FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2979630717> ?p ?o ?g. }

• W2979630717 endingPage "124216" @default.
• W2979630717 startingPage "124216" @default.
• W2979630717 abstract "There are multiple relationship curves between watershed socioeconomic development and water environmental conservation. The goal of this paper is to present a theoretical pattern of economic activities and water environmental improvement by incorporating their bidirectional causality relationship. The Environmental Kuznets Curve (EKC) has been useful in connecting economic and environmental concepts, thus becoming an important tool for evaluating watershed sustainability. The high governance cost makes water pollution-intensive goods relatively expensive to produce. According to the EKC, there is an inverted U‐shaped relationship between water environment deterioration and the level of social and economic improvement. Based on EKC hypothesis, we studied the coordinated relationship between environmental protection of water and economic and social sustainable development in the Huntai River Watershed from 2003 to 2012 by means of a panel threshold data. The EKC is an empirical curve that describes the relationship between economic restructuring and water environmental conditions improvement. The Green Environmental Kuznets Curve (GEKC) is used to describe the relationship among watershed pollution emissions, the environmental quality and income per capita. However, according to the GEKC, U‐shaped relationship exists between the coordination of water environmental protection and the level of social economic development (SED). The GEKC is affected by water resource consumption, the regional trade situation (T), the per capita gross domestic product level (Y), and controls on the total amount of water pollution discharge. We try to adjust to the type of GEKC curve and apply dataset of water pollutant emission reduction cost control to fit main characteristic of equilibrium cost. In accordance with Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) data statistical analysis, the value of the GEKC regression equation independent variables Y, Y2, per capita water resources consumption (E), T, adding value of production (AVP) and modified socioeconomic development index (MSDI) were 1.988, −0.150, 0.720, 0.216, 0.070, and 1.890, respectively. In the long-term, the correlation formula of watershed per capita sewage discharge (C) to Y was 2.090–0.300Y. For every 1% increase in E, T, AVP, and MSDI, C increased by approximately 0.720%, 0.216%, 0.072%, and 1.890%, respectively. The E, T and MSDI are key factors in developing an effective way to control the levels of water pollution discharge. Pollution is endogenous, so water environment pollution is not necessarily caused by economic growth. According to the total amount control of water pollution discharge, the GEKC equilibrium optimization calculation results can be used to effectively control watershed pollutant emissions and improve water environmental quality. Through the adjustment of industrial structure or industrial transformation and technological progress, it is possible to change the relationship between current economic growth and water environment. With GEKC related Curve characteristic and indicator classes, it is possible to realize harmonious and sustainable development of regional economy and society in the Huntai River Watershed." @default.
• W2979630717 created "2019-10-18" @default.
• W2979630717 creator A5005122107 @default.
• W2979630717 creator A5055478763 @default.
• W2979630717 creator A5061443116 @default.
• W2979630717 creator A5065100344 @default.
• W2979630717 creator A5068390417 @default.
• W2979630717 creator A5088584990 @default.
• W2979630717 date "2019-12-01" @default.
• W2979630717 modified "2023-09-27" @default.
• W2979630717 title "Equilibrium cost of water environmental protection based on watershed sustainability" @default.
• W2979630717 cites W1571515062 @default.
• W2979630717 cites W1965423556 @default.
• W2979630717 cites W1970171438 @default.
• W2979630717 cites W1972521039 @default.
• W2979630717 cites W1978775682 @default.
• W2979630717 cites W2002878662 @default.
• W2979630717 cites W2003750580 @default.
• W2979630717 cites W2009191664 @default.
• W2979630717 cites W2015758517 @default.
• W2979630717 cites W2017809080 @default.
• W2979630717 cites W2022448116 @default.
• W2979630717 cites W2025161033 @default.
• W2979630717 cites W2064344976 @default.
• W2979630717 cites W2080578930 @default.
• W2979630717 cites W2110603299 @default.
• W2979630717 cites W2117513178 @default.
