数据驱动下的环境变迁与区域经济成长关联分析
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摘要
由于经济发展的复杂性,本文旨在探索由环境变迁引发这一动态、复杂而又相互作用的过程,通过引入环境变迁与经济成长两方面因素分析其中的潜在关联性,并将区域稳定性作为环境变迁与经济成长相互作用后的衡量指标,来评估该过程.1)通过使用衡量国家经济健康程度的健康性与复杂性(Fitness and Complexity)算法,获得了新的评估国家经济成长的国家经济健康性系数,该系数能在竞争激烈的动态国际贸易环境下有更好预测GDP的表现.随后建立机器学习模型,成功预测了不同国家的稳定性类别,且预测精度都在90%左右.2)实现了基于数据的环境变迁和区域经济成长的关联性可视化分析,通过分析能够得到潜在关联性结论:一些发展中国家经济稳定性与水资源和二氧化碳排放呈强关联,而发达国家则与人均耕地面积有关联.3)设立评估国家稳定性的新指标,与世界主流指标相比,构建的新指标更注重原始数据的量化,减少了概念抽象的指标对预测性能的影响,且在评估区域经济成长时能更符合当前国际的实际经济情况.本文提出的评估区域稳定性的新排名是完全基于量化指标的,因此更容易实现,说服力更强.通过实际的预测效果分析,该新排名在衡量区域稳定性时弥补了世界主流排名由抽象指标带来的预测失真缺陷,能够满足基本的区域稳定性预测功能,并且能够对预测结果造成影响的主要因素进行解释.
Regional economic development not only impacts regional politicization and economic construction but also improves national comprehensive competitiveness.How to predict the relationship between regional stability and economic development is an important problem.Accurately and quantitatively explaining development trends by using historical regional economic development data to analyze future development of the region can be difficult owing to the complexity of economic development.The goal of this work is to explore the dynamic,complex,and interactive process of the conflict caused by environmental change and analyze the potential correlation between environmental change and economic growth by regarding regional stability as a measure of the relationship between environmental change and economic growth.The main aspects of this work are as follows:1)Using the Fitness and Complexity algorithm achieves better performance in predicting national GDP growth.By applying machine learning models,we can predict stability categories of different countries with a prediction accuracy of 90%.2)We perform a correlation visualization analysis of data-based environmental change and regional economic growth.We find that some developing countries have strong economic stability associated with water resources and carbon dioxide emissions,whereas the economic stability of developed countries is associated with per capita arable land.3)We propose new indicators.Compared with the current mainstream indicators,the new indicators are more focused on quantification of raw data,reducing the impact of conceptual abstraction indicators on forecast performance,which makes them more responsive to assessing a country's stabilization.Through actual forecasting effect analysis,the new ranking compensates for the prediction distortion defects caused by the abstract indicators in the world mainstream ranking when measuring regional stability and can satisfy the basic regional stability prediction function.Moreover,it may help us to better understand the prediction results with factor explanation.
Regional economic development not only impacts regional politicization and economic construction but also improves national comprehensive competitiveness.How to predict the relationship between regional stability and economic development is an important problem.Accurately and quantitatively explaining development trends by using historical regional economic development data to analyze future development of the region can be difficult owing to the complexity of economic development.The goal of this work is to explore the dynamic,complex,and interactive process of the conflict caused by environmental change and analyze the potential correlation between environmental change and economic growth by regarding regional stability as a measure of the relationship between environmental change and economic growth.The main aspects of this work are as follows:1)Using the Fitness and Complexity algorithm achieves better performance in predicting national GDP growth.By applying machine learning models,we can predict stability categories of different countries with a prediction accuracy of 90%.2)We perform a correlation visualization analysis of data-based environmental change and regional economic growth.We find that some developing countries have strong economic stability associated with water resources and carbon dioxide emissions,whereas the economic stability of developed countries is associated with per capita arable land.3)We propose new indicators.Compared with the current mainstream indicators,the new indicators are more focused on quantification of raw data,reducing the impact of conceptual abstraction indicators on forecast performance,which makes them more responsive to assessing a country's stabilization.Through actual forecasting effect analysis,the new ranking compensates for the prediction distortion defects caused by the abstract indicators in the world mainstream ranking when measuring regional stability and can satisfy the basic regional stability prediction function.Moreover,it may help us to better understand the prediction results with factor explanation.
引文
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[2] Busby J W.Climate change and national security:an agenda for action[M].Washington D.C.:Georgetown University Press,2007:93-95
[3] Vorosmarty C J.Global water resources:vulnerability from climate change and population growth[J].Science,2000,289(5477):284-288
[4] G?rg C,Brand U,Haberl H,et al.Challenges for social-ecological transformations:contributions from social and political ecology[J].Sustainability,2017,9(7):1045
[5] Portes A,Sensenbrenner J.Embeddedness and immigration:notes on the social determinants of economic action[J].American Journal of Sociology,1993,98(6):1320-1350
[6] Hechter M,Wallerstein I.The modern world-system:capitalist agriculture and the origins of the European world-economy in the sixteenth century[J].Contemporary Sociology,1975,4(3):217
[7] Sassen S.Cities in a world economy[M].London:Sage Publications,2018
[8] 王玮,陈恩红.关联规则的相关性研究[J].计算机工程,2000,26(7):6-8WANG Wei,CHEN Enhong.Research on correlation of association rules[J].Computer Engineering,2000,26(7):6-8
[9] Harmeling S.Global climate risk index 2008[R].Berlin and Bonn:Germanwatch,2007
[10] Harmeling S.Global climate risk index 2009[R].Berlin and Bonn:Germanwatch,2008
[11] Harmeling S.Global climate risk index 2010[R].Berlin and Bonn:Germanwatch,2009
[12] Harmeling S.Global climate risk index 2011[R].Berlin and Bonn:Germanwatch,2010
[13] Harmeling S.Global climate risk index 2012[R].Berlin and Bonn:Germanwatch,2011
[14] Harmeling S,Eckstein D.Global climate risk index 2013[R].Berlin and Bonn:Germanwatch,2012
[15] Kreft S,Eckstein D,Junghans L,et al.Global climate risk index 2014[R].Berlin and Bonn:Germanwatch,2013
[16] Tacchella A,Cristelli M,Caldarelli G,et al.A new metrics for countries' fitness and products' complexity[J].Scientific Reports,2012,2:723
[17] Cristelli M,Gabrielli A,Tacchella A,et al.Measuring the intangibles:a metrics for the economic complexity of countries and products[J].PLoS One,2013,8(8):e70726
[18] Liao H,Huang X M,Vidmer A,et al.Economic complexity based recommendation enhance the efficiency of the belt and road initiative[J].Entropy,2018,20(9):718
[19] Tacchella A,Mazzilli D,Pietronero L.A dynamical systems approach to gross domestic product forecasting[J].Nature Physics,2018,14(8):861-865
[20] Cristelli M,Tacchella A,Cader M,et al.On the predictability of growth[M].The World Bank,2017.DOI:10.1596/1813-9450-8117