Urban Industrial Water Supply and Demand: System Dynamic Model and Simulation Based on Cobb–Douglas Function
In order to meet the needs of water-saving society development, the system dynamics method and the Cobb–Douglas (C–D) production function were combined to build a supply and demand model for urban industrial water use. In this model, the industrial water demand function is expressed as the sum of th...
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| Veröffentlicht in: | Sustainability 2019-11, Vol.11 (21), p.5893 |
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| creator | Li, Kebai Ma, Tianyi Wei, Guo Zhang, Yuqian Feng, Xueyan |
| description | In order to meet the needs of water-saving society development, the system dynamics method and the Cobb–Douglas (C–D) production function were combined to build a supply and demand model for urban industrial water use. In this model, the industrial water demand function is expressed as the sum of the general industrial water demand and the power industry water demand, the urban water supply function is expressed as the Cobb–Douglas production function, investment and labor input are used as the control variables, and the difference between supply and demand in various situations is simulated by adjusting their values. In addition, the system simulation is conducted for Suzhou City, Jiangsu Province, China, with 16 sets of different, carefully designed investment and labor input combinations for exploring a most suitable combination of industrial water supply and demand in Suzhou. We divide the results of prediction into four categories: supply less than demand, supply equals demand, supply exceeds demand, and supply much larger than demand. The balance between supply and demand is a most suitable setting for Suzhou City to develop, and the next is the type in which the supply exceeds demand. The other two types cannot meet the development requirements. We concluded that it is easier to adjust the investment than to adjust the labor input when adjusting the control variables to change the industrial water supply. While drawing the ideal combination of investment and labor input, a reasonable range of investment and labor input is also provided: the scope of investment adjustment is 0.6 I 0 − 1.1 I 0 , and the adjustment range of labor input is 0.5 P 0 − 1.2 P 0 . |
| doi_str_mv | 10.3390/su11215893 |
| format | Article |
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In this model, the industrial water demand function is expressed as the sum of the general industrial water demand and the power industry water demand, the urban water supply function is expressed as the Cobb–Douglas production function, investment and labor input are used as the control variables, and the difference between supply and demand in various situations is simulated by adjusting their values. In addition, the system simulation is conducted for Suzhou City, Jiangsu Province, China, with 16 sets of different, carefully designed investment and labor input combinations for exploring a most suitable combination of industrial water supply and demand in Suzhou. We divide the results of prediction into four categories: supply less than demand, supply equals demand, supply exceeds demand, and supply much larger than demand. The balance between supply and demand is a most suitable setting for Suzhou City to develop, and the next is the type in which the supply exceeds demand. The other two types cannot meet the development requirements. We concluded that it is easier to adjust the investment than to adjust the labor input when adjusting the control variables to change the industrial water supply. While drawing the ideal combination of investment and labor input, a reasonable range of investment and labor input is also provided: the scope of investment adjustment is 0.6 I 0 − 1.1 I 0 , and the adjustment range of labor input is 0.5 P 0 − 1.2 P 0 .</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su11215893</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Climate change ; Consumption ; Decision making ; Dynamic models ; Efficiency ; Electric industries ; Industrial water ; Irrigation ; Labor ; Linear programming ; Management decisions ; Methods ; Neural networks ; Planning ; Population growth ; Production functions ; Regions ; River networks ; Simulation ; Supply & demand ; System dynamics ; Trends ; Water conservation ; Water demand ; Water shortages ; Water supply ; Water use</subject><ispartof>Sustainability, 2019-11, Vol.11 (21), p.5893</ispartof><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-bb64e46b4f0e7f437e6cbf9240284da2a377b4d694e3fe853c02cc3367d2085f3</citedby><cites>FETCH-LOGICAL-c295t-bb64e46b4f0e7f437e6cbf9240284da2a377b4d694e3fe853c02cc3367d2085f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Li, Kebai</creatorcontrib><creatorcontrib>Ma, Tianyi</creatorcontrib><creatorcontrib>Wei, Guo</creatorcontrib><creatorcontrib>Zhang, Yuqian</creatorcontrib><creatorcontrib>Feng, Xueyan</creatorcontrib><title>Urban Industrial Water Supply and Demand: System Dynamic Model and Simulation Based on Cobb–Douglas Function</title><title>Sustainability</title><description>In order to meet the needs of water-saving society development, the system dynamics method and the Cobb–Douglas (C–D) production function were combined to build a supply and demand model for urban industrial water use. In this model, the industrial water demand function is expressed as the sum of the general industrial water demand and the power industry water demand, the urban water supply function is expressed as the Cobb–Douglas production function, investment and labor input are used as the control variables, and the difference between supply and demand in various situations is simulated by adjusting their values. In addition, the system simulation is conducted for Suzhou City, Jiangsu Province, China, with 16 sets of different, carefully designed investment and labor input combinations for exploring a most suitable combination of industrial water supply and demand in Suzhou. We divide the results of prediction into four categories: supply less than demand, supply equals demand, supply exceeds demand, and supply much larger than demand. The balance between supply and demand is a most suitable setting for Suzhou City to develop, and the next is the type in which the supply exceeds demand. The other two types cannot meet the development requirements. We concluded that it is easier to adjust the investment than to adjust the labor input when adjusting the control variables to change the industrial water supply. While drawing the ideal combination of investment and labor input, a reasonable range of investment and labor input is also provided: the scope of investment adjustment is 0.6 I 0 − 1.1 I 0 , and the adjustment range of labor input is 0.5 P 0 − 1.2 P 0 .