Does Poverty Matter in Payment for Ecosystem Services Program? Participation in the New Stage Sloping Land Conversion Program
Abstract
:1. Introduction
2. Mechanisms Linking Multidimensional Poverty with Participation in PES Programs
3. Material and Methods
3.1. The NSLCP
3.2. Study Area and Data Collection
3.3. Data Analysis
3.3.1. Definition and Measurement of Multidimensional Poverty
3.3.2. Estimation Approach
4. Results and Discussion
4.1. Links between Poverty and Participation in the NSLCP
4.2. Poverty’s Impacts on Participation in the NSLCP
4.3. Poverty’s Impacts on Participation Efforts
5. Conclusions and Implications
Author Contributions
Acknowledgments
Conflicts of Interest
Appendix A
Sample | County | Province/Implementation Area | |
---|---|---|---|
1. Demographic characteristics | |||
Family size | 3.62 (1.57) | 3.96 (1.28) | 4.21 (1.35) |
Number of labors | 2.82 (1.49) | 2.45 (1.06) | 3.46 (1.37) |
Gender of household head: male | 0.94 (0.24) | — | 0.92 |
Age of HH. (years) | 54.86 (10.83) | 55.89 (11.53) | 49.78 (11.06) |
Age structure: kid ratio(16-) | 0.11 (0.16) | — | 0.14 |
Elderly ratio (65+) | 0.12 (0.26) | — | 0.09 |
Education of household head:years | 6.51 (4.06) | 5.92 (3.57) | 6.34 (3.58) |
2. Socio-economic characteristics | |||
Farmland per capita(ha) | 0.16 (0.19) | 0.15 | 0.19 (0.22) |
House area (m2) | 95.98 (72.05) | — | 130.47 (95.94) |
House area per capita (m2) | 32.87 | 29.5 | 34.9 |
Converted land area(ha) | 1.51 (16.78) | 2.74 (1.34) | — |
Days for farming work | 93.22 (145.57) | 118.56 (47.67) | — |
Days for non-farm work | 256.69 (298.25) | 231.8 (173.48) | — |
Net income per capita | 9966 | 9110 | 7932 |
Variables | Key Questions and Calculations | Literature |
---|---|---|
1. Dependent variables | ||
Participation | Did you participate in NSLCP: =1 if answer is yes; =0 otherwise | |
Participation efforts | Time for converted land management (Days): How many did days your family invest labor for enrolled land management last year? | |
2. Income Poverty variables | ||
Netincome | Annual net income per capita in 2013 = annual net income in 2013/family size; annual net income in 2013 is the sum of net incomes from agriculture, forestry, livestock, outmigration, nonfarm, governmental subsidies in 2014 and income changes compared to last year. (Yuan) | (Statistics Bureau of Shaanxi Province, 2014, 2015) |
IPoverty | =1 if net income per capita ≤2600 yuan per year, 0 otherwise | |
3. Education Poverty variables | ||
EPoverty | =1 if more than half adult members are illiterate or at least one children drop out, 0 otherwise | (Wang and Alkire, 2009; United Nations Development Programme, 2010; Alkire and Santos, 2013; Kolinjivadi et al., 2015) |
E1 | Education Attainment (E1) presents the general education level of the adults, calculated as a binary variable. E1 = 1 if more than half adult members are illiterate. Respondents were asked about the education level of each family member: 1. Illiterate; 2. Primary school; 3. Middle school; 4. High school; 5. Technical secondary school; 6. College or higher education. | |
E2 | School attendance (E2) presents the schooling status of the children in a household. E2 = 1 if household has at least one school-age child (5–16 in China) is not attending school. | |
Eduratio | Ratio of family members with education of middle school or higher (%) | |
4. Health Poverty variables | ||
HPoverty | Ratio of healthy members = number of members in good health/family size (%): Respondents were asked about the health state of each family members: 1. Good; 2. Medium; 3. Bad; | (Ma et al., 2017) |
H1 | =1 if at least one family member are in bad health. Respondents were asked about the health state of each family member: 1. Good; 2. Medium; 3. Bad. | |
H2 | =1 if a household has at least one mental or physical disability and received the disabled subsidies. Households were asked how much about compensation for disability. | |
H3 | =1 if households’ medical consumption accounts for the largest part compared to other expenditures. Households were asked the detailed expenditures of housing, durable goods, schooling, energy, food, etc. in 2014. | |
HeadHealth | Health state of household head: 1. Good; 2. Medium; 3. Bad; | |
5. Food Poverty variables | ||
FPoverty | Value of grain stock per capita = farmland products value/family size (yuan): What is the in-kind value of products on farmlands except those for sales? (yuan) | (Wang and Alkire, 2009) (Xu et al., 2006; Veen and Gebrehiwot, 2011) |
F1 | =1 if household’s farmland per capita is smaller than the basic line for rural households (0.07 ha). | |
F2 | =1 if household’s consumption for food is less than food demand per capita for balanced nutrition (880 yuan, 400 kg*2.2 yuan/kg). Respondents were asked about household annual cash for food and value of grain stock production. The food demand per capita for balanced nutrition is 400 kg calculated by Chinese Academy of Agricultural Sciences(Chinese Academy of Agricultural Sciences, 1986). And the average price of grain (2.2 yuan/kg) in study region in 2013 comes from documents of Statistics Bureau of Shaanxi Province. | |
6. Rights Poverty variables | ||
RPoverty | Autonomy scores are calculated as the sum of five binary questions: 1. Did you have autonomy to decide whether to participate or not? 2. Did village ask your opinion about SLCP design and implementation? 3. Did you have autonomy to decide the size to be enrolled? 4. Did you have autonomy to decide which land plot to be enrolled? 5. Did you have autonomy to choose the tree type to plant on converted lands? =1, if the answer is yes; =0 otherwise | (Xu et al., 2010; Wang, 2017) |
