Optimization of industry structure based on water environmental carrying capacity under uncertainty of the Huai River Basin within Shandong Province, China

Li, Na; Yang, Hong; Wang, Lachun; Huang, Xianjin; Zeng, Chunfen; Wu, Hao; Ma, Xiaowei; Song, Xuetao; Ye, Wei

doi:10.1016/j.jclepro.2015.08.074

Cited by 80 publications

(28 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The economic growth in these provinces primarily rely on the expansion of construction and a substantial increase in the input of production materials, which can also contribute to large increases in the TNL [56,57]. The larger gaps in the central and eastern provinces can be explained by the more advanced and efficient industrial structures in these regions [58][59][60]. However, the reasons for these large gaps vary among different regions of China [61].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Zhu

Yang

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Nighttime light data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R 2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Zhu

Yang

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tang et al [185,186] Heihe River Basin Li and Guo [107], Li et al [109,111] and Zhang et al [231] Heshui River Basin Li et al [117], Liu et al [128,132] and Xu et al [212] Huai River Basin Gu et al [73] and Li et al [112] Hun River Xu et al [215,216] Kaidu-Kongque River Basin Huang et al [88,90], Li et al [125], Zeng et al [222,224,[226][227][228][229] and Zhou et al [242] Lake Tai Watershed Liu et al [134] and Xu and Huang [217] Miyun Reservoir Han et al [81] and Rong et al [175] Nansihu Lake Basin Xie et al [207,209] Tarim River Basin Huang et al [88][89][90] Three Gorges Reservoir Feng et al [58], Han et al [79], Han et al [87], Huang et al [91], Li et al [126], Xu et al [211], Yuan et al [220] and Zhang et al [236] Xiangxi River Basin Han et al [79], Hu et al [86,87], Huang et al …”

Section: China Ertan Reservoirmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

View full text Add to dashboard Cite

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.

show abstract

“…When the coal prices are low, it is a challenge for Shanxi Province to solve environmental problems according to the uniform policies and standards. Secondly, the diversities of environment carrying capacities among provinces can make the different environmental disturbance caused by the same amount of pollution discharged [77,78,79,80]. Therefore, the implementation of the uniform pollution control policies and standards is not rational.…”

Section: Discussionmentioning

confidence: 99%

The Empirical Relationship between Mining Industry Development and Environmental Pollution in China

Lei

et al. 2017

IJERPH

View full text Add to dashboard Cite

This study uses a vector autoregression (VAR) model to analyze changes in pollutants among different mining industries and related policy in China from 2001 to 2014. The results show that: (1) because the pertinence of standards for mining waste water and waste gas emissions are not strong and because the maximum permissible discharge pollutant concentrations in these standards are too high, ammonia nitrogen and industrial sulfur dioxide discharges increased in most mining industries; (2) chemical oxygen demand was taken as an indicator of sewage treatment in environmental protection plans; hence, the chemical oxygen demand discharge decreased in all mining industries; (3) tax reduction policies, which are only implemented in coal mining and washing and extraction of petroleum and natural gas, decreased the industrial solid waste discharge in these two mining industries.

show abstract

Optimization of industry structure based on water environmental carrying capacity under uncertainty of the Huai River Basin within Shandong Province, China

Cited by 80 publications

References 60 publications

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

The Empirical Relationship between Mining Industry Development and Environmental Pollution in China

Contact Info

Product

Resources

About