Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models

Xie, Qichang; Xu, Xiaohu; Liu, Xiaqing

doi:10.1016/j.jclepro.2019.02.166

Cited by 155 publications

(76 citation statements)

References 40 publications

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“…Among the factors related to PM 2.5 concentration, previous studies by Zhang [48], Yun [10], Xie [49], and Ding [50] also showed a significant positive correlation between population density and PM 2.5 ; in addition, Guo and Ding showed that population density is the most important socioeconomic factor affecting PM 2.5 concentration [51,52]. This study also confirms their results with similar findings.…”

Section: Discussionsupporting

confidence: 88%

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone

Yang

Lan

2019

IJERPH

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Particulate matter with a diameter less than 2.5 µm (PM2.5), one of the main sources of air pollution, has increasingly become a concern of the people and governments in China. Examining the socioeconomic factors influencing on PM2.5 concentration is important for regional prevention and control. Previous studies mainly concentrated on the economically developed eastern coastal cities, but few studies focused on inland cities. This study selected Chengdu Plain Economic Zone (CPEZ), an inland region with heavy smog, and used spatial econometrics methods to identify the spatiotemporal distribution characteristics of PM2.5 concentration and the socioeconomic factors underlying it from 2006 to 2016. Moran’s index indicates that PM2.5 concentration in CPEZ does have spatial aggregation characteristics. In general, the spatial clustering from the fluctuation state to the stable low state decreased by 1% annually on average, from 0.190 (p < 0.05) in 2006 to 0.083 (p < 0.1) in 2016. According to the results of the spatial Durbin model (SDM), socioeconomic factors including population density, energy consumption per unit of output, gross domestic product (GDP), and per capita GDP have a positive effect on PM2.5 concentration, while greening rate and per capita park space have a negative effect. Additionally, those factors have identified spatial spillover effects on PM2.5 concentration. This study could be a reference and support for the formulation of more efficient air pollution control policies in inland cities.

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Section: Discussionsupporting

confidence: 88%

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone

Yang

Lan

2019

IJERPH

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“…With the increase of technological level and environmental awareness, people pay more attention to the coordinated development of the economy and the environment. In China, there are related studies that prove this point [ 71 , 72 ]. It is foreseeable that developed regions will reach the inflection point of the inverted “U”-shaped curve early.…”

Section: Resultsmentioning

confidence: 94%

Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities

Biao

Yang

2020

IJERPH

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Fine particulate matter(PM2.5) pollution will affect people’s well-being and cause economic losses. It is of great value to study the impact of PM2.5 on the real estate market. While previous studies have examined the effects of PM2.5 pollution on urban housing prices, there has been little in-depth research on these effects, which are spatially heterogeneous at different conditional quantiles. To address this issue, this study employs quantile regression (QR) and geographically weighted quantile regression (GWQR) models to obtain a full account of asymmetric and spatial non-stationary effects of PM2.5 pollution on urban housing prices through 286 Chinese prefecture-level cities for 2005–2013. Considerable differences in the data distributions and spatial characteristics of PM2.5 pollution and urban housing prices are found, indicating the presence of asymmetric and spatial non-stationary effects. The quantile regression results show that the negative influences of PM2.5 pollution on urban housing prices are stronger at higher quantiles and become more pronounced with time. Furthermore, the spatial relationship between PM2.5 pollution and urban housing prices is spatial non-stationary at most quantiles for the study period. A negative correlation gradually dominates in most of the study areas. At higher quantiles, PM2.5 pollution is always negatively correlated with urban housing prices in eastern coastal areas and is stable over time. Based on these findings, we call for more targeted approaches to regional real estate development and environmental protection policies.

