint and carbon cycle pressure: The case study of Nanjing简

Urban carbon footprint reflects the impact and pressure of human activities on ur- ban environment. Based on city level, this paper estimated carbon emissions and carbon footprint of Nanjing city, analyzed urban carbon footprint intensity and carbon cycle pressure and discussed the influencing factors of carbon footprint through LMDI decomposition model. The main conclusions are as follows： （1） The total carbon emissions of Nanjing increased rapidly since 2000, in which the carbon emission from the use of fossil energy was the largest Meanwhile, carbon sinks of Nanjing presented a declining trend since 2000, which caused the decrease of carbon compensation rate and the increase of urban carbon cycle pressure. （2） The total carbon footprint of Nanjing increased rapidly since 2000, and the carbon deficit was more than ten times of total land areas of Nanjing in 2009, which means Nanjing confronted high carbon cycle pressure. （3） Generally, carbon footprint intensity of Nanjing was on de- crease and the carbon footprint productivity was on increase. This indicated that energy utilization rate and carbon efficiency of Nanjing was improved since 2000, and the policy for energy conservation and emission reduction taken by Nanjing＇s government received better effects. （4） Economic development, population and industrial structure are promoting factors for the increase of carbon footprint of Nanjing, while the industrial carbon footprint intensity was inhibitory factor. （5） Several countermeasures should be taken to decrease urban carbon footprint and alleviate carbon cycle pressure, such as： improvement of the energy efficiency, industrial structure reconstruction, afforestation and environmental protection and land use control. Generally, transition to low-carbon economy is essential for Chinese cities to realize sustainable development in the future.     

中国在气候变化下碳排放、能源消费与低碳经济领域的研究进展综述（英文）

Accompanying the rapid growth of China's population and economy, energy consumption and carbon emission increased significantly from 1978 to 2012. China is now the largest energy consumer and CO2 emitter of the world, leading to much interest in researches on the nexus between energy consumption, carbon emissions and low-carbon economy. This article presents the domestic Chinese studies on this hotpot issue, and we obtain the following findings. First, most research fields involve geography, ecology and resource economics, and research contents contained some analysis of current situation, factors decomposition, predictive analysis and the introduction of methods and models. Second, there exists an inverted "U-shaped" curve connection between carbon emission, energy consumption and economic development. Energy consumption in China will be in a low-speed growth after 2035 and it is expected to peak between 6.19–12.13 billion TCE in 2050. China's carbon emissions are expected to peak in 2035, or during 2020 to 2045, and the optimal range of carbon emissions is between 2.4–3.3 PgC/year(1 PgC=1 billion tons C) in 2050. Third, future research should be focused on global carbon trading, regional carbon flows, reforming the current energy structure, reducing energy consumption and innovating the low-carbon economic theory, as well as establishing a comprehensive theoretical system of energy consumption, carbon emissions and low-carbon economy.     

中国城镇化水平、经济增长和二氧化碳排放的面板协整和因果分析（英文）

Elucidating the complex mechanism between urbanization,economic growth,carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997–2010,this study empirically examines the relationships among urbanization,economic growth and carbon dioxide(CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization,economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth,both of which increase CO2 emissions in China and its eastern,central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization,economic growth and CO2 emissions,indicating that in the long run,urbanization does have a causal effect on economic growth in China,both of which have causal effect on CO2 emissions. At the regional level,we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run,we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China,not supporting the environmental Kuznets curve(EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.     

基于LMDI分解和STIRPAT模型的哈萨克斯坦能源碳排放影响因素的定量分析（英文）

Quantitative analysis of the impact factors in energy-related CO_2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO_2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO_2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO_2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO_2 emissions, which produced 110.86 Mt CO_2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO_2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO_2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO_2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO_2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO_2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.     

