基于动态最优化模型预测福建省污水污泥再利用和城市可持续发展

doi:10.18402/resci.2021.03.05

资源科学 ›› 2021, Vol. 43 ›› Issue (3): 477-488.doi: 10.18402/resci.2021.03.05

基于动态最优化模型预测福建省污水污泥再利用和城市可持续发展

柯文岚¹(), 闫晶晶²(), 吴容容³, 张国丰⁴, 沙景华²

1.福建江夏学院经济贸易学院,福州 350108
2.中国地质大学（北京）经济管理学院,北京 100083
3.阳光学院,福州 350001
4.河北地质大学经济学院,石家庄 050031

收稿日期:2020-09-04 修回日期:2021-01-26 出版日期:2021-03-25 发布日期:2021-05-25
通讯作者: 闫晶晶
作者简介:柯文岚,女,福建泉州人,讲师,研究方向为资源环境经济管理、产业经济学、区域经济学。E-mail: kewenlan1988@hotmail.com
基金资助:
国家自然科学基金面上项目(41971258);福建省社会科学规划项目(FJ2019B144);河北省高等学校人文社会科学研究项目(SD201060)

Reuse potential of wastewater and sludge in Fujian Province based on a dynamic optimization model and sustainable urban development

KE Wenlan¹(), YAN Jingjing²(), WU Rongrong³, ZHANG Guofeng⁴, SHA Jinghua²

1. Shool of Economics and Trade, Fujian Jiangxia University, Fuzhou 350108, China
2. School of Economics and Management, China University of Geosciences, Beijing 100083, China
3. Yango University, Fuzhou 350001, China
4. School of Economics, Hebei GEO University, Shijiazhuang 050031, China

Received:2020-09-04 Revised:2021-01-26 Online:2021-03-25 Published:2021-05-25
Contact: YAN Jingjing

摘要/Abstract

摘要：

目前中国从高速发展转向高质量发展阶段,城市规模迅速扩大,城市污水、污泥大量排放,污水污泥合理处置和资源化利用等问题成为了水污染治理与能源化利用的关键。本文基于投入产出方法构建区域水环境-经济-能源系统动态最优化模型,模拟分析2012—2025年福建省9市污水污泥再利用、环境保护与城市经济增长间均衡发展路径。研究发现：①引入先进技术的环境综合政策系统对经济增长有显著的促进作用,在COD年均减排3%,能源消耗为14500万t标准煤的约束下,通过技术引入和政策调控,到2025年,福建省新增污水处理能力3.7亿m³,新增污泥处理量37.6万t,GDP年均增速达到8.6%。随着环境约束增加、技术投入加大,经济增长具有边际递增效应;②环境政策手段对区域经济影响具有滞后性,地方政府应制定系统的中长期财政计划,以确保环境治理效果的稳定性和持续性;③引入污水污泥处理技术的政策系统为环境治理与经济增长实现“双赢”的发展模式提供了有力支持。运用创新的技术实现节能减排约束下的经济发展目标,有效地促进经济社会发展全面绿色转型。

关键词: 污水污泥处理, 再利用潜力, 动态最优化模型, 投入产出, 福建省

Abstract:

At present, China has changed from the stage of high-speed development to high-quality development, with the rapid expansion of city size, large amount of urban sewage and sludge discharge, rational disposal and resource utilization of sewage and sludge have become the key issues of water pollution control and energy utilization. In this paper, a dynamic optimization model of regional water environment-economy-energy system was established based on the input-output method to simulate and analyze the balanced development path among sewage and sludge reuse, environmental protection and urban economic growth in nine cities of Fujian Province from 2012 to 2025. The results show that: (1) The comprehensive environmental policy system with the introduction of advanced technology has a significant promoting effect on economic growth. Under the constraints of an average annual COD reduction of 3% and an energy consumption of 145 million tons of standard coal, through technology introduction and policy regulation,by 2025, Fujian Province will achieve an additional sewage treatment capacity of 370 million m³, and additional sludge utilization capacity of 376 thousand tons, with an average annual GDP growth rate of 8.6%. With the increase of environmental constraints and technological input, the economic has a marginal increasing effect; (2) The impact of environmental policy on regional economy has obvious lag. Local governments need to systematically formulate medium and long-term financial plans to ensure the stability and sustainability of environmental governance effects. (3) The policy system including introduction of sewage and sludge treatment technology provides strong support for the “win-win” development model of environmental governance and economic growth. And innovative technologies can be used to achieve the economic development goals under the constraints of energy conservation and emission reduction, effectively to promote the comprehensive green transformation of economic and social development.

