江苏省工业用水供需系统动力学模型与工业用水生产要素投入仿真研究

doi:10.13776/j.cnki.resourcesindustries.20230717.007

摘要/Abstract

摘要： 为了研究如何根据工业用水供需变化对工业用水生产要素投入做出适当调整,以促进我国工业用水的优化配置。本文选择具有代表性的江苏省作为研究对象,在选取江苏省工业用水相关数据的基础上,首先使用幂函数描述每万元工业增加值用水量,使用线性函数描述工业增加值,以幂函数与线性函数的乘积拟合工业用水需水量,以柯布-道格拉斯(C-D)函数建立工业用水供水生产函数,然后将需水函数、C-D函数、2种固定资产折旧法与系统动力学方法相结合,进而构建江苏省工业用水供需系统动力学模型,并运用该模型探究江苏省工业用水供需变化与工业用水生产要素投入之间的内在联系；其次将江苏省工业用水供水行业固定资产投资额和劳动力投入量作为调节供水的控制变量,设计了5种生产要素投入组合方案,运用仿真方法研究控制变量的最佳取值组合方案；最后运用敏感性分析检验本文构建的江苏省工业用水供水模型的稳健性。研究结果表明:1)系统动力学(SD)模型具有一定的复杂性,要获得SD模型的满意解,就需要对根据决定系数得到的单变量计算公式进行必要的修正。2)2010—2019年江苏省工业用水需水量持续下降,但从2020年开始又转变为了小幅增长的发展趋势,这是由于江苏省工业增加值用水量的增幅大于该省万元工业增加值用水量的降幅所导致的结果。3)当工业用水需水量下降时,工业用水供水行业可以通过缩减生产要素投入以减少工业用水供水；当工业用水需水量上升时,工业用水供水行业可以通过加大生产要素投入以增加工业用水供水。4)在本文设计的5种生产要素投入组合方案中,经计算分析得知方案3的工业用水供水量与需水量最为接近,为最优方案。对方案3进行敏感性分析后表明,本文构建的工业用水供水模型对参数变化敏感性较低,具有较好的稳健性。

关键词: 工业用水供需系统动力学模型, 工业用水生产要素投入, 仿真研究, 江苏省

Abstract: In order to study how to adjust production elements input according to demand-supply changes of industrial water use for a optimized allocation of China's industrial water use, this paper uses power function to describe water consumption per 10 000 yuan industrial added value in Jiangsu province, and uses linear function to mark added industrial values, product of both can be used to simulate the industrial water demand. Cobb-Douglas function is used set up industrial water supply production function, combined with water demand function, C-D function, 2 fixed asset depreciation methods and system dynamics method, which is applied construct dynamics model of Jiangsu's industrial water demand-supply. This model is used to study the inner relation between Jiangsu's industrial water demand-supply changes and production elements input. This paper uses fixed asset investment and labor inputs in Jiangsu's industrial water supply industry as water supply controlling variable, and designs 5 combination plans of production elements input, and applies simulation to study the optimal combination plan of controlling variable. Sensitivity analysis was used to test the robustness of the industrial water supply model constructed in Jiangsu province. Systematic dynamics model is of complexity whose sole variable formula needs to be corrected for obtaining SD's satisfying solution. Jiangsu's industrial water use has stably declined during 2010 to 2019, turned to be slightly up since 2020, mainly due to the increase of water consumption of industrial added value greater than the decreased water consumption of per 10 000 yuan of industrial added values. Industrial water supply can be decreased through reducing production elements input when industrial water demand declines, and vice versa. Among the five combination plans of production elements input, plan 3 has industrial water supply close to demand, a optimal plan. Its sensitiveness suggests that the industrial water supply model is less sensitive to parameters change, so it has good robustness.

Key words: systematic dynamics model of industrial water supply-demand, production elements input of industrial water use, simulation； Jiangsu province

中图分类号:

TV213.4：F427

李可柏, 丁志蕾. 江苏省工业用水供需系统动力学模型与工业用水生产要素投入仿真研究[J]. 资源与产业, 2023, 25(4): 127-138.