1980-2017年中国碳强度关键影响因子变化特征

The Chinese government ratified the Paris Climate Agreement in 2016.Accordingly,China aims to reduce carbon dioxide emissions per unit of gross domestic product(carbon intensity)to 60%–65%of 2005 levels by 2030.However,since numerous factors influence carbon intensity in China,it is critical to assess their relative importance to determine the most important factors.As traditional methods are inadequate for identifying key factors from a range of factors acting in concert,machine learning was applied in this study.Specifically,random forest algorithm,which is based on decision tree theory,was employed because it is insensitive to multicollinearity,is robust to missing and unbalanced data,and provides reasonable predictive results.We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017.The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry,the proportion of fossil fuel-based energy,and technological progress.The Chinese economy developed rapidly between 1992 and 2007;during this time,the effects of the proportion of service industry,price of fossil fuel,and traditional residential consumption on carbon intensity increased.Subsequently,the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis;during this period,reductions in emissions and the availability of new energy types began to have effects on carbon intensity,and the importance of residential consumption increased.The results suggest that optimizing the energy and industrial structures,promoting technological advancement,increasing green consumption,and reducing emissions are keys to decreasing carbon intensity within China in the future.These approaches will help achieve the goal of reducing carbon intensity to 60%–65%of the 2005 level by 2030.     

中国能源消费碳排放强度及其影响因素的空间计量(英文)

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%–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 1997–2010. 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.     

中国碳排放对国际贸易影响的经验分析(英文)

International trade is an important impact factor to the carbon emissions of a country.As the rapid development of Chinese foreign trade since its entry into the WTO in 2002,the effects of international trade on carbon emissions of China are more and more significant.Using the recent available input-output tables of China and energy consumption data,this study estimated the effects of Chinese foreign trade on carbon emissions and the changes of the effects by analyzing the emissions embodied in trade between 2002 and 2007.The re-sults showed a more and more significant exporting behavior of embodied carbon emissions in Chinese international trade.From 2002 to 2007,the proportion of net exported emissions and domestic exported emissions in domestic emissions increased from 18.32% to 29.79% and from 23.97% to 34.76%,respectively.In addition,about 22.10% and 32.29% of the total imported emissions were generated in processing trade in 2002 and 2007,respectively,which were imported and later exported emissions.Although,most of the sectors showed a growth trend in imported and exported emissions,sectors of electrical machinery and communication electronic equipment,chemical industry,and textile were still the biggest emission exporters,the net exported emissions of which were also the largest.For China and other developing countries,technology improvement may be the most favorable and acceptable ways to re-duce carbon emissions at present stage.In the future negotiations on emissions reduction,it would be more fair and reasonable to include the carbon emissions embodied in international trade when accounting the total emissions of an economy.     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.     

Carbon emissions trends with optimal balanced economic growth of China and the USA and some abatement options for China

It is believed that the global CO2 emissions have to begin dropping in the near fu- ture to limit the temperature increase within 2 degrees by 2100. So it is of great concern to environmentalists and national decision-makers to know how the global or national CO2 emissions would trend. This paper presented an approach to project the future CO2 emissions from the perspective of optimal economic growth, and applied this model to the cases of China and the United States, whose CO2 emissions together contributed to more than 40% of the global emissions. The projection results under the balanced and optimal economic growth path reveal that the CO2 emissions will peak in 2029 for China and 2024 for the USA owing to their empirically implied pace of energy efficiency improvement. Moreover, some abatement options are analyzed for China, which indicate that 1） putting up the energy price will de- crease the emissions at a high cost; 2） enhancing the decline rate of energy intensity can significantly mitigate the emissions with a modest cost; and 3） the energy substitution policy of replacing carbon intensive energies with clean ones has considerable potential to alleviate emissions without compromising the economic development.     

中国能源消费碳排放的空间计量分析(英文)

Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support,this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy con-sumption,spatial autocorrelation analysis of carbon emissions,spatial regression analysis between carbon emissions and their influencing factors.The analyzed results are shown as follows.(1) Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly.(2) The global spatial autocorrelation of carbon emissions from energy consumption in-creased from 1997 to 2009,the spatial autocorrelation analysis showed that there exists a "polarization" phenomenon,the centre of "High-High" agglomeration did not change greatly but expanded currently,the centre of "Low-Low" agglomeration also did not change greatly but narrowed currently.(3) The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population,R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population.The contribution of population to carbon emissions in-creased but the contribution of GDP decreased from 1997 to 2009.The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population,so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.     

