Although ecosystems are valuable, they have been allowed to deteriorate globally in recent decades. However, the spatiotemporal changes in ecosystem-service values (ESVs) and their hotspots in China ...are not well understood. Here, long-term land-cover data, the spatial analysis method and an econometric analysis model were used to examine these changes. The results indicate that the total terrestrial ESV decreased from US$2398.31 billion in 1990 to US$2347.56 billion in 2010 (converted to 2009 dollar values), which provides strong evidence for the tendency of ecosystems in China to deteriorate over time, albeit slightly. We also found that the changes in ESVs had significant spatial heterogeneity. Our analysis showed that the relationship between ESV and gross domestic product (GDP) is generally negative, but this relationship is not always fixed. The Loess Plateau, Guizhou, Hubei, Henan and Xinjiang continually presented concentrated hotspot areas of ESV changes, whereas coastal regions continually presented concentrated cold-spot areas. Overlap analyses and logistic regressions demonstrate that national ecological programs have clear effects on the improvement of ecosystems but that the effectiveness of different policies varies on spatial and temporal scales. The results of this study will support more effective decision-making around the implementation of ecological conservation policies.
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•We examine changes in terrestrial ecosystem services values and their hotspots in China.•The tendency of ecosystems in China to gradually deteriorate.•The variations in terrestrial ESV change have spatial heterogeneity.•Ecological conservation programs have a positive ecological effect on China's ecosystems.
Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse ...resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km2 in 1990 to 747.05 ± 1.50 thousand km2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper.
•Multi-temporal global urban land maps at 30-m resolution are presented.•Google Earth Engine Platform is utilized for global urban land classifications.•The resulting global urban land has overall accuracy of 0.81–0.84.
•Urbanisation, energy consumption, and CO2 emissions relationship is investigated.•We present a panel model and a reduction potential analysis for China’s provinces.•Emissions of provinces in east ...region are much higher than that in central and west regions.•The three variables are found to have a positive bi-directional long run relationship.•Whilst China’s CO2 emissions will increase up to 2020, the potential for reductions is great.
Global warming resulting from rapid economic growth across the world has become a worldwide threat. The coordination of development of urbanisation, energy consumption, and carbon dioxide (CO2) emissions therefore forms an important issue; it has attracted considerable attention from both governments and researchers in recent years. This study investigated the relationship between urbanisation, energy consumption, and CO2 emissions over the period 1995–2011, using a panel data model, based on the data for 30 Chinese provinces. The potential to reduce CO2 emissions was also analysed. The results indicated that per capita CO2 emissions in China were characterised by conspicuous regional imbalances during the period studied; in fact, per capita CO2 emissions decrease gradually from the eastern coastal region to the central region, and then to the western region. Urbanisation, energy consumption, and CO2 emissions were found to present a long run bi-directional positive relationship, the significance of which was discovered to vary between provinces as a result of the scale of their respective economies. In addition, a bi-directional causal relationship was found to exist between urbanisation, energy consumption, and CO2 emissions: specifically, a bi-directional positive causal relationship exists between CO2 emissions and urbanisation, as well as between energy consumption and CO2 emissions, and a one way positive causal relationship exists from urbanisation to energy consumption. Scenario simulations further demonstrated that whilst China’s per capita and total CO2 emissions will increase continuously between 2012 and 2020 under all of the three scenarios developed in this study, the potential to achieve reductions is also high. A better understanding of the relationship between urbanisation, energy consumption, and CO2 emissions will help China to realise the low-carbon economic development.
•The total amount of SO2 emissions was selected as the study object.•Three direct factors driving the emissions were detected.•Total energy consumption increased the emissions most.•Treatment ...technology inhibited the emissions most.•Lowering energy use and updating energy structure would have great potential.
Air pollution is increasingly a focus of concern worldwide due to its adverse impacts on human health and profound influences on global ecosystem. Although the existing studies have paid much attention to the causes of pollutant emissions, they fail to distinguish between direct and indirect factors, yielding the mixed results. Direct causes denote energy-related factors, as air pollutants are mainly produced by energy utilization directly, while indirect elements refer to socio-economic factors, as these factors act on pollutant emissions through affecting energy-related aspects. This paper investigated the impacts of three dominant direct factors: total energy consumption (EC), energy structure (ES) and treatment technology (TT) on sulfur dioxide (SO2) emissions in China during 1995–2014 using the logarithmic mean Divisia Index method. Distinguished from the previous studies which took particular interest in SO2 emissions from the industrial sector, this study put the total amount of SO2 emissions as the target. The results show that increased EC was the main reason for SO2 enhancement, while increasingly advanced TT played a dominant role in inhibiting the emissions throughout the study period. In contrast, ES had an unusually slight effect on SO2 emissions due to its minor variation in the meantime. On regional scale, the differences in relative contribution rates (RCRs) of EC, ES and TT among the eastern, central and western regions all gradually decreased over time; EC in central region had the largest improved effect, ES in eastern region held the greatest reduction effect, and TT in western region got the biggest inhibitory effect. At provincial level, most provinces (60%) had relatively quick EC growth and slow ES adjustment (i.e., reducing the coal consumption rate); only Beijing, Tianjin, Shanghai and Sichuan had a relatively slow growth of EC and quick decrease in the percentage of coal consumption. Further, the projection of SO2 emissions in four scenarios from 2015 to 2020 based on a grey projection model indicated that controlling both EC and ES would be the most efficient approach to SO2 abatement followed by individually controlling EC and ES.
