Proportion Of Secondary Industry Research Articles

Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China—New evidence using the dynamic threshold panel model

Since China’s reform and opening up, the urban economy has substantially developed, and the nation’s urbanization process has advanced to gradually enter a “new normal” phase. In this process, China’s population and industries continue to inundate its cities, and environmental pollution has become a critical problem. This study uses panel data spanning 1998 to 2015 from 29 Chinese provinces and a comprehensive framework of well-designed generalized method of moments (GMM) models to accurately examine the relationships between urbanization and per capita sulfur dioxide (PSO2), per capita industrial soot (PSOOT), and per capita industrial waste gas (PIWG). A newly developed dynamic threshold panel model that incorporates GMM characteristics is utilized to explore how urbanization affects environmental pollution under different industrial structures. This study finds evidence of a comprehensive relationship among urbanization development, the industrial structure, and environmental pollution. Urbanization has contributed to increased environmental pollution. A nonlinear relationship exists between urbanization and environmental pollution in China. Further, urbanization increasingly promotes environmental pollution with an increase in the proportion of secondary industries; however, such promotion decreases with an increase in the proportion of tertiary industries.

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing-Tianjin-Hebei Region, China.

Heavy-duty diesel trucks (HDDTs) contribute significantly to NOX and particulate matter (PM) pollution. Although existing studies have emphasized that HDDTs play a dominant role in vehicular pollution, the spatial distribution pattern of HDDT emissions and their related socioeconomic factors are unclear. To fill this research gap, this study investigates the spatial distribution pattern and spatial autocorrelation characteristics of NOX, PM, and SO2 emissions from HDDTs in 200 districts and counties of the Beijing–Tianjin–Hebei (BTH) region. We used the spatial lag model to calculate the significances and directions of the pollutants from HDDTs and their related socioeconomic factors, namely, per capita GDP, population density, urbanization rate, and proportions of secondary and tertiary industries. Then, the geographical detector technique was applied to quantify the strengths of the significant socioeconomic factors of HDDT emissions. The results show that (1) NOX, PM, and SO2 pollutants emitted by HDDTs in the BTH region have spatial heterogeneity, i.e., low in the north and high in the east and south. (2) The pollutants from HDDTs in the BTH region have significant spatial autocorrelation characteristics. The spatial dependence effect was obvious; for every 1% increase in the HDDT emissions in the surrounding districts and counties, the local HDDT emissions increased by 0.39%. (3) Related factors analysis showed that the proportion of tertiary industries had a significant negative correlation, whereas the proportion of secondary industries and urbanization rate had significant positive correlations with HDDT emissions. Population density and per capita GDP did not pass the significance test. (4) The order of effect intensities of the significant socioeconomic factors was proportion of tertiary industry > proportion of secondary industry > urbanization rate. This study guides scientific decision making for pollution control of HDDTs in the BTH region.

Ecological Security Assessment Based on Ecological Footprint Approach in Hulunbeir Grassland, China.

Hulunbeir grassland, as a crucial ecological barrier and energy supply base in northwest China, suffers from a fragile ecological environment. Therefore, it is crucially important for Hulunbeir grassland to achieve the sustainable development of its social economies and ecological environments through the evaluation of its ecological security. This paper introduces the indexes of the ecological pressure index (EPI), ecological footprint diversity index (EFDI), and ecological coordination coefficient (ECC) based on the ecological footprint model. Furthermore, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was applied to analyze the main driving factors of the change of the ecological footprint. The results showed that: The ecological footprint (EF) per capita of Hulunbeir grassland has nearly doubled in 11 years to 11.04 ha/cap in 2016, while the ecological capacity (EC) per capita was rather low and increased slowly, leading to a continuous increase of per capita ecological deficit (ED) (from 5.7113 ha/cap to 11.0937 ha/cap). Within this, the footprint of fossil energy land and grassland contributed the most to the total EF, and forestland and cropland played the major role in EC. The EPI increased from 0.82 in 2006 to 1.25 in 2016, leading the level of ecological security to increase from level 3 (moderately safe) to level 4 (moderately risky). The indexes of the EFDI and ECC both reached a minimum in 2014 and then began to rise, indicating that Hulunbeir steppe’s ecological environment, as well as its coordination with economy, was considered to be worse in 2014 but then gradually ameliorated. The STIRPAT model indicated that the main factors driving the EF increase were per capita GDP and the proportion of secondary industry, while the decrease of unit GDP energy consumption played an effective role in curbing the continuous growth of the EF. These findings not only have realistic significance in promoting the coordinated development between economy and natural resource utilization under the constraint of fragile environment, but also provide a scientific reference for similar energy-rich ecologically fragile regions.

