Table of Content

    25 May 2020, Volume 30 Issue 5 Previous Issue    Next Issue
    Research Articles
    Beautiful China Initiative: Human-nature harmony theory, evaluation index system and application
    FANG Chuanglin, WANG Zhenbo, LIU Haimeng
    2020, 30 (5):  691-704.  doi: 10.1007/s11442-020-1750-7
    Abstract ( 166 )   HTML ( 5 )   PDF (3207KB) ( 13 )   Save

    The Beautiful China Initiative (BCI) is a plan for the sustainable development of the Chinese nation as well as for China to fulfill the United Nations’ 2030 Agenda for Sustainable Development. The Chinese government’s “five-in-one” approach provides strategic arrangements for developing the BCI, and President Xi Jinping proposed a timetable and “road map” for the BCI at the National Conference on Ecological and Environmental Protection. Nevertheless, the theoretical basis, evaluation index system, evaluation criteria and effectiveness of the BCI are currently unclear. This paper begins by exploring the basic content of the BCI from narrow and broad perspectives. It regards the theory of human-nature harmonious coexistence and the five-in-one beauty theory as the core theoretical bases of the BCI and constructs a five-element BCI evaluation index system (ecological environment, green development, social harmony, system perfection and cultural heritage) and utilizes the assessment method of the United Nations’ Human Development Index to assess scientifically the effectiveness of the BCI in 341 prefecture-level cities. The results show the average BCI index (the Chinese Academy of Sciences Beauty Index) score to be 0.28, which is quite low, while the average scores for the individual element indexes of the ecological environment index, green development index, social harmony index, system perfection index and cultural heritage index are 0.6, 0.22, 0.29, 0.22 and 0.07, respectively. All of these are relatively low values, with relatively large discrepancies in regional development, indicating that progress in the BCI is generally slow and unbalanced. To realize the BCI’s timetable and roadmap to a high quality and high standard, it is suggested that a common system for evaluating the progress of the BCI is developed and promulgated so that dynamic monitoring and phased evaluations can take place; BCI technical assessment standards are compiled and published; BCI comprehensive zoning is undertaken; pilot projects adapted to local conditions are launched in BCI sample areas; and BCI results are incorporated into performance indicators at all levels of government.

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    Geographical thoughts on the relationship between ‘Beautiful China’ and land spatial planning
    CHEN Mingxing, LIANG Longwu, WANG Zhenbo, ZHANG Wenzhong, YU Jianhui, LIANG Yi
    2020, 30 (5):  705-723.  doi: 10.1007/s11442-020-1751-6
    Abstract ( 119 )   HTML ( 4 )   PDF (1926KB) ( 18 )   Save

    The concept of ‘Beautiful China’ is a new goal of ecological construction in the new era of socialism and aims to meet the needs of people as they strive for a better life. National land spatial planning is one major component of the Chinese state’s overall planning for various spatial types. The concept of ‘Beautiful China’ is thus a leading goal of Chinese development in the second centenary. The background of this concept aims for ‘ecological beauty’ as well as the combined beauty of ‘economy-politics-culture-society-ecology.’ The construction of ‘Beautiful China’ therefore necessitates a differentiated evaluation index system that is built on the basis of local conditions. This concept is intimately related to land spatial planning and the idea of Beautiful China guides an important direction for this planning which itself provides an important mechanism and spatial guarantee for construction. The establishment of land spatial planning nevertheless needs to strengthen further discussion of the regional system of human-land relationship, point axis system, main functional division, sustainable development, resources and environmental carrying capacity as well as new urbanization, and the rural multi-system. The aim of this paper is to summarize current thinking in land spatial planning, scientifically analyze the natural geographical conditions, the socioeconomic development, the interrelationship of the land space, plan the goal, vision and path of land space, encourage the public to participate in and carry out dynamic evaluation, build an intelligent system platform for land and spatial planning to realize the goal of ‘Beautiful China’ from a geographical perspective. And they can also present key ideas relating to the compilation and implementation of land spatial planning.

