Interstate pollution spillover and setting environmental standards

Huang, Ce; González, Ernesto D.R. Santibañez; Song, Malin

doi:10.1016/j.jclepro.2017.09.264

Cited by 12 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the above scholars: the improvement of economic development or urbanization level has increased the spillover effect of air pollution; Jeffrey (2017), Huang et al (2018), andTong et al (2020) studied the spillover effect of environmental regulations and policies on air quality governance; Among them, the conclusions of Fang et al (2019) and Tong et al (2020) are similar, that is, the reduction of pollution emissions in the target area is at the cost of the increase of pollution emissions in surrounding areas. Liu et al (2020) studied the air quality changes and spillover effects during the Covid-19 epidemic blockade.…”

Section: Spillover Effects About Air Qualitymentioning

confidence: 99%

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

Cui

Ren²,

Wei³

et al. 2022

Front. Ecol. Evol.

View full text Add to dashboard Cite

Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 < AQI < 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.

show abstract

Section: Spillover Effects About Air Qualitymentioning

confidence: 99%

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

Cui

Ren²,

Wei³

et al. 2022

Front. Ecol. Evol.

View full text Add to dashboard Cite

show abstract

“…As discussed above, CAA enforcement actions do not reflect non-explicit policy efforts like the strategic use of zoning rules and building permits to influence the placement of emission sources. There is ample evidence that state and local officials regularly use non-explicit policy tools to address air pollution problems in their jurisdictions, and that their use of these policy tools is sensitive to public opinion (Pargal et al 1997;Monogan et al 2016;Pinault et al 2016;Huang et al 2018).…”

Section: Policy Outcomes' Responsiveness To Public Opinionmentioning

confidence: 99%

“…Although the efforts themselves are difficult to observe, the aggregate impact of non-explicit policy on air pollution is significant (Monogan et al 2016). Studies that infer non-explicit policy variation through air pollution outcomes suggest that nonexplicit policy efforts are highly responsive to local public opinion (Pargal et al 1997;Pinault et al 2016;Huang et al 2018).…”

Section: Unmeasured Policymentioning

confidence: 99%

Pollution and the public: how information accessibility conditions the public’s responsiveness to policy and outcomes

Buchanan

2022

J. Pub. Pol.

View full text Add to dashboard Cite

This article advances a theory that brings real-world outcomes into our current understanding of the dynamic relationship between public opinion and policy. It examines a vital public good – air pollution remediation in 319 American localities – and estimates a dynamic model of relationships among three key variables: public opinion, policy, and air pollution outcomes. The analysis focuses on both public opinion and air pollution outcomes as dependent variables. I find that public opinion reacts to changes in statewide policy and local air pollution, which suggests the public forms its opinions with whatever reliable information is most readily available. I also find that local public opinion’s impact on local air pollution is substantively meaningful on timescales smaller than 5 years, indicating that the additional policy effort prompted by public opinion change is sufficient to yield tangible real-world outcomes even in the short term.

show abstract

“…Green total factor productivity (GTFP) is an index for evaluating the sustainability of development by comparing effective input and output factors. A large number of studies have used GTFP to assess sustainable development across different regions and sectors (Feng et al, 2015;Song et al, 2015;Fuinhas et al, 2016;Makijenko et al, 2016;Song et al, 2016;Liobikiene et al, 2017;Huang et al, 2018). In the field of agricultural research, many researchers have measured the productivity of agriculture based on input and output factors (Van Ittersum et al, 2003;Peters et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?

et al. 2020

View full text Add to dashboard Cite

Province) in Northwest China, most areas of which are located in arid and semi-arid regions (northwest of the 400 mm precipitation line), accounting for 58.74% of the country's land area and sustaining approximately 7.84×10 6 people. Because of drought conditions and fragile ecology, these regions cannot develop agriculture at the expense of the environment. Given the challenges of global warming, the green total factor productivity (GTFP), taking CO 2 emissions as an undesirable output, is an effective index for measuring the sustainability of agricultural development. Agricultural GTFP can be influenced by both internal production factors (labor force, machinery, land, agricultural plastic film, diesel, pesticide, and fertilizer) and external climate factors (temperature, precipitation, and sunshine duration). In this study, we used the Super-slacks-based measure (Super-SBM) model to measure agricultural GTFP during the period 2000-2016 at the regional level. Our results show that the average agricultural GTFP of most provinces and autonomous regions in arid and semi-arid regions underwent a fluctuating increase during the study period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), and the fluctuation was caused by the production factors (input and output factors). To improve agricultural GTFP, Shaanxi, Shanxi, and Gansu should reduce agricultural labor force input; Shaanxi, Inner Mongolia, Gansu, and Shanxi should decrease machinery input; Shaanxi, Inner Mongolia, Xinjiang, and Shanxi should reduce fertilizer input; Shaanxi, Xinjiang, Gansu, and Ningxia should reduce diesel input; Xinjiang and Gansu should decrease plastic film input; and Gansu, Shanxi, and Inner Mongolia should cut pesticide input. Desirable output agricultural earnings should be increased in Qinghai and Tibet, and undesirable output (CO 2 emissions) should be reduced in Inner Mongolia, Xinjiang, Gansu, and Shaanxi. Agricultural GTFP is influenced not only by internal production factors but also by external climate factors. To determine the influence of climate factors on GTFP in these provinces and autonomous regions, we used a Geographical Detector (Geodetector) model to analyze the influence of climate factors (temperature, precipitation, and sunshine duration) and identify the relationships between different climate factors and GTFP. We found that temperature played a significant role in the spatial heterogeneity of GTFP among provinces and autonomous regions in arid and semi-arid regions. For Xinjiang, Inner Mongolia, and Tibet, a suitable average annual temperature would be in the range of 7°C-9°C; for Gansu, Shanxi, and Ningxia, it would be 11°C-13°C; and for Shaanxi, it would be 15°C-17°C. Stable climatic conditions and more efficient production are prerequisites for the development of sustainable agriculture. Hence, in the agricultural production process, reducing the redundancy of input factors is the best way to

show abstract

Interstate pollution spillover and setting environmental standards

Cited by 12 publications

References 19 publications

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

Pollution and the public: how information accessibility conditions the public’s responsiveness to policy and outcomes

Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?

Contact Info

Product

Resources

About