How does urbanization affect carbon emission intensity under a hierarchical nesting structure? Empirical research on the China Yangtze River Delta urban agglomeration.

Urbanization is an important direction for China's future social development and an important focus of its carbon emission reduction path. China's current administrative management is a vertical nested structure, and the characteristics of high-scale regions have a non-negligible impact on low-scale areas. Taking the county scale of the basic unit of economic and social development as the research scale, according to the panel data of the Yangtze River Delta from 2008 to 2016, a two-level hierarchical linear model (HLM) for carbon emission intensity is constructed, especially considering the characteristics of high-scale regions (i.e., low-carbon pilot cities) at the second level, and is combined with the mediating effect test method to analyze the impact path of urban development on carbon emissions intensity. The results show that (1) there is a spatial nesting relationship between regions of different scales, and the city scale can explain 85.21% of the carbon emissions intensity, which is much higher than the county scale. (2) There is an N-shaped curve relationship between urban development and carbon emissions intensity. After considering the high-scale factor (low-carbon pilot cities) at the city scale (the second level of the HLM), if a high-scale city is a low-carbon pilot city, then improvement in the level of urbanization in the county can promote a reduction in carbon intensity. (3) The impact path of urban development ⇄ per capita gross domestic product (the proportion of secondary industry, patent application volume) → carbon emissions intensity and urban development → the proportion of tertiary industry → carbon emissions intensity is significant. However, the path of the proportion of tertiary industry → urban development → carbon emissions intensity is not significant.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis

This study estimates disparities in carbon intensity in China using a multi-scalar and multi-mechanism analysis. In order to avoid the inconsistency between regional, provincial, and city-level data, city-level CO2 emissions from energy consumption in China were estimated through Defense Meteorological Satellite Program/Operational Linescan System nighttime light imagery. Our results reveal a trend of decreasing inequality in carbon intensity in China, and the study also found that the contribution made by eastern China to that inequality decreased continuously, while the share of inequality in western China increased consistently during the study period. Spatial Markov chains were also applied to identify the spatiotemporal dynamics of inequalities in Chinese cities. The results show that there is a strong effect of the emission status of neighboring cities’ on a city’s emission dynamics and the effect of self-reinforcing agglomeration was significant. Based on a multi-level model, the study further revealed that the disparity in China’s carbon intensity levels was sensitive to the regional hierarchy across a variety of mechanisms acting as potential influencing factors. We found that technological progress and population density have a potential to mitigate the intensities driven by economic development, trade openness, road density, secondary industry proportion, and investment intensity. Through the present study, we argue that the policies targeting emissions mitigation in China have been restrained due to a lack of effective restraint in relation to the influencing factors that have promoted emissions levels, while mitigation factors have not been adequately exploited.

What is the developmental level of outlying expansion patches? A study of 275 Chinese cities using geographical big data

New outlying development zones are a common phenomenon in the development of Chinese cities, and exploring their development level is a matter of great interest. Using geographic big data that cover the entire country, this paper analyzes the urban development level (UDL) of newly added outlying patches within 275 cities at the prefecture level or above in China from 2005 to 2015. The results revealed the following. 1) High-value cities are primarily located in the east, whereas low-value cities are primarily located in the west. The Beijing-Tianjin-Hebei urban agglomeration (BTHUA) is a major area with low UDLs, whereas the Yangtze River Delta urban agglomeration (YRDUA) and the Pearl River Delta urban agglomeration (PRDUA) are major areas with high UDLs. 2) The results of a multivariate regression demonstrate that GDP per capita (GDPPC), average elevation (AE), the distance between the new outlying urban patches, the proportion of secondary industry in GDP (PSI), the average land price in the city (ALP), the original urban land areas (DIS), and economic balance (GDPSD) significantly affect the UDL values of these cities. Relatively higher GDPPC and ALP are associated with a significant increase in the UDL, while more AE, DIS, GDPSD and PSI will decrease the UDL. The results of this paper provide a reference to understand the conditions of outlying developmental zones in China over the past 10 years as well as to explore healthy and reasonable urban spatial expansion forms.

Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster

Global warming and climate change have become a serious environmental problem and China's carbon emissions are currently the highest in the world. Cities are the main sources of carbon emissions and the key to solving these problems. Therefore, research on reducing carbon dioxide emissions is a matter of concern. In this study, a spatial autocorrelation analysis was performed to understand the spatial characteristics of carbon dioxide emissions in 171 Chinese cities. Then, stepwise and geographically weighted regressions were used to explore the processes that drive carbon dioxide emissions in Chinese cities. A two-step cluster was used to classify Chinese cities into different categories based on the degree of impact of each driver. The results showed that there is a spatial aggregation relationship between urban carbon dioxide emissions. High-high clusters mainly occur in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations, while low-low clusters occur in the central, western, and southwestern cities. Among all variables, freight volume, per capita gross domestic product, population density, and the proportion of secondary industries correlate positively with carbon dioxide emissions, whereas the number of buses per 10,000 people correlates negatively with carbon dioxide emissions. The geographically weighted regression model provided more detailed results and revealed the spatial heterogeneity of the effects of the different drivers. The impact of population, economic factors, and industrial factors in the eastern region is significantly greater than that in the central and western regions. Freight volume and public transport have the most significant impact in the northeast region. The clustering results showed that cities can be divided into four types. These findings provide a reference and policy suggestions for how cities in different regions should reduce carbon dioxide emissions.

The influence of market reform on the CO2 emission efficiency of China

In recent years, the constant advancement of market reform in China improves the market allocation efficiency of production factors and further promotes the rapid growth of economic aggregate output in the country. Additionally, the CO2 emitted in China also constantly increases. Based on panel data pertaining to 30 provinces from 1998 to 2015 in China, the CO2 emission efficiency and marketisation level of China were separately measured using a super Slacks-based measure approach and generalised principal component analysis. On this basis, by utilising a dynamic panel regression model and a panel smooth transition regression model, the influence of market reform on CO2 emission efficiency was explored. The results show that the marketisation level exerts a significantly positive effect on CO2 emission efficiency in both models and the results are robust. On the condition of taking energy price as a threshold variable, the influence of process of marketisation is significantly greater than that when taking technological progress as a threshold variable. It implied that although energy price and technological progress both significantly promote the marginal effect of market reform on CO2 emission efficiency, the energy price shows a greater influence than technological progress. Infrastructure construction and urbanisation can inhibit the improvement of CO2 emission efficiency. Additionally, the proportion of secondary industries can delay the improvement of CO2 emission efficiency.

Nonlinear Effects of Environmental Regulation on Environmental Pollution

This paper classifies environmental regulation into two types and constructs a theoretical framework to explore the influences of fee-based environmental regulation and invest-based environmental regulation on environmental pollution. It then establishes some dynamic spatial autoregressive nonlinear econometric models to test the theoretical hypothesis based on 30-area panel data from 2004 to 2016. The results illustrate that inverted “U” shape curve relationship exists between fee-based environmental regulation and environmental pollution, while a “U” shape curve relationship between invest-based environmental regulation and environmental pollution exists. In addition, the findings suggest that improving the proportion of secondary industry can directly promote the environmental quality while effectively control of foreign direct investment and fiscal decentralization is also indispensable. Thus, the government should make targeted research about the optimal intensity of fee-based environmental regulation and invest-based environmental regulation and make targeted enterprise policy for the environmental pollution reduce, which contains promoting the energy revolution and strengthening the depth and strength of opening-up step by step.

Regional Economic Resilience of the Old Industrial Bases in China—A Case Study of Liaoning Province

Regional economic resilience provides a new perspective for explaining regional differences in response to recession shocks. This paper analyzed the regional economic resilience of Liaoning Province in China in terms of its resistance and recoverability dimensions from 1990 to 2015 and explored the determinants of regional economic resilience by constructing a spatial econometric model using panel data. There are three main findings. First, the level of regional economic resilience in Liaoning was low and its urban economy was more vulnerable to external shocks than that of the whole nation. Second, we found that factors such as the local governance arrangement, the regional innovation ability, and the level of economic diversification have significant positive effects on regional economic resilience. Factors such as the proportion of secondary industry and the GDP negatively affect regional economic resilience. Third, regional economic resilience demonstrates a spatial autocorrelation, and the improvement of economic resilience in the surrounding areas will lead to the decline of regional economic resilience.

Effective supply of eco-product and its influence factors at provincial scale of China from the perspective of supply-side reform.