• W2979630717 cites W2120277692 @default.
• W2979630717 cites W2124884000 @default.
• W2979630717 cites W2135001552 @default.
• W2979630717 cites W2136276883 @default.
• W2979630717 cites W2152267541 @default.
• W2979630717 cites W2158725917 @default.
• W2979630717 cites W2190698000 @default.
• W2979630717 cites W2531351868 @default.
• W2979630717 cites W2599422850 @default.
• W2979630717 cites W2659782523 @default.
• W2979630717 cites W2754925220 @default.
• W2979630717 cites W2793339211 @default.
• W2979630717 cites W2807615785 @default.
• W2979630717 cites W2885304822 @default.
• W2979630717 cites W2890071898 @default.
• W2979630717 cites W2892355298 @default.
• W2979630717 cites W2898717136 @default.
• W2979630717 cites W2919632274 @default.
• W2979630717 cites W2963703290 @default.
• W2979630717 cites W3121861737 @default.
• W2979630717 cites W3122477235 @default.
• W2979630717 cites W3123421168 @default.
• W2979630717 cites W3123751053 @default.
• W2979630717 cites W3123833473 @default.
• W2979630717 cites W3125006743 @default.
• W2979630717 cites W3125319129 @default.
• W2979630717 cites W4243041127 @default.
• W2979630717 doi "https://doi.org/10.1016/j.jhydrol.2019.124216" @default.
• W2979630717 hasPublicationYear "2019" @default.
• W2979630717 type Work @default.
• W2979630717 sameAs 2979630717 @default.
• W2979630717 citedByCount "6" @default.
• W2979630717 countsByYear W29796307172020 @default.
• W2979630717 countsByYear W29796307172021 @default.
• W2979630717 countsByYear W29796307172022 @default.
• W2979630717 countsByYear W29796307172023 @default.
• W2979630717 crossrefType "journal-article" @default.
• W2979630717 hasAuthorship W2979630717A5005122107 @default.
• W2979630717 hasAuthorship W2979630717A5055478763 @default.
• W2979630717 hasAuthorship W2979630717A5061443116 @default.
• W2979630717 hasAuthorship W2979630717A5065100344 @default.
• W2979630717 hasAuthorship W2979630717A5068390417 @default.
• W2979630717 hasAuthorship W2979630717A5088584990 @default.
• W2979630717 hasConcept C100129307 @default.
• W2979630717 hasConcept C114350782 @default.
• W2979630717 hasConcept C119857082 @default.
• W2979630717 hasConcept C127598652 @default.
• W2979630717 hasConcept C134560507 @default.
• W2979630717 hasConcept C144024400 @default.
• W2979630717 hasConcept C149923435 @default.
• W2979630717 hasConcept C150547873 @default.
• W2979630717 hasConcept C153823671 @default.
• W2979630717 hasConcept C162324750 @default.
• W2979630717 hasConcept C175605778 @default.
• W2979630717 hasConcept C18903297 @default.
• W2979630717 hasConcept C198428699 @default.
• W2979630717 hasConcept C2780481019 @default.
• W2979630717 hasConcept C2908647359 @default.
• W2979630717 hasConcept C2909468537 @default.
• W2979630717 hasConcept C39432304 @default.
• W2979630717 hasConcept C41008148 @default.
• W2979630717 hasConcept C50522688 @default.
• W2979630717 hasConcept C524765639 @default.
• W2979630717 hasConcept C526734887 @default.
• W2979630717 hasConcept C66204764 @default.
• W2979630717 hasConcept C86803240 @default.
• W2979630717 hasConceptScore W2979630717C100129307 @default.
• W2979630717 hasConceptScore W2979630717C114350782 @default.
• W2979630717 hasConceptScore W2979630717C119857082 @default.
• W2979630717 hasConceptScore W2979630717C127598652 @default.
• W2979630717 hasConceptScore W2979630717C134560507 @default.

• next