R1 | To reflect households’ engagement in village affairs, households were asked with the question how frequently they attended village meetings and other public affairs for one year: 5-scale from very frequently to seldom. R1 = 1 if the answer is seldom or less. | |
R2 | =1 if autonomy scores equal to 0; =0 otherwise. | |
R3 | Respondents were asked to evaluate the fairness of SLCP implementation: 5-scale from very fair to very unfair. R3 = 1 if the answer is unfair or very unfair, 0 otherwise. | |
Voluntary | =1 if household has autonomy to decide whether to participate or not, 0 otherwise. | |
7. Assets Poverty variables | ||
APoverty | Sum of the number of productive assets and durable goods owned, including agricultural machinery or commercial equipment (digging machine, folk lift, etc.), motorized vehicles (bicycle, motorcycle, tractor, and automobile), television, refrigerator, washing machine, and computer. | (Kolinjivadi et al., 2015; Wang, 2017) |
A1 | =1 if household owns 2 or less durable goods. | |
Productasset | Sum of the number of assets for production, like vehicles, agricultural machinery or commercial facilities. | |
8. Living Poverty variables | ||
LPoverty | Living area per capita = house area/family size. (m2) Question: what is the area of your living house? (m2) | (Wang and Alkire, 2009; National Development and Reform Committee, 2016) |
L1 | Question: Do you own the house you are living? 1. Yes; 0. No; L1 = 1 if the answer is no; L1 = 0 otherwise. | |
L2 | Question: what is the structure of your living house? 1. Wood-mud; 2. Brick-wood; 3. Brick-concrete; 4. Others; L2 = 1 if the house is wood-mud constructed; 0 otherwise. | |
L3 | L3 = 1 if living area per capita is smaller than 25 m2 which is the minimum area for security housing in National 13th Five Planning for Resettlement and Anti-poverty Program. | |
9. Degree of Multidimensional Poverty | ||
DMP | Degree of multidimensional poverty reflects the number of human wellbeing dimensions deprived. It is the sum of seven binary variables (I, E, H, L, F, A, R) which represent each dimension of poverty. | |
10. Control Variables | ||
Landslope | Land area with slopes over 25 degrees (ha). Questions were asked about the slope of each land plot held by households: 1. <15 degree; 2. 15–25 degree; 3. 26–35 degree; 4. >35 degree. | (Zbinden and Lee, 2005; Pagiola et al., 2008; Zanella et al., 2014) |
Landquality | Ratio of land area with poor soil quality (%). Questions were asked about the soil quality of each land plot held by households: 1.good; 2. medium; 3.bad. | |
ElderlyR | Ratio of the elderly (%) = the number of elderly(65+)/family size; | (Pagiola et al., 2008; Duesberg et al., 2014; Zanella et al., 2014) |
RiskPreference | Risk preference is the sum of the scores of three questions: 1. To what degree would they like to be the first to undertake a new type production. 2. To what degree would they like to undertake the production with higher profit and risk of loss and debt. 3. To what degree do you agree with the idea that only innovative production with and risks can bring wealth to the poor area: 1–5 scores for absolutely unwilling/disagree to absolutely willing/agree. | |
Payment Gap | Payment gap= (Payments-WTA)/WTA. It presents the deviation of the actual SLCP compensation they got (Payments) from the payments they are willing to accept (WTA). Question was asked about the minimum amount of payments (yuan/ha) the household would like to accept for land conversion. | |
Remittance | Annual total amount of remittance from outmigration. | |
Publicity | Publicity presents the transparency of the SLCP implementation in local village. It is sum of three binary questions: 1. did the village have publicity of the distribution of quotas in SLCP? 2. Did the village publicize the compensation process? 3. Did the village publicize the distribution of other tasks for supporting SLCP? =1 if the answer is yes; =0 otherwise. | (Pagiola et al., 2005; Zbinden and Lee, 2005; Uchida et al., 2007; Pagiola et al., 2008; Mullan and Kontoleon, 2012; Duesberg et al., 2014) |
Irregularity | Households were asked whether irregular practices existed in local village during SLCP implementation, including illegal logging, enforced conversion, reconversion, delayed payments, etc. | |
Training | Number of household members ever attend training courses, including training for agro-forestry, husbandry, outmigration, etc. | |
Accessloan | It presents the development of credit market in local village: Probability for the households to get loans: 5-point scale from impossible to definitely. | |
Tenure | =1 if the converted lands were issued with certificates; =0 otherwise | |
Township | Distance from county administrative center:1 = near; 2 = medium; 3 = far | (Bullock and King, 2011) |
WillNSLCP | Degree of Willingness to Participate in the NSLCP: respondents were asked the question that whether they are willing to participate in the NSLCP: 5-scale from very unwilling to very willing. | |
ResponsiMa | Feeling responsible for land management: respondents were asked the question that whether they feel responsible to manage enrolled forestland: 1 = yes, 0 = no. |
IPoverty | DMP | EPoverty | HPoverty | FPoverty | RPoverty | APoverty | LPoverty | EdlerlyR | Landslope | |
IP. | 1.00 | |||||||||
DMP | 0.18 | 1.00 | ||||||||
EP. | 0.06 | 0.43 | 1.00 | |||||||
HP. | −0.13 | −0.34 | −0.29 | 1.00 | ||||||
FP. | −0.08 | −0.24 | −0.01 | 0.13 | 1.00 | |||||