Eco-product is scarce product with consequences on human health, economy and society all over the world. In order to understand the effective supply of eco-product and figure out its law of scale efficiency, panel data from 2004 to 2015 with labor force, capital, resource, and energy source was carefully selected as the input variables and GDP, sustainable product, and undesirable output as the output variables, the scale efficiency and its differences of effective supply of eco-product in China were measured in this study. SBM-Undesirable model, Theil index, and the model of Tobit were used to analyze the driving factors. The results showed that there was a downward trend in scale efficiency of China's eco-products before 2011, and an upward trend with obvious inter-regional differences since 2012. The influence factors of supply efficiency of eco-product greatly varied among different regions. Overall, some factors had negative influences, including the values of economic development level, industrial structure (the proportion of secondary industry's increase in GDP), science and technology input, financial capital input (the proportion of environment pollution control investment in GDP), energy consumption structure (the proportion of coal consumption in overall energy consumption), and agricultural non-point source pollution. Some factors had positive influences, such as eco-space and foreign trade structure. In Eastern China, factors such as industrial structure, science and technology input, financial capital, energy consumption structure and agricultural non-point source pollution had negative impacts, while the factors of economic development level, eco-space and foreign trade structure had positive impacts. In Northeast China, the factors of economic development level, financial capital, agricultural non-point source pollution and foreign trade structure had negative impacts, while the factors of industrial structure, science and technology input, energy consumption structure and eco-space had positive impacts. In Central China, the factors of economic development level, science and technology input, financial capital, energy consumption structure, agricultural non-point source pollution and eco-space had negative impacts, while the factors of industrial structure and foreign trade structure had positive impacts. In Western China, the factors of industrial structure, energy consumption structure, and agricultural non-point source pollution had negative impacts, while the factors of economic development level, science and technology input, financial capital, eco-space, and foreign trade structure had positive impacts. Based on further theoretical analysis of above-mentioned empirical measurement, some poli-cy implications were put forward for effectively improving the efficiency of eco-product supply from the angle of supply-side reforming, which included emphasizing the transitions mode of sustainable economy, optimizing ecosystem structure, and managing ecosystem as a whole according to natural laws.

Research on the sustainable development of China’s coal cities based on lock-in effect

Contradictions in various coal city aspects have become increasingly prominent, with many cities becoming locked in due to lack of overall planning and resource degradation, and sustainable development of coal cities has attracted widespread attention. However, many research challenges remain regarding breakthrough of coal city lock-in effect. This paper evaluated 43 typical Chinese coal city 2012–2016 lock-in breakthrough abilities considering economy, environment, and sustainable development capacity aspects. We then used the fixed effect model to analyze impact mechanisms for various influencing factors on lock-in breakthrough. We found there was no significant change in overall lock-in breakthrough capacity in recent years. Technological innovation and human resources played important roles in lock-in breakthrough, and higher secondary industry proportion implied poorer ability to lock in breakthroughs. Economic externality and government finance had no significantly affect. Therefore, the government should clarify coal city transformation goals, establish a typical city as a benchmark for others, accelerate alternative industry developments, increase talent introduction and personnel training, and insist that technological innovation solves urban development problems from the root.

The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

Analysis of the Impact of Economic Growth on Urban Land Expansion in Sichuan Province

Based on the panel data of the urban land expansion and economic growth of 18 prefecture- level cities in Sichuan province from 2000 to 2014, this paper analyzes the relationship between the regional economic development and urban land expansion by constructing the econometric model of the area of the built-up area, the economic aggregate and the economic structure variables. The results show that: (1) The evolution law of urban land expansion and economic growth in Sichuan province "N" three times, which basically conforms to the characteristics of Kuznets curve; (2) The change of economic structure will affect the urban land expansion and the area of built-up area - per capita income curve.The increase of the proportion of secondary industry, fiscal expenditure and fiscal revenue will reduce the demand for urban land, so that the area of built-up area - per capita income curve flattens out. On the contrary, the increase in the level of foreign trade, the increase of input-output ratio and the decline of the proportion of the tertiary industry will increase the demand for urban land, making the built-up area - per capita income curve steeper.

Changes in urban air quality during urbanization in China

This paper primarily studies the air quality in China's cities during the urbanization stage. Using a cross-sectional data of 282 cities, we discuss the influencing factors and the existence of the Kuznets Curve for three air pollutants based on the EKC (Environmental Kuznets Curve) theory and the BMA (Bayesian Model Average) method. The results show that the concentration of SO2 and PM10 presents the characteristics of an inverse-U shape. The urbanization process has a significant and negative effect on air pollutant concentration, which means that cities with higher urbanization rate tends to have lower air pollutant concentration. Population density, possession of civil motor vehicles, the proportion of secondary industry, and annual average temperature are the main influencing factors of air pollution. Based on these results, this paper suggests that China should speed up the “new” type urbanization process, constantly optimize the industrial structure, and promote the harmonious development of the economy and environment.

