中国人海经济系统环境适应性演化及预警
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摘要
基于敏感性-稳定性-响应3维要素构建指标体系,运用熵值法和ARIMA-BP组合预测模型研究中国人海经济系统环境适应性的演化及预警。结果表明:①2001～2016年中国人海经济系统环境适应性呈稳定上升态势,总体集中于中警状态,期间经历了"人海环境系统比较优势阶段→耦合协调阶段→人海经济系统比较优势阶段"的双螺旋适应过程,预计2017～2020年再次进入相互契合的轻警状态;②16 a间中国人海经济系统环境适应性波动存在上升期短-衰退期长现象,预计未来4 a人海经济系统环境适应性在经济下行和生态约束背景下的速率不容乐观;③权衡人海经济系统和人海环境系统的关系,追求总体效益最大化,延长适应性周期波动中扩张期活动,差别化和灵活性的适应行为是未来主要排警对策。
Constructing environmental adaptability index system of human-sea economic system based on adaptability factors of sensitivity, stability and response. The entropy method was used to measure the environmental adaptability of human-sea economic system from 2001 to 2016. Combined with ARIMA-BP combined forecasting model, this article forecasts the environmental adaptability of the Chinese human-sea economic system from 2017 to 2020 and makes a detailed analysis of the light display mechanism. The results show that: 1) The environmental adaptability of the Chinese human-sea economic system continued to increase from2001 to 2016, the warning degree rose from serious alert to slight alert and the indicator lamp turned from orange lamp to blue lamp, and 70% of the years in the medium alert. The environmental adaptability evolutionary process of human-sea economic system is a trade-off between human-sea economic system and the humansea environment system, which experienced a comparative advantage phase of human-sea environment system→coupling and coordination stage→the comparative advantage phase of human-sea economic system. It is estimated that it will re-enter the highly coordinated phase of slight alert state from 2017 to 2020. 2) From 2001 to 2016, there was a short rise period and a long decline in the environmental adaptability of the Chinese human-sea economic system. It is estimated that the rate of environmental adaptability fluctuation of the Chinese human-sea economic system will not be optimistic in the context of economic downturn and ecological constraints from 2017 to 2020. 3) The ARIMA-BP combination forecasting model has a good simulation effect,and it is feasible to apply it to the environmental adaptability pre-warning study of the human-sea economic system. 4) The environmental adaptability evolutionary process of human-sea economic system is unique and dynamic, thus, it is the main policing strategy for the future to weigh the relationship between the human-sea economic system and the human-sea environment system in pursuit of maximizing the overall efficiency and extending the expansion period of adaptive cyclical fluctuations, and to develop a differentiated and flexible adaptation action policy.
Constructing environmental adaptability index system of human-sea economic system based on adaptability factors of sensitivity, stability and response. The entropy method was used to measure the environmental adaptability of human-sea economic system from 2001 to 2016. Combined with ARIMA-BP combined forecasting model, this article forecasts the environmental adaptability of the Chinese human-sea economic system from 2017 to 2020 and makes a detailed analysis of the light display mechanism. The results show that: 1) The environmental adaptability of the Chinese human-sea economic system continued to increase from2001 to 2016, the warning degree rose from serious alert to slight alert and the indicator lamp turned from orange lamp to blue lamp, and 70% of the years in the medium alert. The environmental adaptability evolutionary process of human-sea economic system is a trade-off between human-sea economic system and the humansea environment system, which experienced a comparative advantage phase of human-sea environment system→coupling and coordination stage→the comparative advantage phase of human-sea economic system. It is estimated that it will re-enter the highly coordinated phase of slight alert state from 2017 to 2020. 2) From 2001 to 2016, there was a short rise period and a long decline in the environmental adaptability of the Chinese human-sea economic system. It is estimated that the rate of environmental adaptability fluctuation of the Chinese human-sea economic system will not be optimistic in the context of economic downturn and ecological constraints from 2017 to 2020. 3) The ARIMA-BP combination forecasting model has a good simulation effect,and it is feasible to apply it to the environmental adaptability pre-warning study of the human-sea economic system. 4) The environmental adaptability evolutionary process of human-sea economic system is unique and dynamic, thus, it is the main policing strategy for the future to weigh the relationship between the human-sea economic system and the human-sea environment system in pursuit of maximizing the overall efficiency and extending the expansion period of adaptive cyclical fluctuations, and to develop a differentiated and flexible adaptation action policy.
引文
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[12]殷克东.中国海洋经济周期波动监测预警研究[M].北京:人民出版社,2016.[Yin Kedong. The research on the monitoring and warning of marine business cycle fluctuation in China. Beijing:People's Publishing House, 2016.]
