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Examining the Impact of China’s Poverty Alleviation on Nighttime Lighting in 831 State-Level Impoverished Counties

Author

Listed:
  • Yiguo Shen

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Xiaojie Chen

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Qingxin Yao

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Jiahui Ding

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Yuhan Lai

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Yongheng Rao

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

Abstract

China’s poverty alleviation projects have made significant contributions to global poverty eradication. This study investigates the impact of China’s poverty alleviation projects on nighttime lighting in 831 state-level impoverished counties using the “NPP-VIIRS-like” dataset and discusses the difference of land use change under different nighttime light clusters in order to provide reference for future policy formulation and implementation. Our results show that the growth of total intensity of nighttime lighting (GRTNL) and the year-on-year growth rate of total intensity of nighttime lighting (YGRTNL) in China’s impoverished counties are 103.74% and 9.69% from 2013 to 2021, respectively, which are both higher than the average levels of all counties (67.16%, 6.77%) and non-poor counties (64.68%, 6.56%) in China during the same period. Additionally, we discovered that impoverished counties that lifted out of poverty earlier had significantly higher nighttime lighting intensity than those later. Regional analysis reveals that the growth of nighttime lighting intensity shows a trend of decreasing from the central (1550.89 nW·cm −2 ·sr −1 ) to the eastern (924.57), western (762.57), and northeastern regions (588.07), while the growth rate decreases from western regions (282.46%) to the eastern (189.13%), central (178.56%), and northeastern (108.07%). We also identified that Gini coefficient of nighttime lighting has a trend of “slow and short-term rise-rapid and continuous decline”. Moreover, nighttime lighting growth had similar trends with land use change, especially construction land. Overall, our study provides novel insights into the relationship between poverty alleviation effects and nighttime lighting in China’s impoverished counties, which could inform future policy-making and research in this area.

Suggested Citation

  • Yiguo Shen & Xiaojie Chen & Qingxin Yao & Jiahui Ding & Yuhan Lai & Yongheng Rao, 2023. "Examining the Impact of China’s Poverty Alleviation on Nighttime Lighting in 831 State-Level Impoverished Counties," Land, MDPI, vol. 12(6), pages 1-17, May.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1128-:d:1155501
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    References listed on IDEAS

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    1. Shu, Cheng & Xie, Hualin & Jiang, Jinfa & Chen, Qianru, 2018. "Is Urban Land Development Driven by Economic Development or Fiscal Revenue Stimuli in China?," Land Use Policy, Elsevier, vol. 77(C), pages 107-115.
    2. Bowles, Samuel & Carlin, Wendy, 2020. "Inequality as experienced difference: A reformulation of the Gini coefficient," Economics Letters, Elsevier, vol. 186(C).
    3. Dang, Anh Nguyet & Kawasaki, Akiyuki, 2017. "Integrating biophysical and socio-economic factors for land-use and land-cover change projection in agricultural economic regions," Ecological Modelling, Elsevier, vol. 344(C), pages 29-37.
    4. Jochem Marotzke & Dirk Semmann & Manfred Milinski, 2020. "The economic interaction between climate change mitigation, climate migration and poverty," Nature Climate Change, Nature, vol. 10(6), pages 518-525, June.
    5. Xiaocang Xu & Haoran Yang, 2022. "Elderly chronic diseases and catastrophic health expenditure: an important cause of Borderline Poor Families’ return to poverty in rural China," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    6. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    7. Gastwirth, Joseph L, 1972. "The Estimation of the Lorenz Curve and Gini Index," The Review of Economics and Statistics, MIT Press, vol. 54(3), pages 306-316, August.
    8. Thomas Bossuroy & Markus Goldstein & Bassirou Karimou & Dean Karlan & Harounan Kazianga & William Parienté & Patrick Premand & Catherine C. Thomas & Christopher Udry & Julia Vaillant & Kelsey A. Wrigh, 2022. "Tackling psychosocial and capital constraints to alleviate poverty," Nature, Nature, vol. 605(7909), pages 291-297, May.
    9. Ji-Won Park & Chae Un Kim, 2020. "Getting to a feasible income equality," Papers 2011.09119, arXiv.org, revised Mar 2021.
    10. Yue Sun & Yanhui Wang & Chong Huang & Renhua Tan & Junhao Cai, 2023. "Measuring farmers’ sustainable livelihood resilience in the context of poverty alleviation: a case study from Fugong County, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
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