Journal of Geographical Sciences >
Neighborhood impacts on household participation in payments for ecosystem services programs in a Chinese nature reserve: A methodological exploration
Zhang Huijie, PhD Student, specialized in remote sensing image processing and analysis, and spatial data analysis. Email: wszhj2008@gmail.com 
Received date: 20200813
Accepted date: 20210209
Online published: 20210825
Supported by
National Science Foundation under the Dynamics of Coupled Natural and Human Systems Program(DEB1212183)
National Science Foundation under the Dynamics of Coupled Natural and Human Systems Program(BCS1826839)
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 GraintoGreen 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.
ZHANG Huijie , AN Li , BILSBORROW Richard , CHUN Yongwan , YANG Shuang , DAI Jie . Neighborhood impacts on household participation in payments for ecosystem services programs in a Chinese nature reserve: A methodological exploration[J]. Journal of Geographical Sciences, 2021 , 31(6) : 899 922 . DOI: 10.1007/s1144202118771
Figure 1 Fanjingshan National Reserve and sample households in the study site. The core zone designation was based on both conservation goals and local people’s livelihood needs, resulting in a small number of households located within the core zone. At the same time, some households were included in the survey and subsequent data analysis even though they are just outside the reserve’s boundary because they affect the reserve through various activities such as fuelwood collection and collection of medicinal herbs. 
Table 1 Variable names, descriptions and summary statistics 
Category  Variable  Description  Type  Mean  Standard deviation  Min  Max 

PES policy dimension  GTGP payment  PES payment levels for three scenarios (1000 yuan/mu/year)  Discrete; 0.1, 0.2, 0.3 for three scenarios  0.197  0.081  0.1  0.3 
GTGP duration  PES program duration for three different scenarios  Discrete; 4, 8, 12 years  7.70  3.16  4  12  
Economic trees  Allowed to plant only economic trees after enrolling in program  Dichotomous; yes = 1; no = 0  0.40  0.49  0  1  
Ecological plants  Allowed to plant only ecological trees after enrolling in program  Dichotomous; yes = 1; no = 0  0.313  0.464  0  1  
Neighbors participating  Hypothetical percentage of neighborhood members participating in GTGP, three different scenarios  Discrete; 25%, 50%, 75%  0.515  0.185  0.25  0.75  
Concurrent PES variable  FEBC land area  Area of forest land of household (mu)  Continuous; logarithm of amount of land enrolled in FEBC  2.37  1.57  1.2  8.52 
Socioeconomic and demographic variables  Age  Age of respondent at the time of interview  Continuous, years  53.9  12.1  21  86 
Gender  Gender of respondent  Dichotomous; male = 1; female = 2  1.14  0.35  1  2  
Education  Education of respondent  Continuous, years completed  4.95  3.47  0  13  
Annual agricultural expenses, past 12 months  Agricultural expenses (1000 yuan/year)  Continuous  0.899  0.812  0.02  5.34  
Local offfarm income, past 12 months  Local offfarm Income (sum of remittances and local work/business income) (1000 yuan)  Continuous  4.85  10.2  0  50  
Household size  Number of household members  Continuous  3.06  1.40  1  8  
NonGTGP land  Area of nonGTGP land of household (mu)  Continuous  3.88  3.53  0  17 
Table 2 Results of nonspatial MELRM, FELRM without dummy variables, and FELRM with dummy variables: dependent variable, probability of enrolling in GTGP 
Model 1  Model 2  Model 3  

