Houzhe Zhang, Defeng Gu, Xiaojun Duan, Kai Shao and Chunbo Wei
The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.
Abstract
Purpose
The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.
Design/methodology/approach
The error of Jacchia-Roberts atmospheric density model is expressed as an objective function about temperature parameters. The estimation of parameter corrections is a typical nonlinear least-squares problem. Three algorithms for nonlinear least-squares problems, Gauss–Newton (G-N), damped Gauss–Newton (damped G-N) and Levenberg–Marquardt (L-M) algorithms, are adopted to estimate temperature parameter corrections of Jacchia-Roberts for model calibration.
Findings
The results show that G-N algorithm is not convergent at some sampling points. The main reason is the nonlinear relationship between Jacchia-Roberts and its temperature parameters. Damped G-N and L-M algorithms are both convergent at all sampling points. G-N, damped G-N and L-M algorithms reduce the root mean square error of Jacchia-Roberts from 20.4% to 9.3%, 9.4% and 9.4%, respectively. The average iterations of G-N, damped G-N and L-M algorithms are 3.0, 2.8 and 2.9, respectively.
Practical implications
This study is expected to provide a guidance for the selection of nonlinear least-squares estimation methods in atmospheric density model calibration.
Originality/value
The study analyses the performance of three typical nonlinear least-squares estimation methods in the calibration of atmospheric density model. The non-convergent phenomenon of G-N algorithm is discovered and explained. Damped G-N and L-M algorithms are more suitable for the nonlinear least-squares problems in model calibration than G-N algorithm and the first two algorithms have slightly fewer iterations.
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Mitigating agricultural greenhouse gas (GHG) emissions is an essential part of China's effort to achieve net-zero emissions. This study assesses the cost-effectiveness of China's…
Abstract
Purpose
Mitigating agricultural greenhouse gas (GHG) emissions is an essential part of China's effort to achieve net-zero emissions. This study assesses the cost-effectiveness of China's agricultural GHG reduction under diverse carbon policies.
Design/methodology/approach
The study employs a parametric non-radial distance function approach and estimates the technical abatement potential and marginal abatement cost (MAC) of GHG in China's agricultural sector for the 2008–2017 period.
Findings
Agriculture is expected to make a great contribution to China's net-zero emissions progress. This study empirically analyses the cost-effectiveness of China's agricultural GHG reduction under diverse carbon policies. A parametric non-radial distance function approach is used to derive technical abatement potential and MAC of GHG for the 2008–2017 period. The results indicate that no significant improvement had been achieved in terms of agricultural GHG reduction in China during 2008–2017. The country's agricultural sector could reduce 20–40% GHG emissions with a mean value of 31%. In general, western provinces have larger reduction potential than eastern ones. The average MAC for the whole country is 4,656 yuan/ton CO2e during 2008–2017. For most western provinces, their MAC values are considerably higher than those for most eastern provinces. Compared with previous sectoral estimates of GHG mitigation cost, this study’s estimates indicate that reducing agricultural GHG emissions in some provinces is likely to be cost-effective. The Chinese government should consider expanding its national carbon market to cover agricultural sector.
Practical implications
The Chinese government should consider expanding its national carbon market to cover agricultural sector.
Originality/value
Existing studies in the field mostly ignore input constraints, which is inconsistent with carbon mitigation policy practice, especially in the agricultural sector. This study’s approach integrates both input and output constraints reflecting differing policy practice.
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Kaixin Wangzhou, Chunbo Hao and Huamin Wang
With the development of small towns in China, the country pays more and more attention to the protection of landscape resources. It is an urgent problem that is how to protect…
Abstract
Purpose
With the development of small towns in China, the country pays more and more attention to the protection of landscape resources. It is an urgent problem that is how to protect landscape resources and ecological environment while developing economic industry in small towns. Establishing an ecotourism evaluation model can provide valuable reference for ecotourism planning, development protection and sustainable development of small towns.
Design/methodology/approach
This paper uses the analytic hierarchy process (AHP) to construct the ecotourism evaluation system that accords with the characteristics of small towns. A judgment matrix is constructed to determine specific indicators and factor values based on expert survey results. Based on the AHP theory and considering 4 aspects, construction conditions of featured small towns, ecological and environmental conditions, ecotourism resources endowment and development conditions and tourism capacity. In addition, 16 factor evaluations were selected, evaluation model of ecotourism resources were built and each evaluation index value was confirmed by adopting expert's advice.
Findings
Ecological environment, socioeconomic, uniqueness, esthetic ornamental value, small-scale industry scale, type and development level, type and scale, tourism talent level, therapeutic and leisure value were the indispensable components of evaluation of ecotourism resources in featured small towns.
Originality/value
The ideas of ecological environment development are rooted in the hearts of the people with the development of times. The model in this research is pertinent, typical and universal to some extent. Thus it is worth popularizing and applying.
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Abstract
Purpose
The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.
Design/methodology/approach
This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.
Findings
The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.
Originality/value
This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.