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    Spatio-temporal evolution and influencing factors of urban green development efficiency in China
    ZHOU Liang, ZHOU Chenghu, CHE Lei, WANG Bao
    2020, 30 (5):  724-742.  doi: 10.1007/s11442-020-1752-5
    Abstract ( 127 )   HTML ( 2 )   PDF (876KB) ( 12 )   Save

    To resolve conflicts between development and the preservation of the natural environment, enable economic transformation, and achieve the global sustainable development goals (SDGs), green development (GD) is gradually becoming a major strategy in the construction of an ecological civilization and the ideal of building a “beautiful China”, alongside the transformation and reconstruction of the global economy. Based on a combination of the concept and implications of GD, we first used the Slacks Based Model with undesirable outputs (SBM-Undesirable), the Theil index, and the spatial Markov chain to measure the spatial patterns, regional differences, and spatio-temporal evolution of urban green development efficiency (UGDE) in China from 2005 to 2015. Second, by coupling natural and human factors, the mechanism influencing UGDE was quantitatively investigated under the framework of the human-environment interaction. The results showed that: (1) from 2005 to 2015, the UGDE increased from 0.475 to 0.523, i.e., an overall increase of 10%. In terms of temporal variation, there was a staged increase, with its evolution having the characteristics of a “W-shaped” pattern. (2) The regional differences in UGDE followed a pattern of eastern > central > western. For different types of urban agglomeration, the UGDE had inverted pyramid cluster growth characteristics that followed a pattern of “national level > regional level > local level”, forming a stable hierarchical scale structure of “super cities > mega cities > big cities > medium cities > small cities”. (3) UGDE in China has developed with significant spatial agglomeration characteristics. High-efficiency type cities have positive spillover effects, while low-efficiency cities have negative effects. Different types of urban evolution processes have a path dependence, and a spatial club convergence phenomenon exists, in which areas with high UGDE are concentrated and drive low UGDE elsewhere. (4) Under the framework of regional human-environment interaction, the degree of human and social influence on UGDE is greater than that of the natural background. The economic strength, industrial structure, openness, and climate conditions of China have positively promoted UGDE.

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    Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm
    TANG Zhipeng, MEI Ziao, LIU Weidong, XIA Yan
    2020, 30 (5):  743-756.  doi: 10.1007/s11442-020-1753-4
    Abstract ( 82 )   HTML ( 1 )   PDF (685KB) ( 299 )   Save

    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.

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    Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends
    WANG Shaojian, GAO Shuang, HUANG Yongyuan, SHI Chenyi
    2020, 30 (5):  757-774.  doi: 10.1007/s11442-020-1754-3
    Abstract ( 114 )   HTML ( 0 )   PDF (1602KB) ( 4 )   Save

    Climate change resulting from CO2 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.

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    Spatio-temporal pattern and driving forces of urbanization in China’s border areas
    SONG Zhouying, ZHU Qiaoling
    2020, 30 (5):  775-793.  doi: 10.1007/s11442-020-1755-2
    Abstract ( 98 )   HTML ( 4 )   PDF (2274KB) ( 10 )   Save

    Border area is not only an important gateway for inland opening-up, but also an important part of completing the building of a moderately prosperous society and optimizing national urban spatial pattern in China. Due to the location, natural resources endowment, and traffic accessibility, the urbanization speed is relatively slow in border areas. Therefore, it is a special area that needs to pay close attention to, especially under the background of the Belt and Road Initiative and China’s regional coordinated development program. Based on the county-level data from 2000 to 2015, this paper tries to analyze the spatio-temporal pattern of urbanization in 134 border counties, and applies geographical detector method to study the driving forces of urbanization in border areas. Conclusions are as follows: (1) From 2000 to 2015, urbanization rate in border areas has been lower than the national average, and the gap has been widening. Some border counties in southern Xinjiang, Tibet, northeast of Inner Mongolia, and Yunnan, are even facing the problem of population loss. (2) In the same period, urbanization rate in the northwestern and southwestern border is low, while their urbanization rate grows relatively faster comparing with other border counties; urbanization rate in Tibet border is the lowest and grows relatively slowly; urbanization rate in the northeastern and northern border is slightly higher, but it grows slowly or even stagnates. (3) Transportation and industry are the important driving forces of urbanization in border areas, while the driving forces of market is relatively weak. And there are obvious mutual reinforcements among the driving forces, while the effort and explanatory power of resource force increases obviously after interaction. (4) Urbanization rate in the northwestern and southwestern border areas grows relatively fast, with industrial force and transportation force, market force and administrative force as the main driving forces respectively. Tibet border area has the lowest urbanization rate and growth rate, as the driving force of urbanization with strong contribution has not yet formed in Tibet. In the northeastern and northern border areas, the contribution of transportation force to urbanization is greater than other forces, and its interaction with market and industry has obvious effects.