Nonspatial MELRM  Nonspatial FELRM without dummy variables  Nonspatial FELRM with dummy variables  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  1.544  0.089  2.299  0.038  
GTGP payment  4.878  <0.001  1.035  4.878  <0.001  1.035  5.520  <0.001  1.075 
GTGP duration  0.008  0.816  1.048  0.008  0.816  1.048  0.022  0.555  1.099 
Economic trees  0.638  0.015  1.430  0.638  0.015  1.430  0.610  0.027  1.482 
Ecological plants  0.100  0.713  1.415  0.100  0.713  1.415  0.140  0.630  1.486 
Neighbors participating  1.626  0.006  1.035  1.626  0.006  1.035  1.602  0.011  1.087 
FEBC land area  0.159  0.033  1.199  0.159  0.033  1.199  0.082  0.354  1.453 
Age  0.025  0.009  1.129  0.025  0.009  1.129  0.030  0.006  1.356 
Gender  0.668  0.045  1.179  0.668  0.045  1.179  0.801  0.031  1.374 
Education  0.109  0.001  1.210  0.109  0.001  1.210  0.137  0.000  1.441 
Annual agricultural expenses  0.556  <0.001  1.196  0.556  <0.001  1.196  0.333  0.043  1.438 
Local offfarm income  0.037  0.002  1.259  0.037  0.002  1.259  0.040  0.003  1.369 
Household size  0.204  0.019  1.282  0.204  0.019  1.282  0.216  0.029  1.561 
NonGTGP land  0.086  0.011  1.225  0.086  0.011  1.225  0.115  0.005  1.650 
Dummy1  1.916  0.028  1.447  
Dummy2  0.805  0.286  1.470  
Dummy3  0.506  0.544  1.379  
Dummy4  0.708  0.273  1.709  
Dummy5  0.394  0.581  1.567  
Dummy6  0.335  0.553  2.614  
Dummy7  0.353  0.584  1.704  
Dummy8  0.496  0.437  1.625  
Dummy9  1.361  0.086  1.599  
Dummy10  2.147  0.070  1.156  
Dummy11  0.143  0.824  1.643  
Dummy12  0.793  0.241  1.780  
Dummy13  0.091  0.891  1.566  
Dummy14  0.487  0.686  1.162  
Dummy15  15.507  0.985  1.000  
Dummy16  1.234  0.113  1.308  
Dummy17  0.038  0.959  1.457  
Dummy18  0.252  0.673  1.688  
Dummy19  0.406  0.525  1.836  
Dummy20  0.464  0.631  1.404  
Dummy21  0.197  0.762  1.629  
Dummy22  0.162  0.775  1.865  
Dummy23  NA  NA  NA  
Variance of random effect village group  0.000  NA  NA  
AIC  545.20  543.18  554.91 
Number of observations: 435. Bold numbers are statistically significant at the 5% level. 
Table 3 Results of Moran’s test for deviance residuals of nonspatial FELRM with respect to different settings of neighborhood size 
Model (Neighborhood)  Contiguity matrix  Moran’s I  Expected I  Variance  zscore  pvalue 

Model 2 (0.02 km)  Neighbors within 0.02 km  0.502  0.003  0.040  2.485  0.013 
Model 2 (0.04 km)  Neighbors within 0.04 km  0.059  0.005  0.005  0.913  0.361 
Model 2 (0.06 km)  Neighbors within 0.06 km  0.022  0.004  0.002  0.543  0.587 
Model 2 (0.08 km)  Neighbors within 0.08 km  0.076  0.005  0.002  1.953  0.051 
Model 2 (0.1 km)  Neighbors within 0.1 km  0.134  0.004  0.001  3.790  0.000 
Model 2 (0.5 km)  Neighbors within 0.5 km  0.093  0.003  0.000  4.378  0.000 
Model 2 (1.0 km)  Neighbors within 1 km  0.016  0.003  0.000  0.725  0.468 
Model 2 (2.0 km)  Neighbors within 2 km  0.026  0.003  0.000  1.829  0.067 
Model 2 (3.0 km)  Neighbors within 3 km  0.006  0.003  0.000  0.319  0.750 
Model 2 (4.0 km)  Neighbors within 4 km  0.006  0.003  0.000  0.422  0.673 
Model 2 (5.0 km)  Neighbors within 5 km  0.014  0.003  0.000  2.441  0.015 
Model 2 (6.0 km)  Neighbors within 6 km  0.013  0.003  0.000  2.639  0.008 
Bold indicates Moran’s I statistically significant at 5% level. 
Table 4 Results of nonspatial and spatial (ESF) models for probability of enrolling in GTGP 
Number of observations: 435. Bold indicates change of significance level from significant at 5% level to not significant at 5% level. 