Analysis and prediction of household carbon emission in northwest China北大核心CSCD

The economic development, living standard of residents and carbon emissions in Northwest China are lower than the national average. However,with the favorable policies the economic development is being improved and the household living standard is gradually raised up which will lead to an increase of the residents living carbon emissions, and the emission pattern will also be affected. This is detrimental to the fragile ecological environment of the Northwest China. At present, most of the researches on residents' carbon emissions are focused on the eastern and southern regions of China where there are frequent and significant human activities and high carbon emissions, and less attention has been paid to the northwest region, but the increase of carbon emissions and the increase of environmental costs have a more far-reaching impact on the less developed areas. In addition, when researchers pay attention to the prediction of residents' carbon emissions, they usually focus on the quantitative prediction and ignore the spatial pattern prediction, which is not conducive to the coordinated development between regions. Based on the data of energy consumption and consumption expenditure in the five provinces of Northwest China, including Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang from 1997 to 2016, this paper firstly used the direct coefficient method to measure the residents' direct carbon emissions, and the input-output method to calculate the indirect carbon emissions of the residents and analyzes the present situation of residents' carbon emissions in the northwest region. Secondly, based on standard deviation ellipse and Autoregressive Integrated Moving Average Model, the carbon emissions of residents in Northwest China were predicted in terms of quantity and spatial pattern from 2017 to 2021. Major results are listed as follows: From 1997 to 2016, household carbon emissions in Northwest China showed a rising trend with an initial slow pace followed by a quick pace. The direct carbon emissions were stabilized in the range from 0. 3 × 108 t to 0. 4 × 108 t,and the indirect carbon emissions reached 2. 38 × 108 t. The spatial distribution of household carbon emissions in Northwest China was generally steady with a direction pattern from northwest to southeast. And the moving trend of standard deviation ellipse was from northwest to southeast to northwest, and the center of standard deviation ellipse moved around the point of (99. 07 °E,38. 19°N). From 2017 to 2021, the direct household carbon emissions in Northwest China reach to 0.543 × 108 t and the indirect carbon emissions are 3. 631 × 108 t by 2021. With the development of the western region in China and the promotion of poverty alleviation,Xinjiang Province had a lower emission than Shaanxi,but it had the higher growth rate than Shaanxi. These factors are all driving the main areas of carbon emission northwestward. The purpose of this paper is to recommend how to coordinate between the population and consumption and the environment, leading citizens to establish the value of low-carbon consumption. © 2019 Science Press (China). All rights reserved.     

基于土地集约利用的长株潭城市群建设用地供需仿真模拟（英文）

Urban land intensive use is an important indicator in harmonizing the relationship between land supply and demand. The system dynamics(SD) can be used to construct the feedback loop between urban construction land supply and demand and index variable function. Based on this, this study built a supply and demand system dynamic model of urban construction land for Chang-Zhu-Tan urban agglomeration. This model can simulate the change trends of supply and demand of construction land, industrial land, and residential land in 2016–2030 by three scenarios of low, medium, and high intensity modes. The results showed that the scale of construction land of urban agglomeration is expanding, with a rapid increase rate for the urban construction land. The scale and speed of land use based on the three intensity modes existed differences. The large scale and supply of construction land in the low intensity mode caused easily the waste of land resources. In high intensity mode, the scale and supply of construction land were reduced against the healthy development of new-type urbanization. In the medium intensity mode, the scale and supply of land use adapted to the socio-economic development and at the same time reflected the concept of modern urban development. In addition, the results of this study found that the proportion of industrial land in construction land ranged from 15% to 21%, which increased year by year in the low intensity mode, and decreased slowly and stabilized in medium and high intensity modes. The proportion of residential land in construction land ranged from 27% to 35%, which decreased in the low and the medium intensity modes, and maintained a high level in the higher intensity mode. This study contributes to provide scientific reference for decision-making optimization of land supply and demand, urban planning, and land supply-side reform.     

中国能源消费与气候变化的关系

Energy consumption has an inevitable connection with economic level and climate. Based on selected data covering annual total energy consumption and its composition and that of all kinds of energy in 1953-1999, the annual residential energy consumption and the coal and electricity consumption in 1980-1999 in China, the acreage of crops under cultivation suffered from drought and flood annually and gross domestic product (GDP) in 1953-1999 in the whole country, and mean daily temperature data from 29 provincial meteorological stations in the whole country from 1970 to 1999, this paper divides energy consumption into socio-economic energy consumption and climatic energy consumption in the way of multinomial. Itchanges between the climate energy consumption andalso goes further into the relations and their changes between the climate energy consumptionenergy consumption and the economic level inand climate factor and between the socio-economic energy between the climate energy level in China with the method of statistical analysis. At present, there are obvious transitions in the changing relationships of the energy consumption to economy and climate, which comprises the transition of economic system from resource-intensive industry to technology-intensive industry and the transition of climatic driving factors of the energy consumption from driven by the disasters of drought and flood to driven by temperature.     