Land urbanization plays an important supporting and restriction role in the rapid and sustainable development of urbanization in China, and it shows distinctive spatial heteroge- neity. Applying ...urban area as the basic research unit and urban construction land area as the core indicator, this paper establishes the conceptual framework and calculation method for the quantity and rate of land urbanization process. The study evaluates the spatial differen- tiation pattern of absolute and relative process of land urbanization in 658 cities in China from 2000 to 2010. The spatial distribution of cities with rapid land urbanization process is dis- cussed, and the contribution rate and its spatial heterogeneity of major land use types are examined with the aid of GIS. The main conclusions are as follows： （1） Land urbanization in China shows a clear spatial difference. The greater the city scale, the faster its land urbani- zation. The cities with rapid land urbanization show a significant pattern of central distribution in coastal regions and a scattered distribution in the inland regions. （2） Over the last 10 years, the average quantity of land urbanization in the 656 cities was 3.82 km2, the quantity of land urbanization is differentiated by administrative grade. The average rate of land urbanization was 6.89%, obviously faster than the speed of population urbanization. The rate of land ur- banization reveals a pattern of differentiation between coastal and other cities. （3） In the past 10 years, the two primary land use types associated with land urbanization in China are residential and industrial, with a combined contribution rate of 52.49%. The greater the scale of the city, the more significant the driving effect of industrial land. In small- and medium-scale cities of the western and central regions, the growth of residential land is the primary driver of land urbanization, while in coastal urban agglomerations and cities on important communica- tion axes, the growth of industrial land is the main driver. （4） Overall, urban population ag- glomeration, industrial growth and investment are the three drivers of land urbanization in China, but cities of different scales have different drivers.
To reduce carbon dioxide (CO2) emissions attributed widely to human activities, previous studies have paid great attention to the relationships between socioeconomic development, urban forms and CO2 ...emissions in cities, and provided relevant emission mitigation policies through the effective urban spatial planning. However, whether and how different features of urban forms (such as compactness) affecting the levels of CO2 emissions is still debatable, specifically considering the different development levels of the cities. Therefore, this study is to synthetically explore how socioeconomic factors and urban forms work together to affect CO2 emissions with the consideration of differences in development levels of five city tiers in China. First, CO2 emissions in each city were derived from provincial energy statistics, radiance-calibrated nighttime light imageries, and population distribution data based on a disaggregating model. Then, a set of variables representing socioeconomic factors and urban forms were acquired from the city statistics and land use data, respectively. After obtaining the balanced dataset of these five city tiers from 1995 to 2015, the panel data analysis was finally applied to evaluate the consequences of socioeconomic factors and urban forms on CO2 emissions under different development stages. The estimation results show that the economic development, population growth, and urban land expansion are important factors that accelerating CO2 emissions in all the city tiers. Besides, irregular or fragmented structures of urban land use could result in more CO2 emissions due to the increase in potential transportation requirements in all the city tiers. Notably, an increasing concentrated pattern in the urban core is found to increase CO2 emissions in the tier-one cities, but to promote the reduction of CO2 emissions in other four city tiers. The urban spatial development with a compact and multiple-nuclei pattern is suggested to be closely linked with a lower level of CO2 emissions. Such results highlight the importance of a city's development level for decision-making involving the mitigation of CO2 emissions, and provide scientific support for building a low-carbon city from the perspective of both socioeconomic development and urban spatial planning.
•Differentiated impacts of socioeconomic factors and urban forms on CO2 emissions are explored.•Socioeconomic growth and urban land expansion promote CO2 emissions in all city tiers.•Urban forms in different development levels have different impacts on CO2 emissions.•Fragmented patterns of urban land can result in more CO2 emissions in all city tiers.•Compact and centralized developments do little to reduce emissions in tier-one cities.
Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use ...is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human-environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-km resolution model proposed through this article. These distortions occurred because a large percentage of small areas of urban land was merged into other land-use classes. During land-use change simulation, a majority of small urban clusters were also lost using the IMAGE product. Responding to these deficiencies, the 1-km FLUS product developed in this study is able to provide the spatial detail necessary to identify spatial heterogeneous land-use patterns at a global scale. We argue that this new global land-use product has strong potential in radically reducing uncertainty in global environmental modeling.
•The paper estimated the direction and strength of driving factors on PM2.5 concentrations in Chinese cities.•A geographically and temporally weighted regression model (GTWR) was adopted in this ...study.•Higher temperature and wind speed were found to alleviate air pollution in the southeast China.•Per capita GDP and population density were shown to intensify PM2.5 pollution in northwest China.•Urban built-up area was more positively associated with PM2.5 pollution in southeast than in other cities in China.
Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change. Objective: Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature.
In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015.
Cities with serious PM2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country’s southeast, where enhanced precipitation was also shown to reduce PM2.5 concentrations; whilst in southern and central and western regions, precipitation and PM2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM2.5 concentration levels in southeastern cities than in other cities in China.
PM2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.
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.
China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the ...attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models—the ordinary least square model, the spatial lag model, and the spatial error model—as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO2 concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO2 reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO2 concentrations by 0.026μg/m3. Both emission and temperature factors were found to aggravate SO2 concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO2 and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO2 pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO2 pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China.
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•Both source and diffusion factors were used to detect their impacts on SO2 pollution.•Emissions and meteorological conditions were seen as the two types of drivers.•A regional increase of 1mm in precipitation could reduce SO2 by 0.026μg/m3 in China.•Increased emissions could produce more pollution in the northwest than in others.•Higher temperature inhibited SO2 in the southeast, but opposite in the northwest.
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