LI Kebai, DING Zhilei. SIMULATION OF DEMAND-SUPPLY SYSTEM DYNAMICS MODEL AND PRODUCTION ELEMENTS INPUT OF JIANGSU'S INDUSTRIAL WATER USE[J]. Resources & Industries, 2023, 25(4): 127-138.

参考文献

崔东文, 包艳飞, 2020. 基于人工生态系统优化算法的组合生长需水预测模型[J]. 水资源保护, 36(6): 122-130.〔CUI D W, BAO Y F, 2020. Combined growth water demand forecasting model based on artificial ecosystem optimization algorithm[J].Water Resource Protection, 36(6): 122-130.〕

范琳琳, 2013. 固定资产加速折旧法在奖励积分中的应用[J]. 财会通讯(28): 65-67.

姜孟, 李华姣, 2016. 基于系统动力学的区域页岩气开发水资源承载力评价模型研究[J]. 资源与产业, 18(6): 33-39. 〔JIANG M, LI H J, 2016. Evaluation model of regional water carrying capacity in shale gas development based on system dynamics method[J]. Resources & Industries, 18(6): 33-39.〕

李可柏, 2015. 基于Logistic和C-D函数的城市生活用水动态优化及仿真[J]. 上海交通大学学报, 49(2): 178-183.〔LI K B, 2015. Dynamic optimization and simulation of urban domestic water based on logistic and C-D function [J]. Journal of Shanghai Jiao Tong University, 49(2): 178-183.〕

李玲, 潘雪倩, 夏威夷, 等, 2018. 基于SD模型的重庆市水资源承载力模拟分析[J]. 中国农村水利水电(5): 128-133.〔LI L, PAN X Q, XIA W Y, et al, 2018. Simulation and analysis of water resources carrying capacity in Chongqing based on SD model[J]. China Rural Water and Hydropower(5): 128-133.〕

刘婧尧, 胡雨村, 金相哲, 2014. 基于系统动力学的天津市水资源可持续利用[J]. 华中师范大学学报(自然科学版), 48(1): 106-111.〔LIU J Y, HU Y C, JIN X Z, 2014. Sustainable utilization of water resources in Tianjin based on system dynamics [J]. Journal of Central China Normal University(Natural Sciences), 48(1): 106-111.〕

罗龙溪, 吴建平, 陈云, 等, 2019. 基于系统动力学的北京供需水关系仿真研究[J]. 系统仿真学报, 31(12): 2790-2801.〔LUO L X, WU J P, CHEN Y, et al, 2019. System dynamics modeling for simulation of water supply and demand relationship in Beijing[J]. Journal of System Simulation, 31(12): 2790-2801.〕

秦欢欢, 张保祥, 孟凡海, 2017. 基于系统动力学的龙口市水资源供需问题研究[J]. 水资源与水工程学报, 28(4): 45-49, 57.〔QIN H H, ZHANG B X, MENG F H, 2017. Study on water supply and demand of Longkou city based on system dynamics[J]. Journal of Water Resources and Water Engineering, 28(4): 45-49, 57.〕

秦欢欢, 郑春苗, 2018. 基于宏观经济模型和系统动力学的张掖盆地水资源供需研究[J]. 水资源与水工程学报, 29(1): 9-17.〔QIN H H, ZHENG C M, 2018. Study on water resources supply and demand of Zhangye Basin based on macroeconomic model and system dynamics[J]. Journal of Water Resources and Water Engineering, 29(1): 9-17.〕

秦剑, 2015. 水环境危机下北京市水资源供需平衡系统动力学仿真研究[J]. 系统工程理论与实践, 35(3): 671-676.〔QIN J, 2015. Dynamics simulation based on Beijing water supply and demand balance system under emergency situations[J]. Systems Engineering-Theory & Practice, 35(3): 671-676.〕

单义明, 杨侃, 2021. 基于灰色关联度分析的山西省PSO-SVR需水量预测模型[J]. 水电能源科学, 39(2): 18-21.〔SHAN Y M, YANG K, 2021. Forecasting model of PSO-SVR water requirement in shanxi province based on grey correlation analysis[J]. Water Resources and Power, 39(2): 18-21.〕