中国城市群分布及范围的定量评价(英文)

Urban clusters are the expected products of high levels of industry and urbanization in a country, as well as being the basic units of participation in global competition. With respect to China, urban clusters are regarded as the dominant formation for boosting the Chinese urbanization process. However, to date, there is no coincident, efficient, and credible methodological system and set of techniques to identify Chinese urban clusters. This research investigates the potential of a computerized identification method supported by geographic information techniques to provide a better understanding of the distribution of Chinese urban clusters. The identification method is executed based on a geographic information database, a digital elevation model, and socio-economic data with the aid of ArcInfo Macro Language programming. In the method, preliminary boundaries are identified accord-ing to transportation accessibility, and final identifications are achieved from limiting city numbers, population, and GDP in a region with the aid of the rasterized socio-economic dataset. The results show that the method identifies nine Chinese urban clusters, i.e., Pearl River Delta, Lower Yangtze River Valley, Beijing-Tianjin-Hebei Region, Northeast China Plain, Middle Yangtze River Valley, Central China Plains, Western Taiwan Strait, Guanzhong and Chengdu-Chongqing urban clusters. This research represents the first study involving the computerized identification of Chinese urban clusters. Moreover, compared to other related studies, the study’s approach, which combines transportation accessibility and socio-economic characteristics, is shown to be a distinct, effective and reliable way of identifying urban clusters.     

熵视角下的广州城市生态系统可持续发展能力分析(英文)

The urban ecosystem possesses dissipating structures that can absorb substances and energy from the external environment and export products and wastes to maintain order within the system. Given these circumstances, this paper analyzed the ability of the urban ecosystem in Guangzhou City to sustain development from the perspective of entropy. The research was carried out in three steps. First, an evaluation index system that considers the ability of the urban ecosystem for sustainable development was formed based on the structures and functions of the urban ecosystem and the change in the entropy of the urban socioeconomic ecosystem. Second, the sustainable development ability assessment model for the urban ecosystem was built using information entropy. Last, by combining the time series variation of the evaluation indicators with the entropy weights, this paper analyzed the influence of the combined factors on the sustainable development ability of the urban ecosystem in Guangzhou and suggested some measures to promote the sustainable development of the urban ecosystem in Guangzhou. The conclusions of this study can be summarized as follows: (1) The urban ecosystem has developed in an orderly and healthy direction, with effective control over the urban environmental pollution problems in Guangzhou between 2004 and 2010. (2) The sustainable development ability of the urban ecosystem had been on an upward trend in Guangzhou during the study period. The ability of the natural urban ecosystem to support the urban socioeconomic ecosystem increased continuously, and the improved ecoenvironment enhanced the harmony and vitality of the urban ecosystem in Guangzhou.     

生态阻力面模型的构建及其在城市扩展模拟中的应用（英文）

Urban expansion models are useful tools to understand urbanization process and have been given much attention.However,urban expansion is a complicated socio-economic phenomenon that is affected by complex and volatile factors involving in great uncertainties.Therefore,the accurate simulation of the urban expansion process remains challenging.In this paper,we make an attempt to solve such uncertainty through a reversal process and view urban expansion as a process wherein the urban landscape overcomes resistance from other landscapes.We developed an innovative approach derived from the minimum cumulative resistance(MCR) model that involved the introduction of a relative resistance factor for different source levels and the consideration of rigid constraints on urban expansion caused by ecological barriers.Using this approach,the urban expansion ecological resistance(UEER)model was created to describe ecological resistance surfaces suitable for simulating urban expansion and used to simulate urban expansion in Guangzhou.The study results demonstrate that the ecological resistance surface generated by the UEER model comprehensively reflects ecological resistance to urban expansion and indicates the spatial trends in urban expansion.The simulation results from the UEER-based model were more realistic and more accurately reflected ecological protection requirements than the conventional MCR-based model.These findings can enhance urban expansion simulation methods.