Impacts of GDP, Fossil Fuel Energy Consumption, Energy Consumption Intensity, and Economic Structure on SO2 Emissions: A Multi-Variate Panel Data Model Analysis on Selected Chinese Provinces

Atmospheric pollution gradually become a focus of concern all over the world owing to its detrimental influence on human health as well as long range impact on global ecosystem. This paper investigated the relationship among SO2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of five provinces in China with the highest SO2 emissions spanning from 2002–2015 based on panel data model. Through comparatively analyzing the coefficients in the established panel data model for Hebei, Henan, Inner Mongolia, Shandong, and Shanxi, we can obtain that: (1) fossil fuel energy consumption made the most devotion to SO2 discharge compared with GDP, energy consumption intensity, and economic structure. And the more the fossil fuel energy consumption, the more the devotion made by it to SO2 discharge. (2) GDP devoted less to SO2 emissions than fossil fuel energy consumption, and the larger the scale of the economy, the greater the contribution made by it to SO2 emissions. (3) The higher the proportion of the secondary industry added value accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO2 emissions. Through analyzing the Granger causality examination results, it can be concluded that: (1) there existed a bi-directional causal relationship between fossil fuel energy consumption and SO2 emissions among five selected provinces. (2) There existed uni-directional causal nexus running from GDP to SO2 emissions, from energy consumption intensity to SO2 emissions, and from economic structure to SO2 emissions among five chosen provinces. Based on the empirical analysis, several policy implications were proposed to provide references for policy makers, which were (1) Giving full play to the guiding role of price signals, and improving the price policy for desulfurization. (2) Formulating a new comprehensive evaluation system to measure the regional development level considering economic development and environmental impacts. (3) Exploring renewable and sustainable energy sources to substitute for fossil fuel energy according to regional resources endowment. (4) Developing high value added and low pollution emissions industries and reducing the proportion of secondary industry.

SVR-based prediction of carbon emissions from energy consumption in Henan Province

This paper analyzes the advantage of support vector regression (SVR) in the prediction of carbon emission and establishes the SVR-based carbon emission prediction model. The model is established using the data of Henan’s carbon emissions and influence factors from the 1991 to 2016 to train and test and then predict the carbon emissions from 2017 to 2021. The results show that: from the perspective of carbon emission from energy consumption, it raised 224.876 million tons of carbon dioxide from 1991 to 2016, and the predicted increment from 2017 to 2021 is 30.5563million tons with an average annual growth rate at 3%. From the perspective of growth rate among the six factors related to carbon emissions it is proved that population urbanization rate per capital GDP and energy consumption per unit of GDP influences the growth rate of carbon emissions less than the proportion of secondary industry and coal consumption ratio of carbon. Finally some suggestions are proposed for the carbon emission reduction of Henan Province.

Study on the Influencing Factors of Carbon Intensity Using Skew-normal Mixed Model

This paper firstly applies the EM algorithm and gives the maximum likelihood estimates of unknown parameters in skew-normal mixed model. For empirical analysis, we verify the skew-normal distribution characteristics of provincial carbon intensity data in China from 2000 to 2014. A skew-normal mixed model is then constructed to study the main influencing factors of carbon intensity of China. It is found that energy intensity would have the most significant influence on carbon intensity, among a group of factors including GDP per capita, proportion of secondary industry, and dependence on foreign trade. Finally, the results are compared with those based on normal mixed model, so as to confirm the statistical excellent properties of skew-normal mixed model.

Ecological risk assessment based on freshwater consumption dynamic analysis of Xiamen City, China

ABSTRACTFreshwater is the lifeline of a city. Shortages in urban water supply and ecological losses occur when freshwater supply capacity and demand are imbalanced. Therefore, systematic research on the risk of freshwater consumption in urban areas is urgently demanded. A scientific understanding of the risk of urban water consumption will contribute to the efficient use of freshwater resources and ensure the stability and sustainable development of cities. Taking Xiamen City as the study area, we evaluated the ecological risk of freshwater consumption scenarios in the years 2020 and 2030 using a multilevel characterization method for urban ecological risk, stepwise regression analysis, and a gray prediction model. The results of our evaluation show that freshwater consumption in Xiamen is highly correlated with the total population, the crop acreage, the proportion of secondary industry, and the treatment rate of domestic sewage. In the 2020 and 2030 scenarios, freshwater consumption in Xiamen City is predicted to increase. Meanwhile, with the construction of water conservancy facilities, the supply capacity of freshwater in Xiamen City will be greatly improved. Therefore, the ecological impacts of freshwater consumption in the 2020 and 2030 scenarios were at the middle and low levels. In this study, the validity of the multilevel characterization method described herein for urban ecological risk has been confirmed. However, calculation of scenario probability is a difficult problem in the framework of this method, and future research should address this issue.