Shaojian Wang

A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions

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.

Identifying the socioeconomic determinants of population exposure to particulate matter (PM 2.5 ) in China using geographically weighted regression modeling

Air pollution contributes significantly to premature death in China. However, only a limited number of studies have identified the potential determinants of population exposure to PM2.5 from a socioeconomic perspective. This paper analyses the socioeconomic determinants of population exposure at the city level in China. We first estimated population exposure to PM2.5 by integrating high resolution spatial distribution maps of PM2.5 concentrations and population density, using data for 2013. Then, geographically weighted regression (GWR) modeling was undertaken to explore the strength and direction of relationships between the selected socioeconomic factors and population exposure. The results indicate that approximately 75% of the population of China lived in an area where PM2.5 concentrations were over 35 μg/m3 in 2013. From the GWR models, we found that the percentages for cities that showed a statistically significant relationship (p < 0.05) between population exposure and each of the six factors were: urbanization, 91.92%; industry share, 91.58%; construction level, 88.55%; urban expansion, 73.40%; income disparity, 64.98%; and private vehicles, 27.27%. The R-squared value for the six factors in the multivariable GWR model was 0.88, and all cities demonstrated a statistically significant relationship. More importantly, the association between the six factors and population exposure was found to be spatially heterogeneous at the local geographic level. Consideration of these six drivers of population exposure can help policy makers and epidemiologists to evaluate and reduce population exposure risks.

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

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.

Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China’s Megacities

Urban form is increasingly being identified as an important determinant of air pollution in developed countries. However, the effect of urban form on air pollution in developing countries has not been adequately addressed in the literature to date, which points to an evident omission in current literature. In order to fill this gap, this study was designed to estimate the impacts of urban form on air pollution for a panel made up of China’s five most rapidly developing megacities (Beijing, Tianjin, Shanghai, Chongqing, and Guangzhou) using time series data from 2000 to 2012. Using the official Air Pollution Index (API) data, this study developed three quantitative indicators: mean air pollution index (MAPI), air pollution ratio (APR), and continuous air pollution ratio (CAPR), to evaluate air pollution levels. Moreover, seven landscape metrics were calculated for the assessment of urban form based on three aspects (urban size, urban shape irregularity, and urban fragmentation) using remote sensing data. Panel data models were subsequently employed to quantify the links between urban form and air pollution. The empirical results demonstrate that urban expansion surprisingly helps to reduce air pollution. The substitution of clean energy for dirty energy that results from urbanization in China offers a possible explanation for this finding. Furthermore, urban shape irregularity positively correlated with the number of days with polluted air conditions, a result could be explained in terms of the influence of urban geometry on traffic congestion in Chinese cities. In addition, a negative association was identified between urban fragmentation and the number of continuous days of air pollution, indicating that polycentric urban forms should be adopted in order to shorten continuous pollution processes. If serious about achieving the meaningful alleviation of air pollution, decision makers and urban planners should take urban form into account when developing sustainable cities in developing countries like China.

Decarbonizing China’s Urban Agglomerations

China’s urban agglomerations contribute 64 percent to China’s energy-related CO2 emissions and thus play a vital role in determining the future of climate change. There is little information available about city-level energy consumption and CO2 emissions; thus, we employ spatiotemporal modeling using Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) nighttime light imagery. Our findings show that such agglomerations have in fact experienced a remarkable decline in CO2 emission intensity—from 0.43 t/thousand yuan to 0.20 t/thousand yuan between 1995 and 2013, which constitutes an average annual decline of 4.34 percent. Despite still very high CO2 intensities in western China, a convergence of CO2 intensities across the country has occurred over the last few decades. Using panel regression modeling, we analyze differences in the decline of CO2 emission intensities due to regional differences in socioeconomic variables such as economic growth, population, economic structure, population density, and characteristics of urbanization. Factors that have hampered the decline of CO2 intensities are the ongoing industrialization that demands the increase in the production of heavy industry, in infrastructure investment, and in housing stock. Key Words: CO2 emission intensity, nighttime light imagery, spatiotemporal modeling, urban agglomerations. 中国的城市集聚, 生产了中国与能源相关的二氧化碳排放的百分之六十四, 因此在决定气候变迁的未来方面扮演了重要角色。但城市层级的能源消耗和二氧化碳排放的可及信息却相当稀少；因此我们运用时空模式化, 该模式化使用防卫气象卫星计画／线形扫描系统（DMSP/OLS）的夜间光影像。我们的研究发现显示, 这些集聚其实经历了二氧化碳排放密集度的明显减少——从1995年的0.43吨／千元到2013年的0.2吨／千元, 每年平均降低百分之四点三四。尽管中国西部的二氧化碳密度仍相当高, 过去数十年来仍发生了全国二氧化碳密度的聚合。我们运用面板迴归模型, 分析因诸如经济成长、人口、经济结构、人口密度与城市化特徵等社会经济变因的区域差异所导致的二氧化碳排放密度降低的差异。阻碍二氧化碳密度降低的因素是需要增加重工业生产、基础建设投资以及房地产需求的持续工业化。 关键词：二氧化碳排放密度, 夜间光影像, 时空模式化, 城市集聚。 Las aglomeraciones urbanas de China contribuyen el 64 por ciento de las emisiones chinas de CO2 relacionadas con energía, para así desempeñar un papel vital en la determinación futura del cambio climático. Hay poca información disponible acerca del consumo de energía a nivel de ciudad y de las emisiones de CO2; entonces, empleamos modelado espaciotemporal usando imágenes de luminosidad nocturna del Programa Satelital Meteorológico de la Defensa/Sistema Operacional de Escaneo en Línea (DMSP/OLS). Nuestros hallazgos muestran que de hecho tales aglomeraciones han experimentado una notable disminución en la intensidad de la emisión de CO2 ––de 0.43t/miles de yuanes a 0.20t/miles yuanes entre 1995 y 2013, lo cual constituye una declinación promedio anual de 4.34 por ciento. Pese a las intensidades de CO2 en China occidental todavía demasiado altas, una convergencia de intensidades del CO2 a través del país ha ocurrido durante las pasadas pocas décadas. Usando modelado de regresión de panel, analizamos las diferencias de la declinación de las intensidades en la emisión de CO2 debidas a diferencias regionales en variables socioeconómicas tales como crecimiento económico, población, estructura económica, densidad de población y características de la urbanización. Los factores que han dificultado la declinación en las intensidades del CO2 son la industrialización en marcha que demanda incremento en la producción de la industria pesada, en inversiones para infraestructura y en el inventario de vivienda.