Table 5 Results of Moran’s test for deviance residuals in the models ofTable 4 
Model (Neighborhood)  Contiguity matrix  Moran's I  Expected I  Variance  zscore  pvalue 

Model 2 (0.02 km NB)  Neighbors within 0.02 km  0.502  0.003  0.040  2.485  0.013 
Model 4 (0.02 km NB)  Neighbors within 0.02 km  0.118  0.206  0.035  0.471  0.637 
Model 2 (0.1 km NB)  Neighbors within 0.1 km  0.134  0.004  0.001  3.790  0.000 
Model 5 (0.1 km NB)  Neighbors within 0.1 km  0.099  0.023  0.001  3.545  0.000 
Model 6 (0.1 km NB)  Neighbors within 0.1 km  0.026  0.025  0.001  0.019  0.985 
Model 2 (0.5 km NB)  Neighbors within 0.5 km  0.093  0.003  0.000  4.378  0.000 
Model 7 (0.5 km NB)  Neighbors within 0.5 km  0.077  0.032  0.000  2.542  0.011 
Model 2 (5 km NB)  Neighbors within 5 km  0.014  0.003  0.000  2.441  0.015 
Model 8 (5 km NB)  Neighbors within 5 km  0.003  0.007  0.000  0.705  0.481 
Model 2 (6 km NB)  Neighbors within 6 km  0.013  0.003  0.000  2.639  0.008 
Model 9 (6 km NB)  Neighbors within 6 km  0.014  0.007  0.000  1.467  0.142 
Bold numbers indicate Moran’s I significant at 5% level. 
Table 6 Results of Moran’s test for independent variables 
Variable  Neighbors within 0.02 km  Neighbors within 0.1 km  Neighbors within 0.5 km  

Moran’s I  zscore  pvalue  Moran’s I  zscore  pvalue  Moran’s I  zscore  pvalue  
GTGP payment  0.004  0.009  0.993  0.005  0.169  0.866  0.003  0.186  0.853 
GTGP duration  0.134  1.005  0.315  0.030  0.648  0.517  0.014  0.442  0.658 
Economic trees  0.003  0.040  0.968  0.025  0.631  0.528  0.005  0.278  0.781 
Ecological plants  0.095  0.684  0.494  0.078  1.883  0.060  0.011  0.484  0.628 
Neighbors participating  0.141  1.023  0.306  0.026  0.558  0.577  0.023  0.782  0.434 
FEBC land area  0.487  3.585  0.000  0.182  4.337  0.000  0.190  7.163  0.000 
Age  0.487  3.617  0.000  0.054  1.315  0.188  0.003  0.023  0.982 
Gender  0.028  0.192  0.848  0.056  1.267  0.205  0.096  3.513  0.000 
Education  0.010  0.055  0.956  0.026  0.678  0.498  0.094  3.589  0.000 
Annual agricultural expenses  0.049  0.384  0.701  0.058  1.446  0.148  0.221  8.430  0.000 
Local offfarm income  0.138  1.046  0.296  0.028  0.712  0.477  0.083  3.023  0.003 
Household size  0.329  2.454  0.014  0.097  2.353  0.019  0.137  5.172  0.000 
NonGTGP land  0.277  2.067  0.039  0.159  3.798  0.000  0.242  9.089  0.000 
Variable  Neighbors within 5 km  Neighbors within 6 km  
Moran’s I  zscore  pvalue  Moran’s I  zscore  pvalue  
GTGP payment  0.001  0.321  0.748  0.001  0.362  0.717  
GTGP duration  0.014  1.339  0.181  0.013  1.331  0.183  
Economic trees  0.008  1.117  0.264  0.003  0.603  0.547  
Ecological plants  0.009  1.285  0.199  0.005  0.934  0.350  
Neighbors participating  0.001  0.370  0.712  0.000  0.314  0.754  
FEBC land area  0.112  12.653  0.000  0.063  8.185  0.000  
Age  0.021  2.022  0.043  0.017  1.804  0.071  
Gender  0.058  6.660  0.000  0.062  7.981  0.000  
Education  0.007  1.009  0.313  0.000  0.248  0.804  
Annual agricultural expenses  0.118  13.472  0.000  0.114  14.577  0.000  
Local offfarm income  0.021  2.111  0.035  0.016  1.681  0.093  
Household size  0.047  5.492  0.000  0.073  9.332  0.000  
NonGTGP land  0.118  13.311  0.000  0.138  17.419  0.000 
Bold numbers indicate Moran’s I significant at 5% level. 