RP. | 0.09 | −0.17 | −0.11 | 0.01 | 0.15 | 1.00 | ||||
AP. | 0.02 | −0.38 | −0.29 | 0.26 | 0.13 | 0.08 | 1.00 | |||
LP. | 0.15 | −0.34 | 0.08 | −0.08 | 0.10 | 0.13 | 0.10 | 1.00 | ||
Eld. | 0.11 | 0.19 | 0.31 | −0.30 | 0.01 | 0.00 | −0.10 | 0.06 | 1.00 | |
Slo. | −0.11 | 0.02 | −0.03 | 0.04 | 0.03 | 0.01 | −0.05 | −0.03 | 0.00 | 1.00 |
Qua. | 0.12 | 0.11 | 0.09 | −0.14 | −0.14 | −0.04 | −0.06 | 0.02 | 0.08 | 0.11 |
Trai. | 0.04 | −0.13 | −0.15 | 0.06 | 0.11 | 0.17 | 0.10 | −0.04 | −0.03 | −0.02 |
Loa. | −0.11 | −0.15 | −0.17 | 0.11 | 0.06 | 0.13 | 0.12 | 0.02 | −0.07 | 0.03 |
Rem | −0.21 | −0.04 | −0.09 | 0.15 | −0.13 | −0.15 | 0.01 | −0.23 | −0.21 | 0.11 |
Pub. | −0.05 | −0.16 | −0.04 | −0.02 | −0.02 | −0.09 | 0.16 | 0.17 | 0.01 | −0.11 |
Inrr. | −0.10 | 0.17 | −0.07 | −0.12 | −0.09 | 0.07 | −0.03 | −0.14 | 0.00 | −0.03 |
Cte. | 0.05 | −0.10 | 0.04 | −0.10 | 0.04 | 0.11 | 0.01 | −0.03 | 0.05 | −0.03 |
Risk | 0.05 | 0.00 | −0.05 | 0.14 | 0.10 | 0.09 | 0.14 | 0.01 | −0.12 | 0.05 |
Gap. | 0.02 | 0.02 | −0.05 | −0.07 | −0.07 | 0.06 | −0.04 | 0.00 | 0.06 | −0.17 |
Tp. | 0.08 | −0.02 | −0.05 | 0.03 | 0.02 | 0.27 | −0.02 | 0.01 | −0.02 | −0.01 |
Landquality | Training | Accessloan | Remittance | Publicity | Irregularity | Tenure | Riskprefer. | Payment Gap | Township | |
IP. | ||||||||||
DMP | ||||||||||
EP. | ||||||||||
HP. | ||||||||||
FP. | ||||||||||
RP. | ||||||||||
AP. | ||||||||||
LP. | ||||||||||
Eld. | ||||||||||
Slo. | ||||||||||
Qua. | 1.00 | |||||||||
Trai. | −0.15 | 1.00 | ||||||||
Loa. | −0.06 | 0.02 | 1.00 | |||||||
Rem | 0.08 | −0.12 | 0.06 | 1.00 | ||||||
Pub. | 0.03 | −0.04 | −0.01 | −0.01 | 1.00 | |||||
Inrr. | 0.02 | −0.04 | −0.04 | 0.18 | −0.01 | 1.00 | ||||
Cte. | −0.11 | 0.00 | 0.01 | −0.05 | 0.15 | −0.06 | 1.00 | |||
Risk | −0.07 | 0.07 | 0.11 | 0.04 | −0.16 | −0.04 | −0.04 | 1.00 | ||
Gap. | 0.01 | −0.01 | −0.03 | 0.00 | 0.08 | 0.04 | −0.02 | −0.07 | 1.00 | |
Tp. | 0.01 | 0.01 | 0.00 | −0.03 | 0.08 | −0.03 | 0.09 | 0.07 | 0.06 | 1.00 |
NSLCP | Participation | Participation Efforts | ||||
---|---|---|---|---|---|---|
Original | Robust | Original | Robust | Original | Robust | |
EPoverty → Eduratio | −0.14 (0.08) * | 0.61 (0.37) * | −2.96 (3.58) | −4.68 (3.74) | —— | —— |
Eduratio square | —— | −0.64 (0.39) | —— | —— | —— | —— |
HPoverty → headhealth | −0.17 (0.12) | −0.14 (0.12) | 9.05 (5.11) * | −5.43 (2.21) ** | —— | —— |
FPoverty → Self-sufficient ratio | 0.00 (0.00) * | 0.05 (0.09) | 0.00 (0.00) | 0.00 (0.00) | —— | —— |
RPoverty → voluntary | 0.05 (0.02) * | 0.13 (0.07) * | −1.67 (1.38) | −1.50 (1.29) | —— | —— |
APoverty → Productasset | −0.03 (0.02) | −0.03 (0.02) | 3.25 (1.57) ** | 6.82 (2.99) ** | —— | —— |
LPoverty | 0.00 (0.00) | 0.00 (0.00) | −0.11 (0.08) | −0.15 (0.08) * | —— | —— |
IPoverty → netincomeper | —— | —— | —— | —— | −12.88 (6.19) ** | 0.00 (0.00) ** |
Income square | —— | —— | —— | —— | 0.00 (0.00) ** | |
ElderlyR | −0.25 (0.15) | −0.27 (0.14) * | 3.03 (10.07) | 5.93 (10.90) | −2.25 (10.96) | −0.10 (13.09) |
RiskPreference | −0.01 (0.01) | −0.01 (0.02) | −0.80 (0.55) | −0.73 (0.52) | −0.12 (0.50) | −0.03 (0.61) |
Payment Gap | 0.00 (0.04) | 0.00 (0.07) | 1.24 (2.30) | 1.97 (2.23) | −0.40 (1.95) | −0.03 (2.31) |
Remittance | 0.00 (0.00) | 0.00 (0.05) | −0.00 (0.00) | 0.00 (0.00) * | —— | —— |
Publicity | 0.06 (0.03) ** | 0.06 (0.03) ** | 1.62 (1.54) | 2.35 (1.39) * | 2.20 (1.25) * | 2.70 (1.64) |
Irregular | −0.03 (0.02) | −0.02 (0.00) | 1.92 (1.68) | 1.88 (1.64) | 0.37 (1.68) | 0.20 (1.38) |
Training | 0.05 (0.05) | 0.08 (0.03) * | 0.41 (1.83) | 0.89 (1.69) | 1.46 (1.68) | 1.51 (2.17) |
Accessloan | −0.02 (0.03) | −0.01 (0.10) | 3.45 (1.78) * | 2.24 (1.52) | 2.34 (1.62) | 2.45 (1.79) |
Tenure | 0.00 (0.07) | 0.01 (0.03) | 7.01 (3.72) * | 6.95 (3.59) * | 5.55 (3.67) | 6.51 (4.11) |
Township | 0.03 (0.04) | 0.04 (0.01) | 1.18 (2.23) | 0.93 (2.32) | 0.03 (1.77) | −0.75 (2.40) |
Landslope | 0.08 (0.03) ** | 0.08 (0.04) ** | 5.44 (1.89) *** | 6.13 (1.98) *** | 4.18 (1.76) ** | 3.57 (1.61) ** |
Landquality | −0.03 (0.10) | −0.04 (0.03) | −6.73 (4.86) | −6.74 (5.14) | −8.42 (5.09) * | −10.13 (5.91) * |
WillNSLCP | 0.00 (0.03) | 0.01 (0.04) | 0.14 (1.96) | 0.25 (2.35) | −1.02 (2.41) | −1.16 (1.95) |
Log Likelihood | −133.39 | −130.99 | −295.71 | −290.05 | −302.23 | −300.76 |
Pseudo R2 | 0.11 | 0.10 | 0.05 | 0.05 | 0.03 | 0.03 |
References
- Reardon, T.; Vosti, S.A. Links between rural poverty and the environment in developing-countries—Asset categories and investment poverty. World Dev. 1995, 23, 1495–1506. [Google Scholar] [CrossRef]
- Narain, U.; Gupta, S.; Veld, K.V. Poverty and the environment: Exploring the relationship between household incomes, private assets, and natural assets. Land Econ. 2008, 84, 148–167. [Google Scholar] [CrossRef]
- Barbier, E.B. Poverty, development, and environment. Environ. Dev. Econ. 2010, 15, 635–660. [Google Scholar] [CrossRef]
- Milder, J.C.; Scherr, S.J.; Bracer, C. Trends and future potential of payment for ecosystem services to alleviate rural poverty in developing countries. Ecol. Soc. 2010, 15, 4. [Google Scholar] [CrossRef]
- Bulte, E.H.; Lipper, L.; Stringer, R.; Zilberman, D. Payments for ecosystem services and poverty reduction: Concepts, issues, and empirical perspectives. Environ. Dev. Econ. 2008, 13, 245–254. [Google Scholar] [CrossRef]
- Démurger, S.; Pelletier, A. Volunteer and satisfied? Rural households’ participation in a payments for environmental services programme in Inner Mongolia. Ecol. Econ. 2015, 116, 25–33. [Google Scholar] [CrossRef]