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“…" "环境库兹涅茨曲线 (EKC) " "脱钩 (Decoupling) " "生态效率 (Eco-efficiency) " 和 "耦合协调 (Coupling Coordination)" 为主题/关键词，并辅以 "区域(Regional)" "经济增长(Economic Growth)" "资源(Resource)" "环境(Environment)" 等主题/关 [16] 、增长假说(Pressure-led Growth) [17] 、互馈假说(Bidi-rectional Causality) [18] 和中立假说(No Causal Relationship) [19] [9] 。例如，单位根检验技术方面，当样本容量较小时， ADF 检验稳健性较好，而当序列存在自相关和异方差时， PP 检验稳健性更佳 [20] 。协整检验技术方面，有观点认为多变量 J-J 最大似然法优于双变量 E-G 两步法 [9,21] ，另一种声音则认为没有明确的证据表明任何一种技术优于其他，甚至同一数据集下不同检验技术所得弹性值也存在较大差异 [20] 。而在数据方面，学术界已基本达成共识，认为面板表 1 区域经济增长与资源环境压力格兰杰因果关系的研究文献 Table 1 Literature on Grainger causal relationship between regional economic growth and resource-environmental pressure 作者 Appiah [9] Alshehry 等 [10] Cowan 等 [11] Yoo 等 [12] Chen 等 [13] 彭新育等 [14] 陈桂月等 [15] 研究期 [22][23][24][25] 。然而，该假说与其理论解释仍面临 3 个方面的挑战：一是仅关注经济增长对资源环境的影响，忽视了资源环境系统对经济系统的反馈 [26,27] [28][29][30][31][32][33][34] 。深究原因，一方面在于资源环境问题具有复杂性、流动性与扩散性。例如，瞬时性、局域性污染物大多支持 EKC 假说，而累积性、扩散性污染物则多拒绝 [25] 。另一方面在于研究模型、方法、指标及数据等技术性因素的差异，甚至植根于研究案例资源环境本底、社会经济条件、政治体制、能源与环境规制等情境的差异 [22,35] [36] 。这可能是由于检验结果对模型的函数形式及估计技术过于敏感 [37] 。有批评者认为单一的回归模型不能全面刻画经济与资源环境系统的关系 [27] 。因此，有学者构建了包含产业结构、技术进步、国际贸易、消费偏好、能源价格、环境政策，甚至社会因素的结构模型，以揭示曲线形态变化的 "黑箱" [38,39] Table 2 Literature on environmental Kuznets curve (EKC) hypothesis between regional economic growt...…”

Section: 引言unclassified

Research progress and implication of the relationship between regional economic growth and resource-environmental pressure

PENG¹,

Guo²,

Zhang³

et al. 2020

资源科学

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The relationship between economic growth and resource-environmental pressure is an important research area for regional sustainable development. However, few studies have systematically summarized relevant research results within different theoretical contexts, and few have compared the pros and cons of different theories, methods, models, indicators, and evaluation systems. Therefore, it is difficult to determine their theoretical contributions. This article reviewed five kinds of theoretical models and empirical studies in this field. It also compared their theoretical connotations, methods, evaluation systems, and applicability. In addition, this article proposed the basic framework and key content of analysis on the relationship between regional economic growth and resource-environmental pressures. We aimed to provide some theoretical reference and technical support for advancing related research. The results show that: (1) The theories, methods, and indicators of this field have different advantages, disadvantages, and application scope. Relevant research should be carried out under the basic framework of testing the direction of causality → identifying the EKC turning point/decoupling state → assessing the ecoefficiency intensity → determining the coupling coordination phase. Regional sustainable development policies should be developed based on the idea of"direction regulation → turning point/state regulation → intensity regulation → system regulation." (2) We should focus on method, model, and evaluation system optimization. Studies on discovering driving factors, mechanisms, and path selection should be emphasized. Furthermore, it is necessary to implement research on multi-case comparison, remote coupling, and theoretical association in the future.

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Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models

Cited by 155 publications

References 40 publications

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone

Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities

Research progress and implication of the relationship between regional economic growth and resource-environmental pressure

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