Table S1 Results of nonspatial and spatial models with neighbors within 0.1 km for probability of enrolling in GTGP (with fewer than the top 17 eigenvectors) 
Model 2  Model 10  Model 11  Model 12  

Neighborhood size  Nonspatial  Neighbors within 0.1 km  Neighbors within 0.1 km  Neighbors within 0.1 km  
Number of eigenvectors  0  14  15  16  
Eigenvectors  NA  EV1EV14  EV1EV15  EV1EV16  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  1.352  0.168  1.079  0.278  1.628  0.138  
GTGP payment  4.878  <0.001  1.035  5.035  <0.001  1.046  5.217  <0.001  1.055  5.859  <0.001  1.053 
GTGP duration  0.008  0.816  1.048  0.015  0.686  1.091  0.018  0.616  1.097  0.019  0.629  1.098 
Economic trees  0.638  0.015  1.430  0.719  0.009  1.455  0.726  0.009  1.465  0.850  0.004  1.464 
Ecological plants  0.100  0.713  1.415  0.085  0.766  1.441  0.152  0.599  1.477  0.181  0.557  1.473 
Neighbors participating  1.626  0.006  1.035  1.680  0.006  1.048  1.708  0.006  1.052  2.037  0.002  1.053 
FEBC land area  0.159  0.033  1.199  0.190  0.017  1.311  0.191  0.017  1.314  0.224  0.008  1.315 
Age  0.025  0.009  1.129  0.027  0.013  1.366  0.025  0.026  1.380  0.030  0.013  1.408 
Gender  0.668  0.045  1.179  0.509  0.164  1.296  0.608  0.103  1.339  0.605  0.138  1.436 
Education  0.109  0.001  1.210  0.143  <0.001  1.335  0.158  <0.001  1.428  0.162  <0.001  1.444 
Annual agricultural expenses  0.556  <0.001  1.196  0.494  0.001  1.254  0.486  0.002  1.257  0.431  0.009  1.266 
Local offfarm income  0.037  0.002  1.259  0.044  <0.001  1.394  0.046  <0.001  1.418  0.045  0.001  1.427 
Household size  0.204  0.019  1.282  0.224  0.020  1.492  0.258  0.009  1.569  0.267  0.010  1.595 
NonGTGP land  0.086  0.011  1.225  0.087  0.018  1.325  0.091  0.013  1.331  0.099  0.011  1.332 
AIC  543.18  541.01  539.74  539.48 
Number of observations: 435. Bold indicates change of significance level from significant at 5% level to not significant at 5% level 
Table S2 Results of nonspatial and spatial models with neighbors within 0.1 km for probability of enrolling in GTGP (with the top 17 or more eigenvectors) 
Model 2  Model 13  Model 14  

Neighborhood size  Nonspatial  Neighbors within 0.1 km  Neighbors within 0.1 km  
Number of eigenvectors  0  17  18  
Eigenvectors  NA  EV1EV17  EV1EV18  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  0.806  0.428  1.684  0.126  
GTGP payment  4.878  <0.001  1.035  5.126  <0.001  1.082  5.888  <0.001  1.083  
GTGP duration  0.008  0.816  1.048  0.021  0.568  1.121  0.013  0.738  1.129  
Economic trees  0.638  0.015  1.430  0.713  0.010  1.520  0.842  0.005  1.517  
Ecological plants  0.100  0.713  1.415  0.142  0.624  1.481  0.204  0.508  1.486  
Neighbors participating  1.626  0.006  1.035  1.727  0.006  1.088  2.118  0.002  1.097  
FEBC land area  0.159  0.033  1.199  0.190  0.018  1.363  0.250  0.004  1.429  
Age  0.025  0.009  1.129  0.022  0.046  1.477  0.032  0.009  1.482  
Gender  0.668  0.045  1.179  0.752  0.054  1.423  0.628  0.123  1.432  
Education  0.109  0.001  1.210  0.165  <0.001  1.451  0.166  <0.001  1.470  
Model 2  Model 13  Model 14  
Neighborhood size  Nonspatial  Neighbors within 0.1 km  Neighbors within 0.1 km  
Number of eigenvectors  0  17  18  
Eigenvectors  NA  EV1EV17  EV1EV18  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
Annual agricultural expenses  0.556  <0.001  1.196  0.502  0.001  1.270  0.446  0.007  1.273  
Local offfarm income  0.037  0.002  1.259  0.046  <0.001  1.428  0.046  <0.001  1.434  
Household size  0.204  0.019  1.282  0.274  0.006  1.558  0.265  0.010  1.549  
NonGTGP land  0.086  0.011  1.225  0.090  0.014  1.365  0.092  0.019  1.373  
AIC  543.18  497.92  497.27  
Model 15  Model 16  