Key words: wastewater and sludge treatment, reuse potential, dynamic optimization model, input-output, Fujian Province

柯文岚, 闫晶晶, 吴容容, 张国丰, 沙景华. 基于动态最优化模型预测福建省污水污泥再利用和城市可持续发展[J]. 资源科学, 2021, 43(3): 477-488.

KE Wenlan, YAN Jingjing, WU Rongrong, ZHANG Guofeng, SHA Jinghua. Reuse potential of wastewater and sludge in Fujian Province based on a dynamic optimization model and sustainable urban development[J]. Resources Science, 2021, 43(3): 477-488.

图/表 9

图1

表1

表2

图2

表3

图3

图4

图5

图6

参考文献 35

[1]	Wei L L, Zhu F Y, Li Q Y, et al. Development, current state and future trends of sludge management in China: Based on exploratory data and CO₂-equivaient emissions analysis[J]. Environment International, 2020, DOI: 10.1016/j.envint.2020.106093.
[2]	Diaz-Elsayed N, Rezaei N, Guo T, et al. Wasterwater-based resource recovery technologies across scale: A review[J]. Resources Conservation and Recycling, 2019,145:94-112.
[3]	Nyam Y S, Kotir J H, Jordaan A J, et al. Developing a conceptual model for sustainable water resource management and agricultural development: The case of the Breede river catchment area, South Africa[J]. Environmental Management, 2021, DOI: 10.1007/s00267-020-01399-x. doi: 10.1007/s00267-021-01458-x pmid: 33730191
[4]	修红玲, 朱文彬, 韦家兴, 等. 中国水资源承载能力调控关键技术与政策研究[J]. 北京师范大学学报(自然科学版), 2020,56(3):467-473.
	[ Xiu H L, Zhu W B, Wei J X, et al. Key technologies and policies to adjust water resource-carrying capacity[J]. Journal of Beijing Normal University (Natural Science), 2020,56(3):467-473.]
[5]	任鹏飞, 杨永强, 张新妙. 膜生物反应器(MBR)节能减耗的研究进展[J]. 现代化工, 2017,37(12):43-45.
	[ Ren P F, Yang Y Q, Zhang X M. Research progress in energy conversation for membrane bioreactor[J]. Modern Chemical Industry, 2017,37(12):43-45.]
[6]	谢昆, 尹静, 陈星. 中国城市污水处理工程污泥处置技术研究进展[J]. 工业水处理, 2020,40(7):18-23.
	[ Xie K, Yin J, Chen X. Research progress on sludge treatment technology of urban sewage treatment project in China[J]. Industrial Water Treatment, 2020,40(7):18-23.]
[7]	Singh V, Phuleria H C, Chandel M. Estimation of energy recovery potential of sewage sludge in India: Waste to watt approach[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.122538. pmid: 33078048
[8]	Xu Q X, Li X M, Ding R R, et al. Understanding and mitigating the toxicity of cadmium to the anaerobic fermentation of waste activated sludge[J]. Water Research, 2017,124(1):269-279.
[9]	张陈俊, 许静茹, 张丽娜, 等. 长江经济带水资源消耗时空差异驱动效应研究[J]. 资源科学, 2018,40(11):2247-2259.
	[ Zhang C J, Xu J R, Zhang L N, et al. Driving effect of spatial-temporal difference in water resource consumption in the Yangtze River Economic Zone[J]. Resources Science, 2018,40(11):2247-2259.]
[10]	戴俊, 傅彦铭. 环境规制、产业结构对能源效率的影响[J]. 中国农业资源与区划, 2020,41(9):55-63.
	[ Dai J, Fu Y M. The impact of environmental regulation and industrial structure on energy efficiency[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020,41(9):55-63.]