[13]倪海儿,周瑞娟.舟山渔场渔业资源可持续利用水平的灰色评价与预测[J].海洋与湖沼,2009,40(3):319-324.[Ni Haier, Zhou Ruijuan. Grey evaluation and forecast of sustainable use of fisheries resources in Zhoushan fisheries ground. Oceanologia et Limnologia Sinica, 2009, 40(3):319-324.]
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[15]郭付友,佟连军,魏强,等.吉林省松花江流域产业系统环境适应性时空分异与影响因素[J].地理学报,2016,71(3):459-470.[Guo Fuyou, Tong Lianjun, Wei Qiang et al. Spatio-temporal difference and influencing factors of environmental adaptability assessment of industrial system in the Songhua River Basin of Jilin Province. Acta Geographica Sinica, 2016, 71(3):459-470.]
[16]方修琦,殷培红.弹性,脆弱性和适应——IHDP三个核心概念综述[J].地理科学进展,2007,26(5):11-22.[Fang Xiuqi, Yin Peihong. Review on the three key concepts of resilience, vulnerability and adaptation in the research of global environmental change. Progress in Geography, 2007, 26(5):11-22.]
[17]Gallopín G C. Linkages between vulnerability, resilience, and adaptive capacity[J]. Global Environmental Change, 2006, 16(3):293-303.
[18]国家海洋局.中国海洋统计年鉴[M].北京:海洋出版社,2001-2015.[State Oceanic Administration. China marine statistical yearbook. Beijing:China Ocean Press, 2001-2015.]
[19]韩瑞玲,佟连军,朱绍华,等.基于ARMA模型的沈阳经济区经济与环境协调发展研究[J].地理科学,2014,34(1):32-39.[Han Ruilin, Tong Lianjun, Zhu Shaohua et al. The coordinated development of economy and environment based on ARMA model in Shenyang economic zone. Scientia Geographica Sinica,2014, 34(1):32-39.]
[20]陈晓红,吴广斌,万鲁河.基于BP的城市化与生态环境耦合脆弱性与协调性动态模拟研究——以黑龙江省东部煤电化基地为例[J].地理科学,2014,34(11):1337-1343.[Chen Xiaohong,Wu Guangbin, Wan Luhe. Dynamic simulation of vulnerability and coordination of the coupling of urban and ecological environment based on BP:A case of the coal-electricity base in the Eastern Heilongjiang Province. Scientia Geographica Sinica,2014, 34(11):1337-1343.]
[21]雷可为,陈瑛.基于BP神经网络和ARIMA组合模型的中国入境游客量预测[J].旅游学刊,2007,22(4):20-25.[Lei Kewei,Chen Ying. Forecast of inbound tourists to China based on BP neural network and ARIMA combined model. Tourism Tribune,2007, 22(4):20-25.]
[22]赵成柏,毛春梅.基于ARIMA和BP神经网络组合模型的我国碳排放强度预测[J].长江流域资源与环境,2012,21(6):665-671.[Zhao Chengbai, Mao Chunmei. Forecast of intensity of carbon emission to China based on BP neural network and ARIMA combined mode. Resources and Environment in the Yangtze Basin, 2012, 21(6):665-671.]
[2]温晓金,杨新军,王子侨.多适应目标下的山地城市社会-生态系统脆弱性评价[J].地理研究,2016,35(2):299-312.[Wen Xiaojin, Yang Xinjun, Wang Ziqiao. Assessment on the vulnerability of social-ecological systems in a mountainous city depending on multi-targets adaption. Geographical Research, 2016, 35(2):299-312.]
[3]张小飞,彭建,王仰麟,等.全球变化背景下景观生态适应性特征[J].地理科学进展,2017,36(9):1167-1175.[Chang Hsiaofei,Peng Jian, Wang Yanglin et al. Characteristics of landscape system in the context of global change. Progress in Geography,2017, 36(9):1167-1175.]
[4]李博,张志强,苏飞,等.环渤海地区海洋产业生态系统适应性时空演变及影响因素[J].地理科学,2017,37(5):701-708.[Li Bo, Zhang Zhiqiang, Su Fei et al. Spatio-temporal evolution and influencing factors of marine industrial ecosystem adaptability in the Bohai Sea Region. Scientia Geographica Sinica,2017, 37(5):701-708.]
[5]黎洁.陕西安康移民搬迁农户的生计适应策略与适应力感知[J].中国人口·资源与环境,2016,26(9):44-52.[Li Jie. Livelihood adaptation strategy and perceived adaptive capacity of rural relocated households in Southern Shaanxi province, China. China Population Resources and Environment, 2016, 26(9):44-52.]
[6]Bradley M, Putten I V, Sheaves M. The pace and progress of adaptation:Marine climate change preparedness in Australia’s coastal communities[J]. Marine Policy, 2015, 53:13-20.
[7]Alfieri L, Feyen L, Baldassarre G D. Increasing flood risk under climate change:A pan-European assessment of the benefits of four adaptation strategies[J]. Climatic Change, 2016, 136(3-4):507-521.