Neighborhood size  Neighbors within 0.1 km  Neighbors within 0.1 km  
Number of eigenvectors  19  20  
Eigenvectors  EV1EV19  EV1EV20  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.661  0.133  1.411  0.211  
GTGP payment  5.930  <0.001  1.085  6.059  <0.001  1.090  
GTGP duration  0.013  0.745  1.134  0.005  0.896  1.139  
Economic trees  0.865  0.004  1.520  0.843  0.005  1.511  
Ecological plants  0.238  0.444  1.490  0.213  0.498  1.463  
Neighbors participating  2.150  0.001  1.101  2.016  0.003  1.097  
FEBC land area  0.252  0.004  1.414  0.293  0.001  1.457  
Age  0.030  0.013  1.496  0.028  0.027  1.526  
Gender  0.685  0.096  1.462  0.648  0.117  1.450  
Education  0.163  <0.001  1.475  0.157  <0.001  1.468  
Annual agricultural expenses  0.479  0.004  1.318  0.459  0.009  1.356  
Local offfarm income  0.046  <0.001  1.444  0.048  <0.001  1.476  
Household size  0.261  0.012  1.558  0.247  0.020  1.566  
NonGTGP land  0.097  0.014  1.373  0.082  0.042  1.373  
AIC  497.5  487.28 
Number of observations: 435. Bold numbers with asterisk indicate change of significance level from significant at 5% level to not significant at 5% level 
Table S3 Results of Moran’s test for deviance residuals in the models of Tables S1 and S2 
Model (Neighborhood)  Contiguity matrix  Eigenvectors  Moran’s I  Expected I  Variance  zscore  pvalue 

Model 2 (nonspatial model)  Neighbors within 0.1 km  NA  0.134  0.004  0.001  3.790  0.000 
Model 10 (0.1 km NB)  Neighbors within 0.1 km  EV1EV14  0.042  0.072  0.001  4.382  0.000 
Model 11 (0.1 km NB)  Neighbors within 0.1 km  EV1EV15  0.031  0.074  0.001  4.106  0.000 
Model 12 (0.1 km NB)  Neighbors within 0.1 km  EV1EV16  0.021  0.077  0.001  3.829  0.000 
Model 13 (0.1 km NB)  Neighbors within 0.1 km  EV1EV17  0.143  0.079  0.001  2.507  0.012 
Model 14 (0.1 km NB)  Neighbors within 0.1 km  EV1EV18  0.148  0.082  0.001  2.629  0.009 
Model 15 (0.1 km NB)  Neighbors within 0.1 km  EV1EV19  0.155  0.084  0.001  2.801  0.005 
Model 16 (0.1 km NB)  Neighbors within 0.1 km  EV1EV20  0.194  0.087  0.001  4.303  0.000 
Table S4 Comparison for the nonspatial model, bestpractice model, and Rook 3rd order ESF model: dependent variable, probability of enrolling in GTGP 
Model 2  Model 6  Model 24  

Contiguity  Nonspatial  Neighbors within 0.1 km  Rook 3rd order  
Number of candidate eigenvectors for stepwise procedure  NA  12  16  
Number of eigenvectors  0  5  4  
Eigenvectors  NA  EV9, EV4, EV6, EV10, EV17  EV16, EV2, EV10, EV5  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  2.239  0.027  1.028  0.288  
GTGP payment  4.878  <0.001  1.035  5.571  <0.001  1.057  4.422  0.001  1.036 
GTGP duration  0.008  0.816  1.048  0.002  0.959  1.084  0.006  0.877  1.065 
Economic trees  0.638  0.015  1.430  0.748  0.008  1.490  0.590  0.028  1.424 
Ecological plants  0.1  0.713  1.415  0.087  0.766  1.455  0.070  0.805  1.425 
Neighbors participating  1.626  0.006  1.035  1.791  0.004  1.053  1.597  0.009  1.038 
FEBC land area  0.159  0.033  1.199  0.180  0.021  1.231  0.169  0.031  1.248 
Age  0.025  0.009  1.129  0.034  0.001  1.240  0.022  0.032  1.185 
Gender  0.668  0.045  1.179  0.713  0.053  1.259  0.577  0.093  1.200 
Education  0.109  0.001  1.210  0.100  0.008  1.278  0.100  0.006  1.290 
Annual agricultural expenses  0.556  <0.001  1.196  0.504  0.002  1.221  0.598  <0.001  1.267 
Local offfarm income  0.037  0.002  1.259  0.039  0.002  1.309  0.041  0.001  1.233 
Household size  0.204  0.019  1.282  0.201  0.032  1.346  0.248  0.008  1.355 
NonGTGP land  0.086  0.011  1.225  0.091  0.011  1.267  0.117  0.002  1.365 
AIC  543.18  506.44  527.84 
Number of observations: 435. Bold indicates change of significance level from significant at 5% level to not significant at 5% level. 