[11]	Zhou X Y, Lei K, Meng W, et al. Industrial structural upgrading and spatial optimization based on water environment carrying capacity[J]. Journal of Cleaner Production, 2017,165:1462-1472.
[12]	Yang X, Pang J, Teng F, et al. The environmental co-benefit and economic impact of China’s low-carbon pathways: Evidence from linking bottom-up and top-down models[J]. Renewable & Sustainable Energy Reviews, 2021, DOI: 10.1016/j.rser.2020.110438.
[13]	Gastelum J, Krishnamurthy G, Ochoa N, et al. The use of system dynamics model to enhance integrated resources planning implementation[J]. Water Resources Management, 2018,32:2247-2260.
[14]	Wang X C, Klemes J J, Wang Y T, et al. Water-Energy-Carbon Emissions nexus analysis of China: An environmental input-output model-based approach[J]. Applied Energy, 2020, DOI: 10.1016/j.apenergy.2019.114431. pmid: 32952265
[15]	Halkos G, Tsilika K. Dynamic input-output models in environmental problems: A computational approach with CAS software[J]. Computational Economics, 2016,47(3):489-497.
[16]	Ma Y, Song Z, Li S Q, et al. Dynamic evolution analysis of the factors driving the growth of energy-related CO₂ emissions in China: An input-output analysis[J]. PLoS ONE, 2020, DOI: 10.1371/journal.pone.0243557. pmid: 33905439
[17]	Bekchanov M, Sood A, Pinto A, et al. Systematic review of water economy modeling applications[J]. Journal of Water Resources Planning and Management, 2017, DOI: 10.1061/(ASCE)WR.1943-5452.0000793. pmid: 31666759
[18]	Zhou Q, Yabar H, Mizunoya T, et al. Exploring the potential of introducing technology innovation and regulations in the energy sector in China: A regional dynamic evaluation model[J]. Journal of Cleaner Production, 2016,112:1537-1548.
[19]	Kang J D, Ng T S, Su B. Optimizing electricity mix for CO₂ emissions reduction: A robust input-output linear programming model[J]. European Journal of Operational Research, 2020,287(1):280-292.
[20]	Freire-González J, Decker C A, Hall J W. A linear programming approach to water allocation during a drought[J]. Water, 2018,10:363.
[21]	Yang W, Song J N, Higano Y, et al. Exploration and assessment of optimal policy combination for total water pollution control with a dynamic simulation model[J]. Journal of Cleaner Production, 2015,102:342-352.
[22]	Song C, Yan J J, Sha J H, et al. Dynamic modeling application for simulating optimal policies on water conservation in Zhangjiakou City, China[J]. Journal of Cleaner Production, 2018,201:111-122.
[23]	Ke W L, Sha J H, Yan J J, et al. A multi-objective input-output linear model for water supply, economic growth and environmental planning in resource-based cities[J]. Sustainability, 2016, DOI: 10.3390/su8020160. pmid: 29682334