粟晓玲, 谢娟, 周正弘, 2020. 基于SD变化环境下农业水资源供需平衡模拟[J]. 排灌机械工程学报, 38(3): 285-291.〔SU X L, XIE J, ZHOU Z H, 2020. System dynamics model-based simulation of agricultural water resources supply and demand balance in changing environments[J].Journal of Drainage and Irrigation Machinery Engineering, 38(3): 285-291.〕

熊远南, 2020. 基于改进灰色—多元回归组合预测模型的燃煤电厂智慧水务研究[J]. 化工进展, 39(S2): 393-400. 〔XIONG Y N, 2020. Smart water affair of coal-fired power plant based on improved combination prediction model of grey system and regression analysis[J].Chemical Industry and Engineering Progress, 39(S2): 393-400.〕

许纪校, 王尧, 唐勇军, 2022. 红线控制下高质量水资源优化配置: 以南京市为例[J]. 资源与产业, 24(5): 70-80.〔XU J X, WANG Y, TANG Y J, 2022. A case study on Nanjing: optimized allocation of quality water resource under red line control[J]. Resources & Industries, 24(5): 70-80.〕

杨明杰, 杨广, 高普章, 等, 2018. 基于SD模型的干旱区城市水资源供需分析: 以新疆克拉玛依市为例[J]. 人民长江, 49(5): 45-51, 73.〔YANG M J, YANG G, GAO P Z, et al, 2018. Analysis of the supply and demand of water resources for city in arid region based on SD model: a case of Karamay, Xinjiang [J]. Yangtze River, 49(5): 45-51, 73.〕

周庆海, 万希宁, 2011. 固定资产折旧方法比较及应用思考[J]. 财会月刊(23): 48-49.

AALIREZAEI A, KHAN M S A, KABIR G, et al, 2021. Prediction of water security level for achieving sustainable development objectives in Saskatchewan, Canada: implications for resource conservation in developed economies[J].Journal of Cleaner Production, 311:127521.

AHMADI M H, ZARGHAMI M, 2019. Should water supply for megacities depend on outside resources?： a Monte-Carlo system dynamics simulation for Shiraz, Iran[J]. Sustainable Cities and Society, 44: 163-170.

FANG W, AN H Z, LI H J, et al, 2017. Accessing on the sustainability of urban ecologicaleconomic systems by means of a coupled emergy and system dynamics model: a case study of Beijing[J]. Energy Policy, 100(1): 326-337.

GOHARI A, MIRCHI A, MADANI K, 2017. System dynamics evaluation of climate change adaptation strategies for water resources management in central Iran[J]. Water Resources Management, 31(5): 1413-1434.

HERBERT Z C, ASGHAR Z, OROZA C A, 2021. Long-term reservoir inflow forecasts: enhanced water supply and inflow volume accuracy using deep learning[J]. Journal of Hydrology, 601.DOI： 10. 1016/j. jhydrol. 2021. 126676.

LI K B, MA T Y, DOOLING T, et al, 2019a. Urban comprehensive water consumption: nonlinear control of production factor input based upon the C-D function[J]. Sustainability, 11(4): 1125.

LI K B, MA T Y, WEI G, et al, 2019b. Urban industrial water supply and demand: system dynamic model and simulation based on Cobb-Douglas function[J]. Sustainability, 11(21): 5893.

LI K B, HAJAR S, DING Z L, et al, 2020. Dynamic optimization of input production factors for urban industrial water supply and demand management[J].Journal of Environmental Management, 270:110807.

NARAYANAN L K, SANKARANARAYANAN S, 2020. IoT-based water demand forecasting and distribution design for smart city[J]. Journal of Water and Climate Change, 11(4): 1411-1428.

PEREA G R, POYATO E C , MONTESINOS P, et al, 2019. Optimisation of water demand forecasting by artificial intelligence with short data sets[J]. Biosystems Engineering, 177: 59-66.

PLUCINSKI B, SUN Y, WANG S Y S, et al, 2019. Feasibility of multi-year forecast for the colorado river water supply: time series modeling[J]. Water, 11(12): 2433.

SMOLAK K, KASIECZKA B, FIALKIEWICZ W, et al, 2020. Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models[J]. Urban Water Journal, 17(1): 32-42.

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