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China’s Sloping Land Conversion Program: Does Expansion Equal Success?

  • Chinese University of Hong Kong Shenzhen
  • Forest Trends

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This paper uses a 2003 household survey to examine implementation and impacts of China’s Sloping Land Conversion Program. We find that land targeting has been strongly influenced by program goals, but that mistargeting also occurred. Using a treatment effects approach to evaluate program impact, we find evidence of positive impact on cropping, husbandry, and total income, though the results are not robust enough to support government claims of huge gains. We also find evidence that lack of participant choice could be dampening program impacts, and that allowing households autonomy could lead to improvement in program cost-effectiveness and outcomes.
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Jintao Xu is a professor at the Department of Environmental Sciences, Peking University; his e-mail is Ran Tao is a senior research fellow at the Center for Chinese Agricultural Policy (CCAP),
Chinese Academy of Sciences, and the Shaw Research Fellow at the Institute for Chinese Studies, University of
Oxford; his e-mail is Zhigang Xu is a senior research fellow at CCAP; his e-mail is Michael T. Bennett is a post-doctoral researcher at the Department of Environmental
Sciences, Peking University; his e-mail is This research was funded by the
World Bank and the Ford Foundation for the Taskforce on Forests and Grasslands, China Council for
International Cooperation on Environment and Development. The authors wish to thank Uma Lele and Susan
Shen of the World Bank, Guofang Shen of the Chinese Academy of Engineering, Hein Mallee of the Ford
Foundation, Scott Rozelle and Emi Uchida of UC-Davis, and Yazhen Gong and Jikun Huang of CCAP for their
help in designing and implementing the survey. The authors also wish to thank the editor and three referees for
their valuable comments on an earlier draft of the paper. All findings, opinions and mistakes are the sole
responsibility of the authors.
China’s Sloping Land Conversion Program:
Does Expansion Equal Success?
Jintao Xu, Ran Tao, Zhigang Xu, and Michael T. Bennett
With a total budget of RMB 337 billion (over US$40 billion), the Sloping Land Conversion Program
(SLCP) is the largest land retirement program in the developing world. Also known as Grain for
Green, SLCP has the goal of converting 14.67 million hectares of fragile cropland (and a “soft”
target of an equal area of wasteland) to forests or grasslands by program completion in 2010. As
such, pending successful completion, the program could have significant implications for China’s
forests and remaining natural ecosystems, representing a 10%-20% increase in current national forest
area, and roughly a 10% decrease in China’s cultivated area. To date, the program has already
reached almost half of its goals. However, based on concern that fast-paced expansion during the full
implementation phase (2002+) and significant problems already observed in program design and
execution threatens SLCP’s long-term sustainability, this paper uses a 2003 household and
village-level survey to evaluate program implementation and impact on participant income. We find
that though targeting has been strongly influenced by program goals, too many low-sloping plots have
been enrolled into the program. The survey data also reveals other significant problems in
implementation (lack of participant choice, shortfalls in subsidies delivered), and little evidence –
contrary to government claims – of a significantly positive impact on participant income. We conclude
by arguing that, at minimum, SLCP expansion should be halted so that design and implementation
can be properly evaluated and monitored. More fundamentally, we question the underlying logic
behind the program, especially given its large-scale campaign-style approach aimed at providing a
final solution towards some of rural China’s most serious environmental problems.
Keywords: Sloping Land Conversion Program, Payments for Environmental Services Schemes,
Afforestation, China.