</description><subject>Climate change</subject><subject>Consumption</subject><subject>Decision making</subject><subject>Dynamic models</subject><subject>Efficiency</subject><subject>Electric industries</subject><subject>Industrial water</subject><subject>Irrigation</subject><subject>Labor</subject><subject>Linear programming</subject><subject>Management decisions</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Planning</subject><subject>Population growth</subject><subject>Production functions</subject><subject>Regions</subject><subject>River networks</subject><subject>Simulation</subject><subject>Supply & demand</subject><subject>System dynamics</subject><subject>Trends</subject><subject>Water conservation</subject><subject>Water demand</subject><subject>Water shortages</subject><subject>Water supply</subject><subject>Water use</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNkL1OwzAAhC0EElXpwhNYYkMK-DdO2KClUKmIoVSMke3YKFXiBDsesvEOvCFPQkqR4Ja74dOddACcY3RFaY6uQ8SYYJ7l9AhMCBI4wYij43_5FMxC2KFRlOIcpxPgtl5JB1eujKH3lazhq-yNh5vYdfUApSvhwjSj3cDNEHrTwMXgZFNp-NSWpv4BNlUTa9lXrYN3MpgSjmHeKvX18blo41stA1xGp_fAGTixsg5m9utTsF3ev8wfk_Xzw2p-u040yXmfKJUyw1LFLDLCMipMqpXNCUMkY6UkkgqhWJnmzFBrMk41IlpTmoqSoIxbOgUXh97Ot-_RhL7YtdG7cbIgnGFKBM_JSF0eKO3bELyxReerRvqhwKjYX1r8XUq_AUWYaX0</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Li, Kebai</creator><creator>Ma, Tianyi</creator><creator>Wei, Guo</creator><creator>Zhang, Yuqian</creator><creator>Feng, Xueyan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20191101</creationdate><title>Urban Industrial Water Supply and Demand: System Dynamic Model and Simulation Based on Cobb–Douglas Function</title><author>Li, Kebai ; Ma, Tianyi ; Wei, Guo ; Zhang, Yuqian ; Feng, Xueyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-bb64e46b4f0e7f437e6cbf9240284da2a377b4d694e3fe853c02cc3367d2085f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Climate change</topic><topic>Consumption</topic><topic>Decision making</topic><topic>Dynamic models</topic><topic>Efficiency</topic><topic>Electric industries</topic><topic>Industrial water</topic><topic>Irrigation</topic><topic>Labor</topic><topic>Linear programming</topic><topic>Management decisions</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Planning</topic><topic>Population growth</topic><topic>Production functions</topic><topic>Regions</topic><topic>River networks</topic><topic>Simulation</topic><topic>Supply & demand</topic><topic>System dynamics</topic><topic>Trends</topic><topic>Water conservation</topic><topic>Water demand</topic><topic>Water shortages</topic><topic>Water supply</topic><topic>Water use</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Kebai</creatorcontrib><creatorcontrib>Ma, Tianyi</creatorcontrib><creatorcontrib>Wei, Guo</creatorcontrib><creatorcontrib>Zhang, Yuqian</creatorcontrib><creatorcontrib>Feng, Xueyan</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Kebai</au><au>Ma, Tianyi</au><au>Wei, Guo</au><au>Zhang, Yuqian</au><au>Feng, Xueyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Urban Industrial Water Supply and Demand: System Dynamic Model and Simulation Based on Cobb–Douglas Function</atitle><jtitle>Sustainability</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>11</volume><issue>21</issue><spage>5893</spage><pages>5893-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>In order to meet the needs of water-saving society development, the system dynamics method and the Cobb–Douglas (C–D) production function were combined to build a supply and demand model for urban industrial water use. In this model, the industrial water demand function is expressed as the sum of the general industrial water demand and the power industry water demand, the urban water supply function is expressed as the Cobb–Douglas production function, investment and labor input are used as the control variables, and the difference between supply and demand in various situations is simulated by adjusting their values. In addition, the system simulation is conducted for Suzhou City, Jiangsu Province, China, with 16 sets of different, carefully designed investment and labor input combinations for exploring a most suitable combination of industrial water supply and demand in Suzhou. We divide the results of prediction into four categories: supply less than demand, supply equals demand, supply exceeds demand, and supply much larger than demand. The balance between supply and demand is a most suitable setting for Suzhou City to develop, and the next is the type in which the supply exceeds demand. The other two types cannot meet the development requirements. We concluded that it is easier to adjust the investment than to adjust the labor input when adjusting the control variables to change the industrial water supply. While drawing the ideal combination of investment and labor input, a reasonable range of investment and labor input is also provided: the scope of investment adjustment is 0.6 I 0 − 1.1 I 0 , and the adjustment range of labor input is 0.5 P 0 − 1.2 P 0 .</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su11215893</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Climate change Consumption Decision making Dynamic models Efficiency Electric industries Industrial water Irrigation Labor Linear programming Management decisions Methods Neural networks Planning Population growth Production functions Regions River networks Simulation Supply & demand System dynamics Trends Water conservation Water demand Water shortages Water supply Water use |
| title | Urban Industrial Water Supply and Demand: System Dynamic Model and Simulation Based on Cobb–Douglas Function |
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