Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China co...Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.展开更多
The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more ...The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more than 90%of world freight movement and emits 3%of global carbon dioxide(CO_(2))emissions.China is the world’s largest emitter of CO_(2 )and plays a key role in mitigating global climate change.In order to tackle this pressing concern,this study analyses the port’s throughput,the current number of trucks and their emissions during the container truck purchasing process.Previous studies about container truck purchasing plans mostly focused on the trucks’price and port needs.The objective of this study is to minimize the total cost of a port’s inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs.The study considers the port of Yangtze as a case study.The study has designed two scenarios.(i)The base scenario(business-asusual,BAU)is used to quantify the relationship between pollutant emissions and system cost.In the base scenario,no environmental control facilities are used during the planning period,and there is no need to purchase new energy container trucks.(ii)The expected scenario(Scenario A)is for three planning periods.In Scenario A,the emissions levels are required to remain at the same level as the first planning period during the whole planning period.By solving the above model,the number of all truck types,system cost,container throughput and truck emissions in the port area were analysed.The results showed that if no emission reduction control measures are implemented in the next 9 years,the growth rate of pollutants in the port area could reach 20%.In addition,the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel(diesel)trucks.This study could also help to minimize system costs associated with port planning and management.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41201164)Humanities and Social Science Research Planning Fund,Ministry of Education of China(No.12YJCZH299)
文摘Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.
基金the National Natural Science Foundation of China(Grant No.51678461).
文摘The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more than 90%of world freight movement and emits 3%of global carbon dioxide(CO_(2))emissions.China is the world’s largest emitter of CO_(2 )and plays a key role in mitigating global climate change.In order to tackle this pressing concern,this study analyses the port’s throughput,the current number of trucks and their emissions during the container truck purchasing process.Previous studies about container truck purchasing plans mostly focused on the trucks’price and port needs.The objective of this study is to minimize the total cost of a port’s inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs.The study considers the port of Yangtze as a case study.The study has designed two scenarios.(i)The base scenario(business-asusual,BAU)is used to quantify the relationship between pollutant emissions and system cost.In the base scenario,no environmental control facilities are used during the planning period,and there is no need to purchase new energy container trucks.(ii)The expected scenario(Scenario A)is for three planning periods.In Scenario A,the emissions levels are required to remain at the same level as the first planning period during the whole planning period.By solving the above model,the number of all truck types,system cost,container throughput and truck emissions in the port area were analysed.The results showed that if no emission reduction control measures are implemented in the next 9 years,the growth rate of pollutants in the port area could reach 20%.In addition,the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel(diesel)trucks.This study could also help to minimize system costs associated with port planning and management.