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In 1999, China initiated the most ambitious land conversion/afforestation program in the developing
world. With a total budget of RMB 337 billion (over US$40 billion), the Sloping Land Conversion
Program (SLCP) has the stated environmental goals of reducing water and soil erosion, increasing
China’s forest cover, and helping farmers to shift to a more sustainable structure of agricultural
production. Under SLCP, the State Forestry Administration plans to convert 14.67 million hectares
of fragile cropland to forests (4.4 million of which will be land with slopes of 25o or above), and has
an additional “soft” target of afforesting an equal area of wasteland (SFA, 2003). As such, pending
successful completion, the program could have significant implications for China’s forests and
remaining natural ecosystems, representing a 10%-20% increase in current national forest area, and
roughly a 10% decrease in China’s cultivated area (Hyde et al., 2002; ZGTJNJ, 2001).1 To
compensate farmers for crop income foregone due to participation, the government has provided
grain, cash and free seedlings as the program subsidy package. At present, SLCP involves the
participation of millions of rural households across a wide area containing huge ecological and
economic heterogeneity. Around RMB 50 billion (over US$6 billion) has been spent so far, and 7.2
million hectares of cropland have already been retired by the end of 2003 (SFA, 2003; Xu and
others, 2001).
This paper is motivated by worries that underlying these optimistic numbers (SLCP has already
reached almost half of its goal) is the possibility that the program is in danger of falling
significantly short of its goals. The particular concern of this paper is the fast rate of expansion in
recent years, and the claims made by the government to justify this expansion. More than 80% of
total retired area by the end of 2003 was enrolled in just the first two years of full implementation
(2002-2003). Added to this, a growing body of evidence points to significant problems in design
and implementation, suggesting that fast expansion, in absence of careful monitoring and evaluation,
1 This is comparable to the other large land set-aside program in the world, the US Conservation Reserve Program (CRP), which in
2000 had enrolled some 13.56 million ha, or nearly 10% of cropland in the US (Heimlich, 2003).
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might in reality be damaging SLCP’s long-term sustainability (Xu, Tao and Xu, 2004; Xu and
others, 2006; Xu and others, 2004). Despite such evidence, however, the central government
continued to push expansion in 2004, basing this on claims that the program has already
significantly improved rural livelihoods and environmental conditions, and has argued that it is a
key policy for restoring China’s degraded ecosystems (e.g. China Green Times, 2003, 2004 and
2005). Given SLCP’s sheer size and its important environmental goals, this paper thus examines
whether evidence exists to support these claims.
We use data from a 2003 household and village-level survey conducted by the Center for Chinese
Agricultural Policy, Chinese Academy of Sciences, to evaluate program implementation and impact
on participant income during the pilot phase. Overall, we find that systematic problems in
implementation indeed exist, and that the program has so far failed to achieve the great success
claimed of it. These results, we argue, raise questions about the underlying motivations and
assumptions behind fast-paced expansion of SLCP. The remainder of the paper is organized as
follows. Sections I and II present program background and the details of the political economy
underlying its alarming rate of expansion in recent years. Sections III, IV and V then present the
results of analysis of program implementation and impact on participant income. Section VI
discusses the implications of these results.
Stricken by a historic 267-day Yellow River dry-out in 1997, and the 1998 Yangtze River floods that
caused significant economic damage and loss of life, the Chinese government claimed that it is
necessary to take immediate action to alleviate water and soil erosion (Xu and others, 2001). In the
aftermath of the floods, experts generally agreed that high rates of deforestation and the consequent
increase in rates of soil erosion in the upper reaches of the Yangtze River Basin exacerbated, if not
G4G_3rdworld_final.doc 3
precipitated the disaster (World Bank, 2001; World Wildlife Fund, 2003). In general, soil erosion is
one of China’s most pressing environmental problems (Huang, 2000). An estimated 2 billion tons of
silt is released into the Yangtze and the Yellow River annually, with 65% of this coming from
sloping cropland. Data suggests that west China, with 70% of the approximately 6.07 million ha of
agricultural land with slopes greater than 25o, contributes the majority of this (Xu and others, 2001).
The central government thus initiated the Sloping Land Conversion Program (SLCP) in 1999 with
particular emphasis on west China.