[24]	Hao X X, Yan J J, Sha J H, et al. Exploring the synthetic optimal policies for solving problems of agricultural water use with a dynamic optimization simulation model[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.125062.
[25]	Forsund F R. Input-output Models: National Economic Models and The Environment[A]. Kneese A V, Sweeney J L. Handbook of Natural Resource and Energy Economics[M]. Amsterdam: Elsevier Science Pub, 1985.
[26]	Freeman A M, Havemen R H, Kneese A V. The Economics of Environmental Policy[M]. New York: Wiley, 1973.
[27]	彭红松, 郭丽佳, 章锦河, 等. 区域经济增长与资源环境压力的关系研究进展[J]. 资源科学, 2020,42(4):593-606.
	[ Peng H S, Guo L J, Zhang J H, et al. Research progress and implication of the relationship between regional economic growth and resource-environmental pressure[J]. Resources Science, 2020,42(4):593-606.]
[28]	Yan J J, Xu F, Mizunoya T, et al. Endogenous derivation of optimal environmental policies for proper treatment of stockbreeding wastes in the upstream region of the Miyun Reservoir, Beijing[J]. Papers in Regional Science, 2014,93(2):477-500.
[29]	魏忠庆, 胡志荣, 上官海东, 等. 基于数学模拟的污水处理厂设计: 方法与案例[J]. 中国给水排水, 2019,35(10):21-26.
	[ Wei Z Q, Hu Z R, Shangguan H D, et al. Model-based design of sewage treatment plants: Methodology and case studies[J]. China Water & Wastewater, 2019,35(10):21-26.]
[30]	谢詹东, 张小平, 魏江州, 等. 双膜内循环生物反应处理工艺(DMBR)的应用实践与总结[J]. 中国给水排水, 2017,33(18):22-24.
	[ Xie Z D, Zhang X P, Wei J Z, et al. Application and summary of double membrane internal cycling bioreactor treatment process(DMBR)[J]. China Water & Wastewater, 2017,33(18):22-24.]
[31]	薄荣业, 王敦球, 金樾, 等. 厌氧陶瓷平板膜生物反应器处理生活污水应用研究[J]. 水处理技术, 2015,41(11):103-106.
	[ Bo R Y, Wang D Q, Jin Y, et al. Study on anaerobic ceramic membrane bioreactor in treating domestic sewage[J]. Technology of Water Treatment, 2015,41(11):103-106.]
[32]	任鸿梅, 任龙飞. 萃取式膜生物反应器在水处理中的应用[J]. 净水技术, 2017,36(2):90-92.
	[ Ren H M, Ren L F. Application of extractive membrane bioreactor in wastewater treatment[J]. Water Purification Technology, 2017,36(2):90-92.]
[33]	肖本益, 唐心漪, 易皓, 等. 两种污泥高级厌氧消化工艺的比较: 高温热处理和低温热碱处理[J]. 环境卫生工程, 2020,28(2):94.
	[ Xiao B Y, Tang X Y, Yi H, et al. Comparison of two advanced anaerobic digestions of sewage sludge with high-temperature thermal pretreatment and low-temperature thermal-alkaline pretreatment[J]. Environmental Sanitation Engineering, 2020,28(2):94.]
[34]	王涛, 宿宇. 污泥处理处置技术路线综述[J]. 中国环保产业, 2020, (1):51-55.
	[ Wang T, Su Y. Analysis on stabilization, reducing, harmlessness and resource levels of sludge treatment and disposal technical route[J]. China Environmental Protection Industry, 2020, (1):51-55.]
[35]	汤姆·蒂坦伯格, 琳恩·刘易斯, 著. 环境与自然资源经济学(第八版)[M]. 王晓霞, 杨鹂, 石磊, 译. 北京: 中国人民大学出版社, 2011.
	[ Tietenberg T, Lewis L. Environmental and Natural Resource Economics (8th Edition)[M]. Wang X X, Yang L, Shi L, Trans. Beijing: Renmin University of China Press, 2011.]

