Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach

Bilgili, Faik; Nathaniel, Solomon Prince; Kuşkaya, Sevda; Kassouri, Yacouba

doi:10.1007/s11356-021-14506-0

Cited by 63 publications

(24 citation statements)

References 96 publications

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“…Given that Zhejiang Province, as with much of the rest of China, has experienced rapid economic growth over the last 2 or 3 decades, a detailed understanding of the role of economic factors in relation to atmospheric pollution is especially useful. The global interpretability feature of SHAP explanations reveals that growth of GDP initially led to sharp increases in atmospheric pollution but that the influence of increasing GDP declined more recently, a trend that parallels the inverted U-shaped environmental Kuznets curve [73]. These results are consistent with studies of PM 2.5 in other parts of China [74], and indeed the course of atmospheric pollution in general.…”

Section: Discussionsupporting

confidence: 81%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

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Section: Discussionsupporting

confidence: 81%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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“…The research of He and Lin (2019) shows that only some provinces in eastern China have crossed the turning point of EKC, while most cities in central and western China are still on the left side of EKC. Bilgili et al (2021) employed a panel quantile regression model to verify the environmental Kuznets Curve. Dietz and Rosa (1994) improved the IPAT model, namely, the STIRPAT model.…”

Section: Literature Reviewmentioning

confidence: 99%

Does Technological Innovation Promote Haze Pollution Control? New Evidence Based on Panel Threshold Model and Spatial Econometric Model

Shi

Zhang

2022

Front. Environ. Sci.

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Since the reform and opening up, China’s rapid economic growth mainly depends on the industrial development mode of “high energy consumption and high pollution,” which has caused serious haze pollution. In order to achieve the goal of haze control and sustainable development, we need to give full play to the role of technological innovation. Empirical analysis of the haze control effect of technological innovation has theoretical significance and practical value. Based on the panel data of 30 provinces in China from 2005 to 2018 and the PM2.5 concentration data published by the atmospheric composition analysis group of Dalhousie University, this study selects R&D personnel input and technology market turnover to represent the level of technological innovation and uses the panel data model, threshold effect model, and spatial Durbin model to empirically analyze the impact of technological innovation on haze pollution control. The empirical results show that 1) technological innovation can significantly reduce the PM2.5 concentration of the province, showing a positive haze control effect; 2) technological innovation indicates a negative indirect effect on PM2.5 concentration, confirming the “technology spillover effect,” that is, technological innovation also has a haze control effect on the surrounding provinces; 3) with the increase in the province’s economic aggregate, the haze control effect of technological innovation shows a trend of “high low high,” and the role of technological innovation is the lowest in the stage of economic transformation; and 4) from the perspective of regional differentiation, the haze control effect of technological innovation is the largest in the central region, and the smallest in the western region. Technological innovation indicates a positive haze control effect on all regions at all stages of economic development. This study provides policy suggestions for the government and enterprises to use innovation for cleaner production and sustainable development.

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“…Other studies have analyzed the impact of energy policies, green finance policies, green investment, fiscal decentralization, credit ratings policies on the reduction of CO 2 emissions in China, in emerging markets or in developed countries respectively (Bilgili et al, 2019;Kuşkaya and Bilgili, 2019;Ji et al, 2021a;Bilgili et al, 2021;Ji et al, 2021b;Hao et al, 2021;Li et al, 2021;Shen et al, 2021;Umar et al, 2021;Yu et al, 2022). Elahi and other researchers (Elahi et al, 2018;Elahi et al, 2019;Elahi et al, 2021a;Elahi et al, 2021b) focused on climatic factors, socio-psychological behavior and institutional and public policies regarding the use of farmland.…”

Section: Introductionmentioning

confidence: 99%

How Does Government Policy Improve Green Technology Innovation: An Empirical Study in China

Niu

et al. 2022

Front. Environ. Sci.

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This paper divides government policy according to policy quantity, policy effectiveness and policy executive force so that the government policy can be quantified in more detail. Green patent data is used to represent green technological innovation, and the fixed effect model and panel data analysis from 2010 to 2019 are employed. The empirical results show that government policy has a significant direct promoting effect on green technology innovation. And the positive impact of policy quantity and policy effectiveness on green technology innovation is greater than that of policy executive force. In addition, the government policy will weaken the positive effect of enterprise innovation vitality on green technology innovation. Research conclusions also show that the direct and indirect effects of government policies on green technology innovation are heterogeneous. The government still needs appropriately policies adapted to the local situation, coordinated in policy quantity, policy effectiveness, and executive force, and accelerate the establishment of market-oriented green technology innovation environment. Different regions also should find the right green technology innovation policy scheme for their own regions.

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Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach

Cited by 63 publications

References 96 publications

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Does Technological Innovation Promote Haze Pollution Control? New Evidence Based on Panel Threshold Model and Spatial Econometric Model

How Does Government Policy Improve Green Technology Innovation: An Empirical Study in China

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