SLCP is an important departure from China’s other water and soil conservation and forestry
programs. It is one of the first, and certainly the most ambitious, “payment for environmental
services” programs in China. Most other large national forestry programs, such as the Natural
Forest Protection Program (initiated in 1998) and the Northeast, North and Northwest China Green
Belt Program (initiated in the late 1970s) are directly implemented by either state-owned forest
enterprises or by local forest authorities. In contrast, SLCP uses a public payment scheme that
directly engages millions of rural households as core agents of project implementation. As such,
central to the program’s long-term success is its incentive-compatibility for participating farmers.
Program subsidies need to more than offset farmers’ current opportunity costs to ensure that
participation is attractive. Beyond the subsidy period, the program either needs to have effectively
induced farmers to shift to other, sufficiently lucrative activities, or the forests and pastures planted
as part of program participation need to provide farmers with sufficient income to minimize
incentives to return converted land to cultivation. In recognition of these challenges, the central
government has clearly stated that the program aims not only to conserve soil and water in China’s
ecologically fragile areas, but also to restructure the rural economy so that participating farmers can
gradually shift into more ecologically and economically sustainable activities such as husbandry,
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forestry, and off-farm work (SFA, 2003). Thus, an implicit assumption behind the program is that
retiring marginal land from cultivation is a prerequisite for rural economic restructuring.
The program stipulates that farmers who convert degraded and highly sloping cropland back to
either “ecological forests” (defined by the State Forestry Administration as timber-producing
forests), “economic forests” (orchards, or plantations of trees with medicinal value) or grassland
will be compensated with 1) an annual in-kind subsidy of grain, 2) a cash subsidy, and 3) free
seedlings, provided to the farmer at the beginning of the planting period. To account for differences
in regional average yields, the annual grain subsidy has been set at 2250 kg/ha in the Yangtze River
Basin, and 1500 kg/ha in the Yellow River Basin. The cash subsidy is RMB 300/ha of eligible land
(US$37/ha) per year. Both grain and cash subsidies are for 8 years if ecological forests are planted
and for 5 years or 2 years if economic forests or grasses are planted, respectively (SFA, 2003).2
These payments are quite generous, even by international standards. For example, in monetary
terms compensation per hectare in the Yellow River and Yangtze River basins has been around 2
times and 3 times, respectively, average rental payments of the US Conservation Reserve Program
(Heimlich, 2003).3
Finally, SLCP is most notable for its sheer size. Under the program, the State Forestry
Administration plans to convert around 14.67 million hectares of fragile cropland to forest by 2010
(4.4 million of which is estimated to be on land with slopes of 25o or above) and also has a “soft”
target of afforesting an equal area of wasteland (SFA, 2003; WWF, 2003).4 The central government
poured RMB 7.68 billion (US$930 million) in grain and cash subsidies into the program during the
2 An additional component of the program is the provision of free seedlings for afforestation of denuded mountainous areas. See
footnote 4.
3 These calculations are based on the 8-year subsidy period for China and the CRP’s 10-year subsidy period. Heimlich (2003)
estimates average rental payments of the CRP in 2000 to be US$45.62/acre/year. Grain was monetized at the program’s subsidy grain
purchase price of RMB 1.4 /kg. The exchange rate used for this comparison is RMB 8.28 per US dollar.
4 Inclusion of the goal of “greening” of denuded mountainous areas in SLCP does not represent a new initiative, but rather a
restatement of a longstanding policy in China.
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three-year pilot period alone, and by the end of 2003 total accumulated government expenditures
approached RMB 50 billion (over US$6 billion), around 68% of which has been for grain
subsidies.5 Fifteen million farmers have entered the program in just the first five years, and leaders
have estimated that upon completion it will affect 40-60 million rural households (Uchida, Xu and
Rozelle, 2005; Xu and others, 2006). Zhang and others (Forthcoming) finds in a survey of
investment projects during 1998-2003 in 2459 sample villages across 6 provinces in China that
SLCP is the third most common project being implemented, behind road and bridge, and irrigation
investments. If the program is to be completed as the SFA has planned, total program expenditures
will reach RMB 337 billion (over US$40 billion).6 In comparison, 13.56 million hectares of
cropland was enrolled in the US Conservation Reserve Program in 2000, with estimated outlays in
2001 of US$1.7 billion (Heimlich, 2003).