- Rodríguez, L.C.; Pascual, U.; Muradian, R.; Pazmino, N.; Whitten, S. Towards a unified scheme for environmental and social protection: Learning from PES and CCT experiences in developing countries. Ecol. Econ. 2011, 70, 2163–2174. [Google Scholar] [CrossRef]
- Grieg-Gran, M.; Porras, I.; Wunder, S. How can market mechanisms for forest environmental services help the poor? Preliminary lessons from Latin America. World Dev. 2005, 33, 1511–1527. [Google Scholar] [CrossRef]
- Gauvin, C.; Uchida, E.; Rozelle, S.; Xu, J.; Zhan, J. Cost-effectiveness of payments for ecosystem services with dual goals of environment and poverty alleviation. Environ. Manag. 2010, 45, 488–501. [Google Scholar] [CrossRef] [PubMed]
- Kolinjivadi, V.; Grant, A.; Adamowski, J.; Kosoy, N. Juggling multiple dimensions in a complex socio-ecosystem: The issue of targeting in payments for ecosystem services. Geoforum 2015, 58, 1–13. [Google Scholar] [CrossRef]
- Pagiola, S.; Arcenas, A.; Platais, G. Can payments for environmental services help reduce poverty? An exploration of the issues and the evidence to date from Latin America. World Dev. 2005, 33, 237–253. [Google Scholar] [CrossRef]
- Ferraro, P.J.; Hanauer, M.M.; Miteva, D.A.; Nelson, J.L.; Pattanayak, S.K.; Nolte, C.; Sims, K.R. Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation. Proc. Natl. Acad. Sci. USA 2015, 112, 7420–7425. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fisher, J.A.; Patenaude, G.; Giri, K.; Lewis, K.; Meir, P.; Pinho, P.; Rounsevell, M.D.A.; Williams, M. Understanding the relationships between ecosystem services and poverty alleviation: A conceptual framework. Ecosyst. Serv. 2014, 7, 34–45. [Google Scholar] [CrossRef]
- Antle, J.M.; Stoorvogel, J.J. Payments for ecosystem services, poverty and sustainability: The case of agricultural soil carbon sequestration. Nat. Res. Manag. Policy 2009, 31, 133–161. [Google Scholar]
- Mazunda, J.; Shively, G. Measuring the forest and income impacts of forest user group participation under Malawi’s Forest Co-management Program. Ecol. Econ. 2015, 119, 262–273. [Google Scholar] [CrossRef]
- Zanella, M.A.; Schleyer, C.; Speelman, S. Why do farmers join Payments for Ecosystem Services (PES) schemes? An assessment of PES water scheme participation in Brazil. Ecol. Econ. 2014, 105, 166–176. [Google Scholar] [CrossRef]
- Pagiola, S. Payments for environmental services in Costa Rica. Ecol. Econ. 2008, 65, 712–724. [Google Scholar] [CrossRef] [Green Version]
- National Development and Reform Commission. Notice of Issuing New-Stage SLoping Land Conversion Program Scheme; National Development and Reform Commission: Beijing, China, 2014.
- China State Council. Accurate Targeting of Poverty Alleviation Program; China State Council: Beijing, China, 2015.
- Duan, W.; Lang, Z.; Wen, Y. The effects of the sloping land conversion program on poverty alleviation in the Wuling mountainous area of China. Small Scale For. 2015, 14, 331–350. [Google Scholar] [CrossRef]
- Li, H.; Yao, S.; Yin, R.; Liu, G. Assessing the decadal impact of China’s sloping land conversion program on household income under enrollment and earning differentiation. For. Policy Econ. 2015, 61, 95–103. [Google Scholar] [CrossRef]
- Liu, Z.; Gong, Y.; Kontoleon, A. How do payments for environmental services affect land tenure? Theory and evidence from China. Ecol. Econ. 2018, 144, 195–213. [Google Scholar] [CrossRef]
- Ferraro, P.J.; Hanauer, M.M. Protecting ecosystems and alleviating poverty with parks and reserves: ‘Win-Win’ or tradeoffs? Environ. Resour. Econ. 2010, 48, 269–286. [Google Scholar] [CrossRef]
- Pagiola, S.; Rios, A.R.; Arcenas, A. Can the poor participate in payments for environmental services? Lessons from the Silvopastoral Project in Nicaragua. Environ. Dev. Econ. 2008, 13, 299–325. [Google Scholar] [CrossRef]
- Feng, L.; Xu, J. Farmers’ willingness to participate in the next-stage grain-for-green project in the three gorges reservoir area, China. Environ. Manag. 2015, 56, 505–518. [Google Scholar] [CrossRef] [PubMed]
- Uchida, E.; Xu, J.; Xu, Z.; Rozelle, S. Are the poor benefiting from China’s land conservation program? Environ. Dev. Econ. 2007, 12, 593–620. [Google Scholar] [CrossRef]
- Duesberg, S.; Upton, V.; O’Connor, D.; Dhubháin, Á.N. Factors influencing Irish farmers’ afforestation intention. For. Policy Econ. 2014, 39, 13–20. [Google Scholar] [CrossRef]
- Zbinden, S.; Lee, D.R. Paying for environmental services: An analysis of participation in costa Rica’s PSA program. World Dev. 2005, 33, 255–272. [Google Scholar] [CrossRef]
- Mullan, K.; Kontoleon, A. Participation in payments for ecosystem services programs: Accounting for participant heterogeneity. J. Environ. Econ. Policy 2012, 1, 235–254. [Google Scholar] [CrossRef]
- Alkire, S.; Santos, M.E. A multidimensional approach: Poverty measurement & beyond. Soc. Indic. Res. 2013, 12, 239–257. [Google Scholar]
- Andam, K.S.; Ferraro, P.J.; Sims, K.R.; Healy, A.; Holland, M.B. Protected areas reduced poverty in Costa Rica and Thailand. Proc. Natl. Acad. Sci. USA 2010, 107, 9996–10001. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, X. The Measurement of Poverty Theories and Methods, 2nd ed.; Social Sciences Academic Press: Beijing, China, 2017. [Google Scholar]
- Guo, H.; Li, B.; Hou, Y.; Lu, S.; Nan, B. Rural households’ willingness to participate in the grain for green program again: A case study of Zhungeer, China. For. Policy Econ. 2014, 44, 42–49. [Google Scholar] [CrossRef]
- China State Council. Outline of Poverty Alleviation Program of Rural China; China State Council: Beijing, China, 2011.
