PM2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies.

Elucidating the key impact factors on PM2.5 concentrations is crucial to formulate effective mitigation policies. In this study, we employed an extended Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model to identify the socioeconomic determinants of PM2.5 concentrations for 12 different regions and across China. The evaluation was based on a balanced panel dataset integrating long-term satellite-derived PM2.5 concentrations and socio-economic data in China from 1999 to 2011. Empirical results indicate that the influencing factors can be ranked in descending order of importance as: proportion of secondary sector of the economy, GDP per capita, urbanization, population, energy intensity, and proportion of tertiary sector. Proportion of secondary sector is the greatest contribution to increasing PM2.5 concentrations, especially for heavily polluted regions. GDP per capita is secondary in importance, and its impact is weakened by the existence of an EKC relationship between GDP per capita and PM2.5 concentrations. Therefore, PM2.5 pollution is an economic development mode problem, rather than a general economic development problem. The impact of urbanization varies across regions; while promoting urbanization will be conducive to decreased PM2.5 concentrations in Northwest China and Northeast China, it will contribute to increased PM2.5 concentrations in other regions. Population and energy intensity are significant in most regions, but neither are decisive factors because of the small absolute value of their coefficients. Finally, different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.

[1]  Yuan Wang,et al.  Asian pollution climatically modulates mid-latitude cyclones following hierarchical modelling and observational analysis , 2014, Nature Communications.

[2]  H. Akimoto.  Global Air Quality and Pollution , 2003, Science.

[3]  Brantley Liddle,et al.  Demographic Dynamics and Per Capita Environmental Impact: Using Panel Regressions and Household Decompositions to Examine Population and Transport , 2003 .

[4]  Kebin He,et al.  The characteristics of PM2.5 in Beijing, China , 2001 .

[5]  K. He,et al.  Chemical characteristics of haze during summer and winter in Guangzhou , 2009 .

[6]  Dong Jiang,et al.  Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China , 2013, International journal of environmental research and public health.

[7]  Boqiang Lin,et al.  A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models , 2016 .

[8]  Weiqi Zhou,et al.  Impact of urbanization level on urban air quality: a case of fine particles (PM(2.5)) in Chinese cities. , 2014, Environmental pollution.

[9]  F. Dominici,et al.  Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. , 2006, JAMA.

[10]  E. Rosa,et al.  Effects of population and affluence on CO2 emissions. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Jonathan N. Katz,et al.  What To Do (and Not to Do) with Time-Series Cross-Section Data , 1995, American Political Science Review.

[12]  Yaoqiu Kuang,et al.  China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines , 2014 .

[13]  K. He,et al.  Characteristics of PM 2.5 speciation in representative megacities and across China , 2011 .

[14]  Yu Hao,et al.  The influential factors of urban PM2.5 concentrations in China: a spatial econometric analysis , 2016 .

[15]  S. Pickett,et al.  An optimum city size? The scaling relationship for urban population and fine particulate (PM(2.5)) concentration. , 2016, Environmental pollution.

[16]  Boqiang Lin,et al.  Regional differences of pollution emissions in China: contributing factors and mitigation strategies , 2016 .

[17]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[18]  M. Charlton,et al.  Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis , 1998 .

[19]  C. Chan,et al.  Air pollution in mega cities in China , 2008 .

[20]  W. Reed,et al.  Which panel data estimator should I use? , 2011 .

[21]  Haizhen Yang,et al.  Concentration and chemical composition of PM2.5 in Shanghai for a 1-year period , 2003 .

[22]  G. Peters,et al.  The socioeconomic drivers of China’s primary PM2.5 emissions , 2014 .

[23]  P. Ehrlich,et al.  IMPACT OF POPULATION GROWTH , 1971, Science.

[24]  M. Brauer,et al.  Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application , 2010, Environmental health perspectives.

[25]  Zifa Wang,et al.  The air-borne particulate pollution in Beijing—concentration, composition, distribution and sources , 2004 .

[26]  Shuxiao Wang,et al.  Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. , 2007, Environmental science & technology.

[27]  Zhen-bo Wang,et al.  Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration. , 2016, Chemosphere.

[28]  Alan D. Lopez,et al.  Rapid health transition in China, 1990–2010: findings from the Global Burden of Disease Study 2010 , 2013, The Lancet.

[29]  Jiansheng Wu,et al.  Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011 , 2016 .

[30]  A. Cohen,et al.  Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. , 2012, Environmental science & technology.

[31]  R. Burnett,et al.  Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. , 2002, JAMA.

[32]  Jiming Hao,et al.  Primary air pollutant emissions of coal-fired power plants in China: Current status and future prediction , 2008 .

[33]  Yuesi Wang,et al.  Reductions of PM2.5 in Beijing-Tianjin-Hebei urban agglomerations during the 2008 Olympic Games , 2012, Advances in Atmospheric Sciences.

[34]  Zhuang Miao,et al.  Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models , 2015 .

[35]  G. Grossman,et al.  Economic Growth and the Environment , 1994 .

[36]  Dennis Normile,et al.  China's Living Laboratory in Urbanization , 2008, Science.

[37]  A. C. Marques,et al.  Are public policies towards renewables successful? Evidence from European countries , 2012 .

[38]  Alan D. Lopez,et al.  Age-specific and sex-specific mortality in 187 countries, 1970–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[39]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[40]  Michael Jerrett,et al.  Atmospheric science: The death toll from air-pollution sources , 2015, Nature.

[41]  Hsiao-Tien Pao,et al.  CO2 emissions, energy consumption and economic growth in BRIC countries , 2010 .

[42]  B. Baltagi,et al.  Econometric Analysis of Panel Data , 2020, Springer Texts in Business and Economics.

[43]  Judith C. Chow,et al.  Characteristics of carbonaceous aerosol in Pearl River Delta Region, China during 2001 winter period , 2003 .

[44]  Marc L. Serre,et al.  Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States , 2012, Environmental health perspectives.

[45]  Renjian Zhang,et al.  Chemical characterization and source apportionment of PM 2 . 5 in Beijing : seasonal perspective , 2013 .

[46]  E. Rosa,et al.  STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts , 2003 .

[47]  A. Piazzalunga,et al.  High secondary aerosol contribution to particulate pollution during haze events in China , 2014, Nature.

[48]  J. Lelieveld,et al.  The contribution of outdoor air pollution sources to premature mortality on a global scale , 2015, Nature.

[49]  R. Parks,et al.  Efficient Estimation of a System of Regression Equations when Disturbances are Both Serially and Contemporaneously Correlated , 1967 .