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    A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan
    2020, 30 (5):  794-822.  doi: 10.1007/s11442-020-1756-1
    Abstract ( 101 )   HTML ( 2 )   PDF (8028KB) ( 7 )   Save

    Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners. This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts. Since 2005, the Ministry of Land, Infrastructure, and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations. Although this data is useful, it does not provide complete information at every site for all market participants. Therefore, estimating and mapping land prices based on sound statistical theories is required. This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms. Land use, elevation, and socioeconomic factors, including population density and distance to railway stations, were used for modeling. Results show the superiority of the random forest algorithm. Overall, land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.

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    Analysis of critical river discharge for saltwater intrusion control in the upper South Branch of the Yangtze River Estuary
    SUN Zhaohua, FAN Jiewei, YAN Xin, XIE Cuisong
    2020, 30 (5):  823-842.  doi: 10.1007/s11442-020-1757-0
    Abstract ( 97 )   HTML ( 0 )   PDF (1785KB) ( 3 )   Save

    Saltwater intrusion in the estuary area threatens the use of freshwater resources. If river discharge increases to a critical value, then saltwater intrusion frequency and salinity level decreases. In this study, long-term river discharge and tidal range data in the Yangtze River Estuary (YRE) and salinity data obtained in the upper South Branch of the YRE were used to analyze the characteristics of different variables and the basic law of their interactions. Two methods, namely, the material analysis method and empirical models, were applied to determine the critical river discharge for saltwater intrusion control. Results are as follows: (1) the salinity might exceed the drinking water standard of China when the river discharge was less than 30,000 m3/s. Approximately 69% of salinity excessive days occurred when the river discharge was less than 15,000 m3/s; (2) the tidal range in the YRE roughly varied in sinusoidal pattern with a 15-day cycle length. Exponential relationship existed between daily salinity (chlorinity) and daily mean tidal range. Combining these two features with the cumulative frequency statistics of tidal ranges, it was showed that notable saltwater intrusion occurred when the tidal range was more than 2.7 m at Qinglonggang station. Moreover, the critical discharge was found to be slightly higher than 11,000 m3/s; (3) various of empirical models for salinity prediction could be chosen to calculate the critical discharge. The values obtained by different models were in the range of 11,000-12,000 m3/s; (4) the proposed critical discharge to reduce notable saltwater intrusion was 11,500 m3/s. After the Three Gorges Reservoir operation, the minimum river discharge into the YRE in 2008-2017 was below the critical discharge, thereby suggesting an increase in the minimum river discharge by reservoir regulation in drought periods.

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    Simulation on the stochastic evolution of hydraulic geometry relationships with the stochastic changing bankfull discharges in the Lower Yellow River
    SONG Xiaolong, ZHONG Deyu, WANG Guangqian
    2020, 30 (5):  843-864.  doi: 10.1007/s11442-020-1758-z
    Abstract ( 59 )   HTML ( 0 )   PDF (3447KB) ( 5 )   Save

    Extreme weather is an important noise factor in affecting dynamic access to river morphology information. The response characteristics of river channel on climate disturbances draw us to develop a method to investigate the dynamic evolution of bankfull channel geometries (including the hydraulic geometry variables and bankfull discharges) with stochastic differential equations in this study. Three different forms of random inputs, including single Gaussian white noise and compound Gaussian/Fractional white noise plus Poisson noise, are explored respectively on the basis of the classical deterministic models. The model parameters are consistently estimated by applying a composite nonparametric maximum likelihood estimation (MLE) method. Results of the model application in the Lower Yellow River reveal the potential responses of bankfull channel geometries to climate disturbances in a probabilistic way, and, the calculated average trends mainly run to synchronize with the measured values. Comparisons among the three models confirm the advantage of Fractional jump-diffusion model, and through further discussion, stream power based on such a model is concluded as a better systematic measure of river dynamics. The proposed method helps to offer an effective tool for analyzing fluvial relationships and improves the ability of crisis management of river system under varying environment conditions.

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