Though the sheer scale and budget of SLCP are encouraging signals of the Chinese government’s
growing commitment towards the environment, they do not guarantee success. In fact, a growing
body of evidence suggests that significant problems in program design and implementation threaten
SLCP’s long-term goals (Tao, Xu and Xu, 2004; Xu and Cao, 2001; Zuo, 2001; Xu and others,
2004). This paper, in particular, is motivated by a trend in program implementation that Chinese
policymakers might construe as evidence of SLCP’s success: its fast rate of expansion. As detailed
in Figure 1 below, during the pilot phase (1999 to 2001) an average of 402,000 hectares of cropland
was enrolled into SLCP annually. According to internal government reports, upon full
implementation the rate of enrollment increased more than sixfold, averaging almost 3 million
hectares of cropland converted per year during 2002-2003. By the end of the pilot phase, 1.2 million
hectares of cropland and 0.47 million hectares of barren land had been converted, and SLCP was
5 Authors’ calculations from SFA (2003).
6 This includes a subsidy period to 2017.
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being implemented in 400 counties across 20 provinces (Xu and others, 2001; Uchida, Xu and
Rozelle, 2005). Just two years after this, at the end of 2003, fully 7.2 million hectares of cropland
had been enrolled and 4.92 million hectares of barren land afforested, and the program
encompassed more than 2000 counties in 25 provinces.7
Though on paper the program has already reached half of its goals in the first five years of
implementation, in reality this is alarming given the significant problems in implementation
observed during the pilot phase. A key worry is that the program places undue burden and cost on
local governments, which in turn could be causing problems in program administration observed
during the pilot phase such as low survival rates of planted trees, insufficient delivery of
compensation to farmers, lack of respect for the principals of volunteerism, and difficulties in
targeting and monitoring (Zuo, 2001; Xu and Cao, 2001; Xu and others, 2004). It has only been
since 2002, in fact, that the central government has allocated administrative fees to provincial
governments for SLCP implementation, and these still appear to be insufficient.8 Fast expansion
has thus created even greater administrative needs that have potentially exacerbated shortfalls in
required funds, thus leading to problems in implementation and subsidy delivery.9
<Figure 1>
Despite such evidence, however, the central government has pushed expansion and touted SLCP as
a huge success. It claims that the program has already significantly improved rural livelihoods and
environmental conditions, and that it is a key policy for restoring China’s degraded ecosystems (e.g.
7 This is according to internal government reports.
8 The 2003 SLCP plan in fact stipulates that local governments are to be responsible for their own implementation costs (SFA,
9 In a township in a key project county in Shaanxi Province, for example, half of the participating plots were not inspected and
compensated on time. In another township of the same county, many participating plots had yet to be inspected even three years after
they had entered SLCP; though the county government recruited 30 additional staff to deal with these problems, manpower has still
been far short of that required to inspect some 67,000 ha of converted land.
G4G_3rdworld_final.doc 7
China Green Times, 2003, 2004 and 2005). An important backdrop to this is China’s grain policy,
which during the 1990s involved large-scaled grain procurements at above-market prices and a
subsequent failed attempt at recentralizing grain markets. This resulted, by 1999, in a State Grain
Bureau burdened by severe financial stress and stocks of aging and unsold grain estimated to be
larger than China’s annual production (Lu, 1999; Lu, 1998; Zhong, 2001). Given this, it has thus
been clear that behind SLCP’s high grain subsidy standard and fast expansion is the additional goal
of aiding the State Grain Bureau. Program grain has been purchased from state stocks at RMB
0.4/kg above market prices, which by the end of 2003 has resulted in a 24.55 million ton
draw-down of stocks and an implicit RMB 9.8 billion subsidy to the Bureau.10
Central authorities seem to believe that SLCP is an important and needed program to address rural
China’s severe levels of environmental degradation. However, the program’s added benefits to the
ailing state grain sector appear to have blinded them, in their enthusiasm to expand SLCP, to the
above-mentioned problems. Since an important justification behind fast expansion has been claims
made about the success of the pilot phase, the remainder of this paper will show that results from a
2003 survey provide no substantive evidence supporting government contentions that SLCP is a
huge success. In fact, the results of the analysis point to problems in implementation, subsidy
delivery and targeting of land for enrollment, and suggest that SLCP could potentially be having a
negative impact on the income of participant households.