- Alkire, S. Foster, J. Counting and multidimensional poverty measurement. J. Public Econ. 2011, 95, 476–487. [Google Scholar] [CrossRef]
- Ma, B.; Ding, H.; Wen, Y. Impacts of biodiversity on multi-dimensional poverty. J. Agrotech. 2017, 4, 116–128. [Google Scholar]
- Wang, X.; Alkire, S. Mesurement of multi-dimensional poverty in China: Estimation and policy implication. Chin. Rural. Econ. 2009, 4, 10–23. [Google Scholar]
- Xu, J.T.; Tao, R.; Xu, Z.G.; Bennett, M.T. China’s sloping land conversion program: Does expansion equal success? Land Econ. 2010, 86, 219–244. [Google Scholar] [CrossRef]
- Zilberman, D.; Lipper, L.; McCarthy, N. When could payments for environmental services benefit the poor? Environ. Dev. Econ. 2008, 13, 255–278. [Google Scholar] [CrossRef]
- Alix-Garcia, J.; De Janvry, A.; Sadoulet, E. The role of deforestation risk and calibrated compensation in designing payments for environmental services. Environ. Dev. Econ. 2008, 13, 375–394. [Google Scholar] [CrossRef]
- Ministry of Environmental Protection. Plan for Conserving National Ecologically Vulnerable Zones; China’s Ministry of Environmental Protection: Beijing, China, 2008.
- Wunder, S. Payments for Environmental Services: Some Nuts and Bolts; CIFOR Occasional Paper No 42; Center for International Forestry Research: Jakarta, Indonesia, 2005. [Google Scholar]
- Bremer, L.L.; Farley, K.A.; Lopez-Carr, D. What factors influence participation in payment for ecosystem services programs? An evaluation of Ecuador’s SocioPáramo program. Land Use Policy 2014, 36, 122–133. [Google Scholar] [CrossRef]
- Wunder, S. Payments for environmental services and the poor: Concepts and preliminary evidence. Environ. Dev. Econ. 2008, 13. [Google Scholar] [CrossRef]
- Bennett, M.T. China’s sloping land conversion program: Institutional innovation or business as usual? Ecol. Econ. 2008, 65, 699–711. [Google Scholar] [CrossRef]
- Pagiola, S.; Zhang, W.; Colom, A. Can payments for watershed services help finance biodiversity conservation? A spatial analysis of Highland Guatemala. J. Nat. Resour. Policy Res. 2010, 2, 7–24. [Google Scholar] [CrossRef] [Green Version]
- Landell-Mills, N.; Porras, I.T. Silver Bullet or Fools’ Gold? A Global Review of Markets for Forest Environmental Services and Their Impacton the Poor; Food and Agriculture Organization of the United Nations: Rome, Italy, 2002. [Google Scholar]
- Li, X.; Zuo, T.; Jin, L. Environment and Poverty: The Chinese Practice and International Experiences; Social Sciences Academic Press: Beijing, China, 2005. [Google Scholar]
- State Forestry Administration. New-stage Sloping Land Conversion Program Plan (2014–2020); State Forestry Administration: Beijing, China, 2014.
- Uchida, E.; Xu, J.T.; Rozelle, S. Grain for green: Cost-effectiveness and sustainability of China’s conservation set-aside program. Land Econ. 2005, 81, 247–264. [Google Scholar] [CrossRef]
- State Ministery of Finance. Notice of Enlarging the Scale of New-stage Sloping Land Conversion Program; State Ministery of Finance: Beijing, China, 2015.
- China State Council. Regulation for SLCP; China State Council: Beijing, China, 2002.
- Yao, S.; Li, H. Agricultural productivity changes induced by the sloping land conversion program: An analysis of Wuqi county in the Loess Plateau region. Environ. Manag. 2010, 45, 541–550. [Google Scholar] [CrossRef] [PubMed]
- Zhao, M.J.; Yin, R.S.; Yao, L.Y.; Xu, T. Assessing the impact of China’s sloping land conversion program on household production efficiency under spatial heterogeneity and output diversification. China Agric. Econ. Rev. 2015, 7, 221–239. [Google Scholar] [CrossRef]
- SLCP Administration Office. Statistics of Annual Scales of SLCP in Counties of Yan’an City; SLCP Administration Office: Yan’an, China, 2015. [Google Scholar]
- Lin, Y.; Yao, S. Impact of the sloping land conversion program on rural household income: An integrated estimation. Land Use Policy 2014, 40, 56–63. [Google Scholar] [CrossRef]
- Bullock, A.; King, B. Evaluating China’s slope land conversion program as sustainable management in Tianquan and Wuqi Counties. J. Environ. Manag. 2011, 92, 1916–1922. [Google Scholar] [CrossRef] [PubMed]
- Statistics Burea of Wuqi County. Statistics Report of Economic and Social Development of Wuqi County; Statistics Burea of Wuqi County: Wuqi, China, 2012.
- Kelly, P.; Huo, X. Do farmers or governments make better land conservation choices? Evidence from China’s Sloping Land Conversion Program. J. For. Econ. 2013, 19, 32–60. [Google Scholar] [CrossRef]
- Yang, X.; Xu, J. Program sustainability and the determinants of farmers’ self-predicted post-program land use decisions: Evidence from the sloping land conversion program in China. Environ. Dev. Econ. 2013, 19, 30–47. [Google Scholar] [CrossRef]
- Statistics Burea of Shaanxi Province. Statistics Report of National Economy and Social Development of Shaanxi; Statistics Burea of Shaanxi Province: Xi’an, China, 2014.
- Statistics Burea of Shaanxi Province. Statistics Report of National Economy and Social Development of Shaanxi; Statistics Burea of Shaanxi Province: Xi’an, China, 2013.
- Sen, A. Development as Freedom; Oxford University Press: Oxford, UK, 1999. [Google Scholar]
- Bourguignon, F.; Chakravarty, S.R. The measurement of multidimensional poverty. J. Econ. Inequal. 2003, 1, 25–49. [Google Scholar] [CrossRef]
- Atkinson, A.B. Multidimensional deprivation: Contrasting social welfare and counting approaches. J. Econ. Inequal. 2003, 1, 51–65. [Google Scholar] [CrossRef]
- United Nations Development Programme. The Real Wealth of Nations: Pathways to Human Development; Human Development Report 2010; United Nations Development Programme: New York, NY, USA, 2010. [Google Scholar]
- Statistics Burea of Shaanxi Province. Rural Households’ Income Increase Bring Great Poverty Reduction in Shaanxi Poor Areas; Statistics Burea of Shaanxi Province: Xi’an, China, 2015.
- National Development and Reform Commission. National 13th Five Planning for Resettlement and Anti-poverty Program; National Development and Reform Commission: Beijing, China, 2016.
