Water resources and water pollution emissions in China's industrial sector: A green-biased technological progress analysis

Li, Jing; See, Kok Fong; Jin, Chi

doi:10.1016/j.jclepro.2019.03.216

Cited by 78 publications

(40 citation statements)

References 36 publications

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“…According to previous studies, on the one hand, industrial enterprises are mostly energy-intensive and regions with higher levels of industrialization consume more energy [61]. On the other hand, due to the release of industrial pollutants, residents' health and well-being will also be damaged [62].…”

Section: Spatial Econometric Analysis Of the Influencing Factors Of Eiwbmentioning

confidence: 99%

Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

Long

Zhang

Chen

et al. 2020

IJERPH

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Current energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem between energy consumption and residents’ health. In this paper, panel data of 30 provinces in mainland China from 2005 to 2016 is used to calculate the EIWB of each province and analyze its spatial distribution. Moreover, the effect of influencing factors on EIWB is investigated by using the spatial Durbin model. The results show that: (1) The EIWB presents a spatial agglomeration. The provinces with high EIWB mostly cluster in the northern China. (2) Industrial structure and energy structure have positive effects on EIWB in local area through increasing energy consumption and damaging residents’ health. (3) The effect of urbanization and income on local EIWB is significantly positive because it will promote energy consumption. (4) Industrial structure, health expenditure, foreign direct investment and technological progress have spatial spillover effects due to its significant impact on residents’ health in neighboring areas. Based on conclusions, the corresponding policy recommendations are proposed.

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Section: Spatial Econometric Analysis Of the Influencing Factors Of Eiwbmentioning

confidence: 99%

Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

Long

Zhang

Chen

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Some scholars try to use this kind of identifying the criteria with non-radial distance functions. For example, Li et al [40] use the directional distance function with undesired output to analyze the green biased technical progress in terms of water resources across China on the basis of Weber and Domazlicky's research [33]. The directional distance function with an undesired output is a non-radial distance function.…”

Section: Literature Reviewmentioning

confidence: 99%

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

Xi-yue

Sun

Wang

2020

IJERPH

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As a significant ecological corridor from west to east across China, the Yangtze River Economical Belt (YREB) is in great need of green development and transformation. Rather than only focusing on the overall growth of green productivity, it is important to identify whether the technical change is biased towards economic performance or green performance in promoting green productivity. By employing the biased technical change theory and Malmquist index decomposition method, we analyze the green biased technical change in terms of industrial water resources in YREB at the output side and the input side respectively. We find that the green biased technical change varies during 2006–2015 at both the input side and output side in YREB. At the input side, water-saving biased technical change is generally dominant compared to water-using biased technical change during 2006–2015, presenting the substitution effects of non-water production factors. At the output side, the economy-growth biased technical change is the main force to promote green productivity, whereas the role of water-conservation biased technical change is insufficient. The green performance at the output side needs to be strengthened compared to the economic performance in YREB. A series of water-related environmental policies introduced in China since 2008 have promoted the green biased technical change both at the input side and the output side in YREB, but the policy effects at the output side is still inadequate compared to that at the input side. The technological innovation in sewage treatment and control need to catch up with the economic growth in YREB. Our research gives insights to enable a deeper understanding of the green biased technical change in YREB and will benefit more focused policy-making of green innovation.

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“…Following previous research, COD discharge was taken as the parameter to determine water quality (Guan et al 2014, Zhao et al 2016, Cai et al 2019, Li et al 2019a. Fundamentally, COD indicates the amount of oxidant consumed during oxidation of organic substance present in water samples.…”

Section: Datamentioning

confidence: 99%

“…In 2014, China has stepped into the 'new normal' phase, and achieved optimisation of economic structures (Mi et al 2017). It means that China's development was no longer driven by investment but innovation and environmentallyfriendly technology, and the transitions have accomplished from high-speed to medium-high-speed growth, and from rapid growth of scale to intensive-and qualityincreasing growth (Meng et al 2019, Li et al 2019a. Additionally, as the world's largest exporter, China also has its economy driven by international trade.…”

Section: Introductionmentioning

confidence: 99%

Quantity and quality of China's water from demand perspectives

Shan

Zhang

et al. 2019

Environ. Res. Lett.

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China is confronted with an unprecedented water crisis regarding its quantity and quality. In this study, we quantified the dynamics of China's embodied water use and chemical oxygen demand (COD) discharge from 2010 to 2015. The analysis was conducted with the latest available water use data across sectors in primary, secondary and tertiary industries and input-output models. The results showed that (1) China's water crisis was alleviated under urbanisation. Urban consumption occupied the largest percentages (over 30%) of embodied water use and COD discharge, but embodied water intensities in urban consumption were far lower than those in rural consumption.(2) The 'new normal' phase witnessed the optimisation of China's water use structures. Embodied water use in light-manufacturing and tertiary sectors increased while those in heavy-manufacturing sectors (except chemicals and transport equipment) dropped.(3) Transformation of China's international market brought positive effects on its domestic water use. China's water use (116-80 billion tonnes (Bts)) 9 and COD discharge (3.95-2.22 million tonnes (Mts)) embodied in export tremendously decreased while its total export values (11-25 trillion CNY) soared. Furthermore, embodied water use and COD discharge in relatively low-end sectors, such as textile, started to transfer from international to domestic markets when a part of China's production activities had been relocated to other developing countries.

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Water resources and water pollution emissions in China's industrial sector: A green-biased technological progress analysis

Cited by 78 publications

References 36 publications

Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

Quantity and quality of China's water from demand perspectives

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