Forests provide enormous ecological, economic, and social benefits, as such forest development should be ori‐ented toward resource-economy-environment harmonization. This paper constructs a comprehensive evalua‐tion...Forests provide enormous ecological, economic, and social benefits, as such forest development should be ori‐ented toward resource-economy-environment harmonization. This paper constructs a comprehensive evalua‐tion index system and uses the coupled coordination degree model to measure the coordinated development level of China’ s forest resources-economy-environment system. The results show that, across 2006‒2020, the level of coupled coordinated development of China’ s forest resources-economy-environment composite system fluctuates in an upward trend, thus gradually developing from an initial imbalance to a high degree of coordi‐nation;the level of coordinated development of each subsystem of the forest resources, economy, and environ‐ment also shows an upward trend. The factors influencing the coordinated development of the forest resource economy-environment system are, in order, the government’ s financial capacity, market environment, scien‐tific and technological innovation capacity, level of economic development, and strength of policy implemen‐tation. Therefore, this paper proposes some measures to improve the coordinated development.展开更多
Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an e...Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.展开更多
将水资源承载力系统划分为水资源、社会、经济、生态环境4个子系统,构建包含24个评价指标的水资源承载力评价指标体系,建立基于熵权的逼近理想解排序(technique for order preference by similarity to an ideal solution,TOPSIS)模型...将水资源承载力系统划分为水资源、社会、经济、生态环境4个子系统,构建包含24个评价指标的水资源承载力评价指标体系,建立基于熵权的逼近理想解排序(technique for order preference by similarity to an ideal solution,TOPSIS)模型、耦合协调度模型、色关联度模型来分析2011―2020年长江经济带11省(市)水资源承载力时空变化过程、子系统间的协调发展水平以及影响水资源承载力的关键因素。结果表明:1)长江经济带11省(市)水资源承载力的表现:下降(2011―2013年)、小幅波动(2014―2017年)、大幅上升(2018―2020年);2)水资源承载力空间差异不大,均值水平呈下游高于上游、上游高于中游的特点;3)各子系统间的协调发展水平的表现:上升(2011―2013年)、明显下降(2014―2017年)、缓慢回升(2018―2020年);4)对水资源承载力影响程度高的因素:产水模数、城市人均日生活用水量、万元国内生产总值(gross domestic product,GDP)用水量、废水治理投资占GDP比重。长江经济带11省(市)水资源承载力水平受地区社会经济发展水平、水资源与生态环境现状影响较大,应因地制宜,合理利用、有效保护水资源。展开更多
基金funded by the National Social Science Foundation of China[Grant No.20BJY075].
文摘Forests provide enormous ecological, economic, and social benefits, as such forest development should be ori‐ented toward resource-economy-environment harmonization. This paper constructs a comprehensive evalua‐tion index system and uses the coupled coordination degree model to measure the coordinated development level of China’ s forest resources-economy-environment system. The results show that, across 2006‒2020, the level of coupled coordinated development of China’ s forest resources-economy-environment composite system fluctuates in an upward trend, thus gradually developing from an initial imbalance to a high degree of coordi‐nation;the level of coordinated development of each subsystem of the forest resources, economy, and environ‐ment also shows an upward trend. The factors influencing the coordinated development of the forest resource economy-environment system are, in order, the government’ s financial capacity, market environment, scien‐tific and technological innovation capacity, level of economic development, and strength of policy implemen‐tation. Therefore, this paper proposes some measures to improve the coordinated development.
文摘Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.
文摘将水资源承载力系统划分为水资源、社会、经济、生态环境4个子系统,构建包含24个评价指标的水资源承载力评价指标体系,建立基于熵权的逼近理想解排序(technique for order preference by similarity to an ideal solution,TOPSIS)模型、耦合协调度模型、色关联度模型来分析2011―2020年长江经济带11省(市)水资源承载力时空变化过程、子系统间的协调发展水平以及影响水资源承载力的关键因素。结果表明:1)长江经济带11省(市)水资源承载力的表现:下降(2011―2013年)、小幅波动(2014―2017年)、大幅上升(2018―2020年);2)水资源承载力空间差异不大,均值水平呈下游高于上游、上游高于中游的特点;3)各子系统间的协调发展水平的表现:上升(2011―2013年)、明显下降(2014―2017年)、缓慢回升(2018―2020年);4)对水资源承载力影响程度高的因素:产水模数、城市人均日生活用水量、万元国内生产总值(gross domestic product,GDP)用水量、废水治理投资占GDP比重。长江经济带11省(市)水资源承载力水平受地区社会经济发展水平、水资源与生态环境现状影响较大,应因地制宜,合理利用、有效保护水资源。