Table S5 Results of nonspatial and spatial (Queen 1st5th order) models for probability of enrolling in GTGP 
Model 2  Model 17  Model 18  

Contiguity  Nonspatial  Queen 1st order  Queen 2nd order  
Number of candidate eigenvectors for stepwise procedure  NA  29  18  
Number of eigenvectors  0  19  4  
Eigenvectors  NA  EV16, EV31, EV40, EV34, EV28, EV38, EV2, EV5, EV39, EV22, EV35, EV18, EV19, EV10, EV24, EV6, EV25, EV13, EV4  EV16, EV2, EV10, EV5  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  0.65  0.551  1.028  0.288  
GTGP payment  4.878  <0.001  1.035  6.005  <0.001  1.102  4.422  0.001  1.036 
GTGP duration  0.008  0.816  1.048  0.015  0.709  1.156  0.006  0.877  1.065 
Model 2  Model 17  Model 18  
Contiguity  Nonspatial  Queen 1st order  Queen 2nd order  
Number of candidate eigenvectors for stepwise procedure  NA  29  18  
Number of eigenvectors  0  19  4  
Eigenvectors  NA  EV16, EV31, EV40, EV34, EV28, EV38, EV2, EV5, EV39, EV22, EV35, EV18, EV19, EV10, EV24, EV6, EV25, EV13, EV4  EV16, EV2, EV10, EV5  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
Economic trees  0.638  0.015  1.430  0.657  0.024  1.478  0.59  0.028  1.424 
Ecological plants  0.1  0.713  1.415  0.095  0.759  1.511  0.07  0.805  1.425 
Neighbors participating  1.626  0.006  1.035  1.362  0.039  1.083  1.597  0.009  1.038 
FEBC land area  0.159  0.033  1.199  0.326  <0.001  1.490  0.169  0.031  1.248 
Age  0.025  0.009  1.129  0.022  0.058  1.331  0.022  0.032  1.185 
Gender  0.668  0.045  1.179  0.653  0.103  1.425  0.577  0.093  1.200 
Education  0.109  0.001  1.210  0.154  <0.001  1.454  0.1  0.006  1.290 
Annual agricultural expenses  0.556  <0.001  1.196  0.449  0.009  1.460  0.598  <0.001  1.267 
Local offfarm income  0.037  0.002  1.259  0.056  <0.001  1.423  0.041  0.001  1.233 
Household size  0.204  0.019  1.282  0.364  0.001  1.616  0.248  0.008  1.355 
NonGTGP land  0.086  0.011  1.225  0.127  0.002  1.454  0.117  0.002  1.365 
AIC  543.18  509.58  527.84  
Model 19  Model 20  Model 21  
Contiguity  Queen 3rd order  Queen 4th order  Queen 5th order  
Number of candidate eigenvectors for stepwise procedure  10  9  8  
Number of eigenvectors  4  4  4  
Eigenvectors  EV5, EV4, EV2, EV6  EV5, EV4, EV2, EV6  EV5, EV4, EV2, EV6  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.363  0.148  1.368  0.146  1.371  0.145  
GTGP payment  5.049  <0.001  1.047  5.049  <0.001  1.047  5.048  <0.001  1.047 
GTGP duration  0.005  0.887  1.057  0.005  0.897  1.058  0.004  0.901  1.058 
Economic trees  0.59  0.027  1.434  0.591  0.026  1.434  0.591  0.026  1.433 
Ecological plants  0.093  0.739  1.427  0.095  0.733  1.426  0.096  0.73  1.426 
Neighbors participating  1.58  0.009  1.047  1.589  0.008  1.047  1.595  0.008  1.048 
FEBC land area  0.167  0.029  1.216  0.165  0.03  1.215  0.164  0.031  1.214 
Age  0.026  0.01  1.158  0.026  0.009  1.158  0.026  0.009  1.157 
Gender  0.74  0.03  1.224  0.738  0.031  1.223  0.736  0.031  1.222 
Education  0.119  0.001  1.251  0.119  0.001  1.250  0.119  0.001  1.250 
Annual agricultural expenses  0.477  0.001  1.217  0.475  0.001  1.220  0.475  0.001  1.222 
Local offfarm income  0.041  0.001  1.302  0.041  0.001  1.304  0.041  0.001  1.305 
Household size  0.296  0.001  1.424  0.299  0.001  1.428  0.3  0.001  1.431 
NonGTGP land  0.111  0.002  1.340  0.11  0.003  1.336  0.109  0.003  1.334 
AIC  538.44  538.44  538.08 
Number of observations: 435. 