	MBR	DMBR	CMBR	EMBR
建设费用/百万元	50	165	70	5
污水处理量/（百万t/年）	18.00	36.50	10.95	1.50
再生水生产量/百万t	13.09	29.20	9.30	1.43
中水产出率/%	0.77	0.80	0.85	0.95
环境效率/(kg/万t)	3300	3450	3540	5600
投资效率/（万t/万元）	0.36	0.22	0.16	0.30
运行成本/（元/t）	1.5	3.0	3.6	1.8

	技术路线	投资/ (元/t)	运行成本/ (元/t)	污泥处理能力/千t	发电量/(kW·h/t)
a	A-D-F	526	714	50	207
b	F-C	500	1250	50	277

	福州	厦门	莆田	三明	泉州	漳州	南平	龙岩	宁德
2012	52.0	72.3	36.4	35.7	39.1	28.8	34.8	48.8	45.4
2025	87.6	90.4	58.6	44.7	63.0	40.0	40.0	62.5	69.4

基于动态最优化模型预测福建省污水污泥再利用和城市可持续发展

Reuse potential of wastewater and sludge in Fujian Province based on a dynamic optimization model and sustainable urban development

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 35

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	宋周莺, 陶蕾, 刘卫东. 海南对外贸易格局演化及其与国内省区市的经济关联[J]. 资源科学, 2021, 43(2): 256-268.
[2]	李晖, 姜文磊, 唐志鹏. 全球贸易隐含碳净流动网络构建及社团发现分析[J]. 资源科学, 2020, 42(6): 1027-1039.
[3]	李艳梅,牛苗苗,张红丽. 京津冀区域内增加值贸易的经济收益和隐含碳排放比较[J]. 资源科学, 2019, 41(9): 1619-1629.
[4]	杨国永, 江强, 田甜, 许文兴. 农民工回乡建房的家庭福利效应——基于福建省农民工流出地的调查[J]. 资源科学, 2019, 41(7): 1213-1226.
[5]	侯秀英, 邱荣祖, 林玉英, 王剑凯, 胡喜生. 交通可达性反贫困作用的空间分异分析——以福建省为例[J]. 资源科学, 2019, 41(11): 2094-2106.
[6]	韩雅清, 林丽梅, 魏远竹, 苏时鹏, 许佳贤. 劳动力转移、合作经营与林业生产效率研究[J]. 资源科学, 2018, 40(4): 838-850.
[7]	李艳梅, 孙丽云, 牛苗苗. 再生资源产业关联及宏观经济效应分析[J]. 资源科学, 2018, 40(3): 580-588.
[8]	张兵兵, 李祎雯. 新附加值贸易视角下中日贸易隐含碳排放的再测算[J]. 资源科学, 2018, 40(2): 250-261.
[9]	余晓泓, 詹夏颜. 基于收益原则的碳排放转移及中国碳排放责任研究[J]. 资源科学, 2018, 40(1): 185-194.
[10]	王丽萍, 刘明浩. 基于投入产出法的中国物流业碳排放测算及影响因素研究[J]. 资源科学, 2018, 40(1): 195-206.
[11]	郭湘宇, 吴正方, 杜海波, 王雷, 杨满根, 陈志彪. 福建省极端降水时空变化特征及其环流因素分析[J]. 资源科学, 2017, 39(6): 1084-1098.
[12]	尹伟华, 张亚雄, 李继峰, 徐丽萍, 王玢. 基于投入产出表的中国八大区域碳排放强度分析[J]. 资源科学, 2017, 39(12): 2258-2264.
[13]	孙艳芝, 沈镭, 钟帅, 刘立涛, 武娜, 李林朋, 孔含笑. 中国碳排放变化的驱动力效应分析[J]. 资源科学, 2017, 39(12): 2265-2274.
[14]	李艳梅, 孙丽云, 张红丽, 刘婷婷. 京津冀区域间产业转移对能源消费碳排放强度的影响[J]. 资源科学, 2017, 39(12): 2275-2286.
[15]	张红丽, 沈镭, 李艳梅. 京津冀经济活动隐含的碳排放转移——基于多区域投入产出模型的分析[J]. 资源科学, 2017, 39(12): 2287-2298.