Comprehensive assessment and intervention strategies of urban light pollution in China based on AHP and entropy weight method

Yiying Chen

doi:10.62051/5abva252

Authors

Yiying Chen

DOI:

https://doi.org/10.62051/5abva252

Keywords:

Light Pollution; Evaluation System; Entropy Weight Method; Analytic Hierarchy Process.

Abstract

Rapid economic growth and urbanization have exacerbated light pollution, threatening ecosystems and human health. This study develops a robust evaluation system using the entropy weight method and analytic hierarchy process (AHP), ensuring objective index weighting and reliable results. By incorporating precise data such as the night light index (DN value), it provides a more accurate assessment of light pollution. The research identifies a decreasing trend in light pollution from major to smaller cities, highlighting the correlation with urban development levels. Based on these findings, the study recommends targeted strategies: promoting green lighting, optimizing urban planning, and enhancing light pollution prevention facilities. These measures aim to mitigate light pollution's impact, protecting human health and ecological balance. Effectively managing light pollution requires collaborative efforts from government, businesses, communities, and the public to create a healthier nocturnal environment.

Downloads

Download data is not yet available.

References

[1] Yi-qiong L, Jun-wu B, Da-jun L, et al. Evaluation of urban green development of typical Chinese cities [J]. Ecological Economy, 2016, 12 (04): 320 - 335.

[2] Hao Q, Wang L, Liu G, et al. Exploring the construction of urban artificial light ecology: a systematic review and the future prospects of light pollution [J]. Environmental Science and Pollution Research, 2023, 30 (46): 101963 - 101988.

[3] Hao Y, Wang P, Zhang Z, et al. A Review of the Characteristics of Light Pollution: Assessment Technique, Policy, and Legislation [J]. Energies, 2024, 17 (11): 2750.

[4] Zhao F, Chu C, Liu R, et al. Assessing Light Pollution Using POI and Luojia1-01 Night-Time Imagery from a Quantitative Perspective at City Scale [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 7544 – 7556.

[5] Deng L, Yang Z, Shi J, et al. Multilevel evaluation model of light pollution (MELP) to evaluate specific areas used in risk exposure assessment: a study case in Chengdu City (Southwest China) [J]. Human and Ecological Risk Assessment: An International Journal, 2024: 1 - 22.

[6] Falchi F, Bará S. Computing light pollution indicators for environmental assessment [J]. Natural Sciences, 2021, 1 (2): e10019.

[7] Elsahragty M, Kim J L. Assessment and strategies to reduce light pollution using geographic information systems[J]. Procedia engineering, 2015, 118: 479 - 488.

[8] Sainger A, Yadav R, Tipare P, et al. Analysis of light pollution prediction using mathematical model and machine learning techniques[C]//Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Springer Singapore, 2020: 31 - 43.

[9] Kocifaj M, Kómar L, Lamphar H, et al. A systematic light pollution modelling bias in present night sky brightness predictions[J]. Nature Astronomy, 2023, 7 (3): 269 - 279.

[10] Sung C Y, Kim Y J. Analysis of the Status of Light Pollution and its Potential Effect on Ecosystem of the Deogyusan National Park[J]. Korean Journal of Environment and Ecology, 2020, 34 (1): 63 - 71.

[11] Mncube Z, Adelabu S, Adagbasa E. Is eThekwini metropolitan municipality (EMM) experiencing light pollution? A remote sensing analysis of nighttime data of EMM, South Africa [J]. Spatial Information Research, 2023, 31 (2): 211 - 223.