- Food and Agriculture Organization. The State of Food Insecurity in the World 2014; FAO: Rome, Italy, 2015. [Google Scholar]
- Veen, A.; Gebrehiwot, T. Effect of policy interventions on food security in Tigray, Northern Ethiopia. Ecol. Soc. 2011, 16. [Google Scholar] [CrossRef]
- Xu, Z.; Xu, J.; Deng, X.; Huang, J.; Uchida, E.; Rozelle, S. Grain for green versus grain: Conflict between food security and conservation set-aside in China. World Dev. 2006, 34, 130–148. [Google Scholar] [CrossRef]
- He, J.; Sikor, T. Notions of justice in payments for ecosystem services: Insights from China’s Sloping Land Conversion Program in Yunnan Province. Land Use Policy 2015, 43, 207–216. [Google Scholar] [CrossRef]
Dimension | Indicators | Brief Description |
---|---|---|
I | Income Poverty | I = 1 if Net income per capita is lower than adjusted national poverty line (2600 yuan per year); I = 0 otherwise. |
E | Education Poverty | E = 1 if E1 = 1 or E2 = 1; E = 0 otherwise. |
E1: Education attainment | E1 = 1 if half or more adults in household are illiterate (not completing at least one year of schooling) | |
E2: School attendance | E2 = 1 if at least one school-age child (5–16 in China) is not attending school | |
H | Health Poverty | H = 1 if H1 = 1 or H2 = 1 or H3 = 1; H = 0 otherwise. |
H1: Physical health | H1 = 1 if at least one family member is in bad health. | |
H2: Disability | H2 = 1 if household has at least one mental or physical disability and received the disabled subsidies. | |
H3: Medical consumption | H3 = 1 if household’s medical consumption accounts for the largest part compared to other expenditures (housing, durable goods, schooling, energy, etc.) | |
L | Living Poverty | L = 1 if L1 = 1 or L2 = 1 or L3 = 1; L = 0 otherwise. |
L1: House ownership | L1 = 1 if household does not own the living house | |
L2: House structure | L2 = 1 if the living house is wood-mud constructed | |
L3: House area | L3 = 1 if household’s housing area per capita is smaller than the minimum of security housing (25 m2) | |
F | Food Poverty | F = 1 if F1 = 1 or F2 = 1; F = 0 otherwise. |
F1: Farmland area | F1 = 1 if household’s farmland per capita is smaller than basic line for rural households (0.07 ha). | |
F2: Food consumption | F2 = 1 if household’s consumption for food is less than food demand per capita for balanced nutrition. | |
R | Rights Poverty | R = 1 if R1 = 1 or R2 = 1 or R3 = 1; R = 0 otherwise. |
R1: Public affairs | R1 = 1 if household seldom or less participates in village public affairs | |
R2: Autonomy | R2 = 1 if household has no autonomy to participate in NSLCP | |
R3: Fairness | R3 = 1 if household thinks SLCP distribution is (very) unfair | |
A | Assets Poverty | A = 1 if A1 = 1; A = 0 otherwise. |
A1: Physical Assets | A1 = 1 if household owns 2 or less durable goods | |
DMP | I + E + H + L + F + R + A, the number of human wellbeing dimensions deprived |
Variables | Brief Description | Mean (SD) | Max | Min |
---|---|---|---|---|
1. Dependent variables | ||||
Participation | =1 if household participated in NSLCP, 0 otherwise | 0.35 (0.48) | 0 | 1 |
Participation efforts | Time invested in converted land management (days) | 19.19 (29.74) | 0 | 180 |
2. Main explanatory variables | ||||
IPoverty | =1 if net income per capita ≤2600 yuan, 0 otherwise | 0.18 (0.39) | 0 | 1 |
EPoverty | =1 if more than half members illiterate or at least one child drops out, 0 otherwise | 0.28 (0.45) | 0 | 1 |
HPoverty | Ratio of healthy members (%) | 0.72 (0.33) | 0 | 1 |
FPoverty | Value of grain stock per capita (yuan) | 629.71 (2049.74) | 0 | 12,500 |
RPoverty | Autonomy scores (0, 1, 2, 3, 4, 5) | 1.76 (1.52) | 0 | 5 |
APoverty | Number of durable goods owned | 4.07 (2.11) | 0 | 14 |
LPoverty | Living area per capita (m2) | 32.87 (35.42) | 3.33 | 150 |
DMP | Degree of multidimensional poverty | 2.92 (1.38) | 0 | 7 |
3. Control variables | ||||
Landslope | Land area with slopes over 25 degrees (ha) | 1.24 (1.18) | 0 | 5.33 |
Landquality | Ratio of land area with poor soil quality (%) | 0.29 (0.34) | 0 | 1 |
WillNSLCP | Degree of Willingness to Participate in the NSLCP | 3.75 (1.10) | 1 | 5 |
ElderlyR | Ratio of the elderly (%) | 0.12 | 0 | 1 |
RiskPreference | Risk preference of household (1, 2, 3) | 9.53 (3.48) | 3 | 15 |
Payment Gap | Deviance of payments received from WTA | 0.67 (14.73) | −1 | 4.4 |
Remittance | Annual total amount of remittance from outmigration | 11,326.06 (20181) | 0 | 90,000 |
Publicity | Publicity of SLCP implementation process(1, 2, 3) | 1.34 (1.29) | 0 | 3 |
Irregularity | Number of irregular practices during SLCP in village | 1.20 (1.48) | 0 | 7 |
Training | Number of household members trained | 0.32 (0.69) | 0 | 4 |