Table S6 Results of nonspatial and spatial (Rook 1st5th order) models for probability of enrolling in GTGP 
Model 2  Model 22  Model 23  

Contiguity  Nonspatial  Rook 1st order  Rook 2nd order  
Number of candidate eigenvectors for stepwise procedure  NA  43  18  
Number of Eigenvectors  0  19  4  
Eigenvectors  NA  EV16, EV31, EV40, EV34, EV28, EV38, EV2, EV5, EV39, EV22, EV35, EV18, EV19, EV10, EV24, EV6, EV25, EV13, EV4  EV16, EV2, EV10, EV5  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  Coef.  pvalue  VIF  
(Intercept)  1.544  0.089  0.65  0.551  1.068  0.269  
GTGP payment  4.878  <0.001  1.035  6.005  <0.001  1.102  4.392  0.001  1.036 
GTGP duration  0.008  0.816  1.048  0.015  0.709  1.156  0.006  0.876  1.066 
Economic trees  0.638  0.015  1.430  0.657  0.024  1.478  0.593  0.027  1.423 
Ecological plants  0.1  0.713  1.415  0.095  0.759  1.511  0.06  0.831  1.423 
Neighbors participating  1.626  0.006  1.035  1.362  0.039  1.083  1.601  0.009  1.039 
FEBC land area  0.159  0.033  1.199  0.326  <0.001  1.490  0.168  0.032  1.249 
Age  0.025  0.009  1.129  0.022  0.058  1.331  0.022  0.028  1.180 
Gender  0.668  0.045  1.179  0.653  0.103  1.425  0.595  0.083  1.198 
Education  0.109  0.001  1.210  0.154  <0.001  1.454  0.1  0.006  1.288 
Annual agricultural expenses  0.556  <0.001  1.196  0.449  0.009  1.460  0.597  <0.001  1.268 
Local offfarm income  0.037  0.002  1.259  0.056  <0.001  1.423  0.04  0.001  1.237 
Household size  0.204  0.019  1.282  0.364  0.001  1.616  0.242  0.009  1.355 
NonGTGP land  0.086  0.011  1.225  0.127  0.002  1.454  0.116  0.002  1.361 
AIC  543.18  509.58  528.8  
Model 24  Model 25  Model 26  
Contiguity  Rook 3rd order  Rook 4th order  Rook 5th order  
Number of candidate eigenvectors for stepwise procedure  16  15  15  
Number of Eigenvectors  4  5  4  
Eigenvectors  EV16, EV2, EV10, EV5  EV2, EV5, EV10, EV14, EV6  EV2, EV5, EV10, EV14  
Coef.  pvalue  VIF  Coef.  pvalue  VIF  pvalue  pvalue  VIF  
(Intercept)  1.028  0.288  1.324  0.158  1.433  0.123  
GTGP payment  4.422  0.001  1.036  4.822  <0.001  1.041  4.727  0.001  1.037 
GTGP duration  0.006  0.877  1.065  0.003  0.934  1.058  0.003  0.942  1.057 
Economic trees  0.59  0.028  1.424  0.574  0.031  1.429  0.598  0.024  1.425 
Ecological plants  0.07  0.805  1.425  0.039  0.889  1.416  0.029  0.916  1.416 
Neighbors participating  1.597  0.009  1.038  1.518  0.011  1.042  1.572  0.009  1.039 
FEBC land area  0.169  0.031  1.248  0.152  0.049  1.215  0.147  0.055  1.209 
Age  0.022  0.032  1.185  0.025  0.011  1.147  0.026  0.008  1.140 
Gender  0.577  0.093  1.200  0.642  0.059  1.201  0.609  0.072  1.184 
Education  0.1  0.006  1.290  0.119  0.001  1.290  0.116  0.001  1.281 
Annual agricultural expenses  0.598  <0.001  1.267  0.497  0.001  1.234  0.519  0.001  1.227 
Local offfarm income  0.041  0.001  1.233  0.038  0.001  1.254  0.038  0.002  1.254 
Household size  0.248  0.008  1.355  0.255  0.006  1.391  0.243  0.008  1.372 
NonGTGP land  0.117  0.002  1.365  0.103  0.005  1.348  0.102  0.005  1.353 
AIC  527.84  538.92  538.9 
Number of observations: 435. Bold indicates change of significance level from significant at 5% level to not significant at 5% level. 
