Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (6): 899-922.doi: 10.1007/s11442-021-1877-1

• Research Articles • Previous Articles    

Neighborhood impacts on household participation in payments for ecosystem services programs in a Chinese nature reserve: A methodological exploration

ZHANG Huijie1,2,3(), AN Li1,3, BILSBORROW Richard4, CHUN Yongwan5, YANG Shuang1,2, DAI Jie1,2,3   

  1. 1. Department of Geography, San Diego State University, San Diego, CA, USA
    2. Department of Geography, University of California, Santa Barbara, CA, USA
    3. Center for Complex Human-Environment Systems, San Diego State University, San Diego, CA, USA
    4. Departments of Biostatistics and Geography and Carolina Population Center, University of North Carolina at Chapel Hill, USA
    5. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA
  • Received:2020-08-13 Accepted:2021-02-09 Published:2021-08-25
  • About author:Zhang Huijie, PhD Student, specialized in remote sensing image processing and analysis, and spatial data analysis. E-mail:
  • Supported by:
    National Science Foundation under the Dynamics of Coupled Natural and Human Systems Program(DEB-1212183);National Science Foundation under the Dynamics of Coupled Natural and Human Systems Program(BCS-1826839);PES program duration for three different scenarios;Population Research Infrastructure Program(P2C);Population Research Infrastructure Program(HD050924)


Payments for Ecosystem Services (PES) programs have been implemented in both developing and developed countries to conserve ecosystems and the vital services they provide. These programs also often seek to maintain or improve the economic wellbeing of the populations living in the corresponding (usually rural) areas. Previous studies suggest that PES policy design, presence or absence of concurrent PES programs, and a variety of socioeconomic and demographic factors can influence decisions of households to participate or not in the PES program. However, neighborhood impacts on household participation in PES have rarely been addressed. This study explores potential neighborhood effects on villagers’ enrollment in the Grain-to-Green Program (GTGP), one of the largest PES programs in the world, using data from China’s Fanjingshan National Nature Reserve. We utilize a fixed effects logistic regression model in combination with the eigenvector spatial filtering (ESF) method to explore whether neighborhood size affects household enrollment in GTGP. By comparing the results with and without ESF, we find that the ESF method can help account for spatial autocorrelation properly and reveal neighborhood impacts that are otherwise hidden, including the effects of area of forest enrolled in a concurrent PES program, gender and household size. The method can thus uncover mechanisms previously undetected due to not taking into account neighborhood impacts and thus provides an additional way to account for neighborhood impacts in PES programs and other studies.

Key words: neighborhood impacts, payments for ecosystem services, Grain-to-Green Program, eigenvector spatial filtering, fixed effects logistic regression model, China