[8]Stewart M G. Risk and economic viability of housing climate adaptation strategies for wind hazards in southeast Australia[J].Mitigation&Adaptation Strategies for Global Change, 2015, 20(4):1-22.
[9]薛桂芳,徐向欣.国际海底管理局适应性管理办法的推行及中国的应对[J].中国海商法研究,2017,28(2):52-59.[Xue Guifang, Xu Xiangxin. Adaptive management in ISA and China’s response. Chinese Journal of Maritime Law, 2017, 28(2):52-59.]
[10]Holbrook N J, Johnson J E. Climate change impacts and adaptation of commercial marine fisheries in Australia:A review of the science[J]. Climatic Change, 2014, 124(4):703-715.
[11]Mokrech M, Kebede A S, Nicholls R J et al. An integrated approach for assessing flood impacts due to future climate and socio-economic conditions and the scope of adaptation in Europe[J]. Climatic Change, 2015, 128(3-4):245-260.
[12]殷克东.中国海洋经济周期波动监测预警研究[M].北京:人民出版社,2016.[Yin Kedong. The research on the monitoring and warning of marine business cycle fluctuation in China. Beijing:People's Publishing House, 2016.]
[13]倪海儿,周瑞娟.舟山渔场渔业资源可持续利用水平的灰色评价与预测[J].海洋与湖沼,2009,40(3):319-324.[Ni Haier, Zhou Ruijuan. Grey evaluation and forecast of sustainable use of fisheries resources in Zhoushan fisheries ground. Oceanologia et Limnologia Sinica, 2009, 40(3):319-324.]
[14]王佳,黄细嘉,张广海,等.我国沿海地区旅游经济预警评价时空差异研究[J].商业经济与管理,2015,281(3):64-74.[Wang Jia, Huang Xijia, Zhang Guanghai et al. The spatial and temporal differences of tourism economic early warning evaluation in coastal areas in China. Journal of Business Economics, 2015,281(3):64-74.]
[15]郭付友,佟连军,魏强,等.吉林省松花江流域产业系统环境适应性时空分异与影响因素[J].地理学报,2016,71(3):459-470.[Guo Fuyou, Tong Lianjun, Wei Qiang et al. Spatio-temporal difference and influencing factors of environmental adaptability assessment of industrial system in the Songhua River Basin of Jilin Province. Acta Geographica Sinica, 2016, 71(3):459-470.]
[16]方修琦,殷培红.弹性,脆弱性和适应——IHDP三个核心概念综述[J].地理科学进展,2007,26(5):11-22.[Fang Xiuqi, Yin Peihong. Review on the three key concepts of resilience, vulnerability and adaptation in the research of global environmental change. Progress in Geography, 2007, 26(5):11-22.]
[17]Gallopín G C. Linkages between vulnerability, resilience, and adaptive capacity[J]. Global Environmental Change, 2006, 16(3):293-303.
[18]国家海洋局.中国海洋统计年鉴[M].北京:海洋出版社,2001-2015.[State Oceanic Administration. China marine statistical yearbook. Beijing:China Ocean Press, 2001-2015.]
[19]韩瑞玲,佟连军,朱绍华,等.基于ARMA模型的沈阳经济区经济与环境协调发展研究[J].地理科学,2014,34(1):32-39.[Han Ruilin, Tong Lianjun, Zhu Shaohua et al. The coordinated development of economy and environment based on ARMA model in Shenyang economic zone. Scientia Geographica Sinica,2014, 34(1):32-39.]
[20]陈晓红,吴广斌,万鲁河.基于BP的城市化与生态环境耦合脆弱性与协调性动态模拟研究——以黑龙江省东部煤电化基地为例[J].地理科学,2014,34(11):1337-1343.[Chen Xiaohong,Wu Guangbin, Wan Luhe. Dynamic simulation of vulnerability and coordination of the coupling of urban and ecological environment based on BP:A case of the coal-electricity base in the Eastern Heilongjiang Province. Scientia Geographica Sinica,2014, 34(11):1337-1343.]
[21]雷可为,陈瑛.基于BP神经网络和ARIMA组合模型的中国入境游客量预测[J].旅游学刊,2007,22(4):20-25.[Lei Kewei,Chen Ying. Forecast of inbound tourists to China based on BP neural network and ARIMA combined model. Tourism Tribune,2007, 22(4):20-25.]
[22]赵成柏,毛春梅.基于ARIMA和BP神经网络组合模型的我国碳排放强度预测[J].长江流域资源与环境,2012,21(6):665-671.[Zhao Chengbai, Mao Chunmei. Forecast of intensity of carbon emission to China based on BP neural network and ARIMA combined mode. Resources and Environment in the Yangtze Basin, 2012, 21(6):665-671.]