Accessloan | Access to loan: 1 = less, 2 = little … 5 = more | 2.03 (1.16) | 0 | 5 |
Tenure | 1 = Converted lands have certificates; 0 otherwise | 0.59 (0.49) | 0 | 1 |
Township | Distance from county administrative center:1 = near; 2 = medium; 3 = far | 1.57 (0.81) | 1 | 3 |
Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | |
---|---|---|---|---|---|
ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | |
EPoverty | −0.15 (0.07) ** | −0.14 (0.08) * | −0.15 (0.09) * | −0.14 (0.08) * | −0.14 (0.08) * |
HPoverty | −0.17 (0.11) | −0.17 (0.12) | −0.20 (0.11) * | −0.18 (0.11) | −0.17 (0.12) |
FPoverty | 0.00 (0.00) * | 0.00 (0.00) * | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) |
RPoverty | 0.05 (0.02) * | 0.05 (0.02) * | 0.05 (0.03) * | 0.05 (0.03) ** | 0.05 (0.02) * |
APoverty | −0.03 (0.02) | −0.03 (0.02) | −0.02 (0.02) | −0.02 (0.02) | −0.02 (0.02) |
LPoverty | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) |
ElderlyR | −0.24 (0.15) | −0.25 (0.15) | −0.28 (0.13) ** | −0.25 (0.13) * | −0.25 (0.13) * |
RiskPreference | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) |
Payment Gap | −0.02 (0.04) | 0.00 (0.04) | 0.00 (0.05) | −0.00 (0.05) | 0.00 (0.05) |
Remittance | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) |
Publicity | 0.05 (0.03) ** | 0.06 (0.03) ** | 0.06 (0.03) ** | 0.07 (0.03) ** | 0.06 (0.03) ** |
Irregular | −0.04 (0.02) | −0.03 (0.02) | −0.03 (0.02) | −0.04 (0.03) | −0.03 (0.02) |
Training | 0.05 (0.05) | 0.05 (0.05) | 0.04 (0.05) | 0.04 (0.05) | 0.04 (0.05) |
Accessloan | −0.02 (0.03) | −0.02 (0.03) | −0.02 (0.03) | −0.02 (0.03) | −0.02 (0.03) |
Tenure | 0.00 (0.07) | 0.00 (0.07) | −0.01 (0.07) | 0.01 (0.07) | 0.00 (0.07) |
Township | 0.02 (0.04) | 0.03 (0.04) | 0.03 (0.04) | 0.03 (0.04) | 0.03 (0.04) |
Landslope | — | 0.08 (0.03) ** | 0.08 (0.03) ** | 0.08 (0.03) *** | 0.08 (0.03) ** |
Landquality | — | −0.03 (0.10) | −0.02 (0.10) | −0.05 (0.10) | −0.03 (0.10) |
WillNSLCP | — | 0.00 (0.03) | 0.01 (0.03) | 0.00 (0.04) | 0.00 (0.03) |
EPoverty*Landslope | — | — | −0.09 (0.07) | — | — |
FPoverty*Landslope | — | — | — | −0.00 (0.00) * | — |
RPoverty*Landslope | — | — | — | — | −0.01 (0.02) |
Log Likelihood | −137.04 | −133.39 | −132.59 | −131.89 | −133.23 |
Pseudo R2 | 0.09 | 0.11 | 0.12 | 0.12 | 0.11 |
Model (6) | Model (7) | Model (8) | Model (9) | Model (10) | Model (11) | |
---|---|---|---|---|---|---|
ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | |
IPoverty | −0.15 (0.08) * | −0.13 (0.08) | −0.14 (0.10) | — | — | — |
DMP | — | — | — | −0.02 (0.02) | −0.01 (0.03) | −0.01 (0.03) |
ElderlyR | −0.21 (0.12) * | −0.22 (0.13) * | −0.22 (0.13) * | −0.21 (0.12) * | −0.22 (0.13) * | −0.21 (0.13) |
RiskPreference | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) | −0.01 (0.01) |
Payment Gap | 0.00 (0.04) | 0.01 (0.04) | 0.01 (0.04) | −0.01 (0.04) | 0.01 (0.04) | 0.01 (0.04) |
Publicity | 0.04 (0.02) | 0.05 (0.03) | 0.05 (0.03) * | 0.04 (0.03) | 0.05 (0.03) * | 0.05 (0.03) * |
Irregular | −0.02 (0.02) | −0.02 (0.02) | −0.02 (0.02) | −0.02 (0.02) | −0.02 (0.02) | −0.02 (0.02) |
Training | 0.07 (0.04) | 0.07 (0.05) | 0.07 (0.05) | 0.07 (0.05) | 0.06 (0.05) | 0.06 (0.05) |
Accessloan | −0.01 (0.03) | −0.01 (0.03) | −0.01 (0.03) | −0.01 (0.03) | −0.01 (0.03) | −0.00 (0.03) |
Tenure | 0.03 (0.07) | 0.03 (0.07) | 0.04 (0.07) | 0.02 (0.07) | 0.03 (0.07) | 0.02 (0.07) |
Township | 0.06 (0.04) | 0.06 (0.04) | 0.06 (0.04) | 0.05 (0.04) | 0.06 (0.04) | 0.06 (0.04) |
Landslope | — | 0.07 (0.03) ** | 0.07 (0.03) ** | — | 0.08 (0.03) ** | 0.07 (0.03) ** |
Landquality | — | −0.01 (0.10) | −0.02 (0.10) | — | −0.03 (0.10) | −0.04 (0.10) |
WillNSLCP | — | 0.02 (0.03) | 0.02 (0.03) | — | 0.02 (0.03) | 0.02 (0.03) |
IPoverty*Landslope | — | — | −0.03 (0.09) | — | — | — |
DMP*Landslope | — | — | — | — | — | 0.04 (0.02) * |
Log Likelihood | −142.43 | −138.62 | −138.57 | −143.83 | −139.63 | −138.14 |
Pseudo R2 | 0.05 | 0.08 | 0.08 | 0.04 | 0.07 | 0.08 |
Model (12) | Model (13) | Model (14) | Model (15) | Model (16) | Model (17) | |
---|---|---|---|---|---|---|
ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | |
EPoverty | −3.24 (4.15) | −2.96 (3.58) | −2.84 (3.59) | −3.18 (5.07) | −3.65 (3.66) | −3.16 (5.09) |
HPoverty | 9.85 (5.09) * | 9.05 (5.11) * | 9.37 (5.20) * | 7.55 (6.36) | 8.52 (5.11) * | 6.27 (6.46) |
FPoverty | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.00 (0.00) | 0.000.00) |
RPoverty | −1.27 (1.34) | −1.67 (1.38) | −1.60 (1.35) | −2.05 (1.52) | −1.74 (1.39) | −1.74 (1.50) |
APoverty | 2.82 (1.72) | 3.25 (1.57) ** | 3.18 (1.54) ** | 2.96 (1.01) *** | 2.90 (1.50) * | 3.35 (1.00) *** |
LPoverty | −0.07 (0.08) | −0.11 (0.08) | −0.12 (0.08) | −0.11 (0.08) | −0.10 (0.08) | −0.06 (0.08) |
ElderlyR | 0.95 (10.46) | 3.03 (10.07) | 2.35 (10.17) | 3.63 (11.33) | 2.35 (10.08) | 0.52 (11.72) |