Table S7 Results of Moran’s test for deviance residuals in the models of Tables S5 and S6 
Model (Neighborhood)  Contiguity matrix  Moran’s I  Expected I  Variance  Zscore  pvalue 

Model 2 (Queen 1st order)  Queen 1st order  0.034  0.005  0.000  2.611  0.009 
Model 17 (Queen 1st order)  Queen 1st order  0.055  0.032  0.000  1.906  0.057 
Model 2 (Queen 2nd order)  Queen 2nd order  0.022  0.004  0.000  3.147  0.002 
Model 18 (Queen 2nd order)  Queen 2nd order  0.004  0.009  0.000  0.658  0.511 
Model 2 (Queen 3rd order)  Queen 3rd order  0.007  0.003  0.000  2.117  0.034 
Model 19 (Queen 3rd order)  Queen 3rd order  0.007  0.003  0.000  0.752  0.452 
Model 2 (Queen 4th order)  Queen 4th order  0.004  0.003  0.000  1.377  0.169 
Model 20 (Queen 4th order)  Queen 4th order  0.010  0.003  0.000  1.357  0.175 
Model 2 (Queen 5th order)  Queen 5th order  0.002  0.003  0.000  1.036  0.300 
Model 21 (Queen 5th order)  Queen 5th order  0.011  0.003  0.000  1.625  0.104 
Model 2 (Rook 1st order)  Rook 1st order  0.034  0.005  0.000  2.611  0.009 
Model 22 (Rook 1st order)  Rook 1st order  0.055  0.032  0.000  1.906  0.057 
Model 2 (Rook 2nd order)  Rook 2nd order  0.022  0.004  0.000  3.147  0.002 
Model 23 (Rook 2nd order)  Rook 2nd order  0.004  0.009  0.000  0.637  0.524 
Model 2 (Rook 3rd order)  Rook 3rd order  0.019  0.004  0.000  2.867  0.004 
Model 24 (Rook 3rd order)  Rook 3rd order  0.005  0.009  0.000  0.524  0.601 
Model 2 (Rook 4th order)  Rook 4th order  0.018  0.004  0.000  2.750  0.006 
Model 25 (Rook 4th order)  Rook 4th order  0.001  0.010  0.000  1.399  0.162 
Model 2 (Rook 5th order)  Rook 5th order  0.018  0.004  0.000  2.686  0.007 
Model 26 (Rook 5th order)  Rook 5th order  0.001  0.009  0.000  1.446  0.148 
Bold numbers indicate Moran’s I significant at 5% level. 
This research was funded by the National Science Foundation under the Dynamics of Coupled Natural and Human Systems program [Grant DEB1212183 and BCS1826839]. This research also received financial and research support from San Diego State University. We thank Fanjingshan National Nature Reserve in China and the Research Center for EcoEnvironmental Sciences at the Chinese Academy of Sciences. The collaboration of Bilsborrow was supported by the Population Research Infrastructure Program grant (P2C, HD050924) to the Carolina Population Center at the University of North Carolina by the US National Institute of Child Health and Human Development. We would also like to thank the editor and anonymous reviewer for their insightful comments.
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