RiskPreference | −0.50 (0.55) | −0.80 (0.55) | −0.79 (0.55) | −0.74 (0.58) | −0.82 (0.55) | −1.16 (0.62) * |
Payment Gap | 0.50 (2.44) | 1.24 (2.30) | 1.40 (2.27) | 1.69 (2.18) | 0.97 (2.28) | 1.78 (2.20) |
Remittance | −0.00 (0.00) | −0.00 (0.00) | −0.00 (0.00) ** | −0.00 (0.00) ** | −0.00 (0.00) ** | −0.00 (0.00) * |
Publicity | 0.35 (1.50) | 1.62 (1.54) | 1.55 (1.53) | 1.54 (1.61) | 1.47 (1.57) | 1.67 (1.61) |
Irregular | 1.84 (1.86) | 1.92 (1.68) | 1.87 (1.66) | 1.89 (1.50) | 1.96 (1.66) | 1.97 (1.50) |
Training | 0.92 (1.70) | 0.41 (1.83) | 0.08 (1.97) | 0.22 (2.01) | 0.26 (1.80) | 1.39 (2.08) |
Accessloan | 3.19 (1.83) * | 3.45 (1.78) * | 3.54 (1.79) ** | 3.35 (1.64) ** | 3.58 (1.79) ** | 2.84 (1.67) * |
Tenure | 6.09 (3.82) | 7.01 (3.72) * | 6.70 (3.58) * | 7.28 (4.02) * | 7.09 (3.71) * | 5.79 (4.03) |
Township | 0.89 (2.14) | 1.18 (2.23) | 0.88 (2.19) | 1.01 (2.34) | 1.29 (2.22) | 1.15 (2.35) |
Landslope | — | 5.44 (1.89) *** | 5.13 (1.91) *** | 5.92 (1.50) *** | 5.54 (1.85) *** | 4.54 (1.55) *** |
Landquality | — | −6.73 (4.86) | −5.94 (4.90) | −5.27 (5.45) | −6.86 (4.90) | −5.72 (5.41) |
WillNSLCP | — | 0.14 (1.96) | 0.23 (1.96) | 1.12 (1.97) | −0.12 (2.02) | 0.44 (1.92) |
HPoverty*Landslope | — | — | 3.80 (6.21) | — | — | — |
APoverty*Landslope | — | — | — | 1.34 (0.71) * | — | — |
HPoverty*WillNSLCP | — | — | — | — | −7.55 (4.27)* | — |
APoverty*WillNSLCP | — | — | — | — | — | −1.84 (0.95) * |
Log Likelihood | −302.08 | −295.71 | −295.52 | −294.02 | −294.67 | −293.92 |
Pseudo R2 | 0.03 | 0.05 | 0.05 | 0.06 | 0.05 | 0.06 |
Model (18) | Model (19) | Model (20) | Model (21) | Model (22) | Model (23) | Model (24) | Model (25) | |
---|---|---|---|---|---|---|---|---|
ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | ME (SE) | |
IPoverty | −15.16 (6.73) ** | −12.88 (6.19) ** | −12.52 (5.99) ** | −14.79 (8.00) * | — | — | — | — |
DMP | — | — | — | — | −1.94 (0.99) * | −1.91 (0.98) * | −1.56 (0.93) * | −1.24 (0.92) |
ElderlyR | −5.55 (10.98) | −2.25 (10.96) | −2.04 (10.91) | −2.54 (11.03) | −4.92 (10.03) | −1.83 (10.21) | 6.06 (11.79) | −1.78 (10.00) |
RiskPreference | 0.03 (0.53) | −0.12 (0.50) | −0.10 (0.51) | −0.16 (0.51) | 0.14 (0.53) | −0.07 (0.49) | 0.08 (0.49) | 0.03 (0.49) |
Payment Gap | −0.70 (1.97) | −0.40 (1.95) | −0.43 (1.97) | −0.44 (1.95) | −0.58 (2.17) | −0.24 (2.11) | −0.10 (2.13) | −0.64 (2.26) |
Publicity | 1.15 (1.21) | 2.20 (1.25) * | 2.27 (1.26) * | 2.28 (1.21) * | 1.16 (1.15) | 2.11 (1.21) * | 1.80 (1.20) | 1.88 (1.24) |
Irregular | 0.76 (1.63) | 0.37 (1.68) | 0.38 (1.67) | 0.28 (1.70) | 1.57 (1.66) | 0.98 (1.68) | 0.98 (1.66) | 0.79 (1.68) |
Training | 1.77 (1.67) | 1.46 (1.68) | 1.41 (1.65) | 1.37 (1.62) | 1.53 (1.51) | 1.29 (1.52) | 1.37 (1.53) | 1.10 (1.48) |
Accessloan | 2.23 (1.61) | 2.34 (1.62) | 2.45 (1.65) | 2.23 (1.59) | 2.32 (1.51) | 2.54 (1.56) | 1.80 (1.58) | 2.50 (1.51) * |
Tenure | 4.60 (3.70) | 5.55 (3.67) | 5.81 (3.79) | 5.13 (3.73) | 4.65 (3.68) | 5.64 (3.67) | 5.86 (3.67) | 5.19 (3.69) |
Township | 0.06 (1.66) | 0.03 (1.77) | −0.08 (1.72) | −0.14 (1.74) | −0.29 (1.63) | −0.16 (1.78) | −0.16 (1.82) | 0.19 (1.80) |
Landslope | — | 4.18 (1.76) ** | 4.04 (1.78) ** | 4.17 (1.73) ** | — | 4.53 (1.72) *** | 4.39 (1.75) ** | 4.59 (1.73) ** |
Landquality | — | −8.42 (5.09) * | −8.27 (5.07) | −8.78 (1.73) * | — | −7.83 (5.11) | −7.08 (5.07) | −8.10 (5.16) |
WillNSLCP | — | −1.02 (2.41) | −1.04 (2.40) | −0.48 (2.71) | — | −1.76 (2.49) | −1.61 (2.52) | −2.27 (2.62) |
IPoverty*Landslope | — | — | −1.98 (4.73) | — | — | — | — | — |
IPoverty*WillNSLCP | — | — | — | 3.86 (9.35) | — | — | — | — |
DMP*Landslope | — | — | — | — | — | — | −1.15 (0.81) | — |
DMP*WillNSLCP | — | — | — | — | — | — | −1.49 (0.87) * | |
Log Likelihood | −306.16 | −302.23 | −302.15 | −302.15 | −307.75 | −303.13 | −302.52 | −302.52 |
Pseudo R2 | 0.02 | 0.03 | 0.03 | 0.03 | 0.01 | 0.03 | 0.03 | 0.03 |
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Share and Cite
Ren, L.; Li, J.; Li, C.; Li, S.; Daily, G.C. Does Poverty Matter in Payment for Ecosystem Services Program? Participation in the New Stage Sloping Land Conversion Program. Sustainability 2018, 10, 1888. https://doi.org/10.3390/su10061888
Ren L, Li J, Li C, Li S, Daily GC. Does Poverty Matter in Payment for Ecosystem Services Program? Participation in the New Stage Sloping Land Conversion Program. Sustainability. 2018; 10(6):1888. https://doi.org/10.3390/su10061888
Chicago/Turabian StyleRen, Linjing, Jie Li, Cong Li, Shuzhuo Li, and Gretchen C. Daily. 2018. "Does Poverty Matter in Payment for Ecosystem Services Program? Participation in the New Stage Sloping Land Conversion Program" Sustainability 10, no. 6: 1888. https://doi.org/10.3390/su10061888