To evaluate SLCP implementation and impact, the Center for Chinese Agricultural Policy (CCAP),
Chinese Academy of Sciences, conducted a household and village-level survey in 2003 in the three
provinces where SLCP was first implemented, located at the middle and upper reaches of the
10 Authors’ calculations based on SFA (2003).
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Yellow River Basin and the upper reaches of the Yangtze River Basin: Shaanxi, Gansu and Sichuan.
Two counties per province, three townships per county, two participating villages per township, and
ten households per village were randomly selected, for a total of 36 village surveys and 360
household surveys. The survey provides a comprehensive and comparatively long window into
program implementation, since both the household and village surveys collected detailed
information for 1999 and 2002 regarding both general characteristics as well as SLCP
implementation. It is thus, to our knowledge, the best available data set for evaluating SLCP.
One troubling discovery of the survey is the predominantly top-down approach towards
implementation that has been taken in the sample villages. This was evident from informal
discussions with households and local officials during fieldwork, as well as in the significant
portion of households that reported that they had little or no choice of whether or not to participate.
As detailed in Table 1 below, only around 53% of surveyed households felt that they could choose
to participate (61.7% of the participants and only 25.9% of non-participants).11 This ranges from
65.8% of households in Shaanxi, to 45.5% in Sichuan, to only 31% in Gansu Province. From
fieldwork, we believe that these responses can be taken at face value; households were aware of the
details of the program and cognizant of their choices. For example, respondents reported lower
levels of choice for aspects of program implementation requiring technical expertise, and therefore
more likely the purview of program officials; only 36% of participant households said they could
choose what kinds of trees to plant on their enrolled land, and only 34.5% and 29.9% of participant
households felt that they could choose which areas and which plots, respectively, to retire.12
Survey results also give evidence that lack of autonomy is, in part, the results of systematic
11 These numbers do not change significantly when controlling for eligibility in terms of having sloping land. Fully 88% of the
sample has land with slope > 15o, and 76.5% has land with slope > 25o.
12 At least one participant household in each village indicated that they had been asked their opinion about project design prior to
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differences in local implementation. Village share of households reporting that they have autonomy
of choice, compared between villages in the same township, has a correlation coefficient of 0.7,
significant at 1%. Results from binomial logit analysis of household autonomy status also support
this. Two models using, alternately, township and county fixed effects, and conditioning on 1999
household and village characteristics, both find that household characteristics are statistically
insignificant.13 At the same time, the marginal effects for 8 out of the 17 township indicators and 3
out of 5 county indicators are significant at 5% or better.14 These indicators, furthermore, have
large marginal effects: the average absolute value of impact on probability is 48%. These results
suggest that households in the sample have had unequal access to the program, and in some cases
have been forced to participate when they would otherwise have not.
<Table 1>
Lack of household autonomy in participation choice runs counter to the program’s explicitly stated
principal of volunteerism (SFA, 2003). More importantly, use of market-based voluntary
mechanisms of participation is key to the efficiency gains promised by payment for environmental
services (PES) programs over traditional command-and-control approaches (Pagiola et al., 2002). In
the case of SLCP, since no bidding mechanism exists to optimally match payer benefits with
participant costs, participation should, at minimum, be voluntary. This could improve cost
effectiveness by ensuring that households with the lowest opportunity costs participate, and would
minimize the possibility that program participation is having negative welfare effects on some
participants. The survey data, in fact, provides some evidence that this second, adverse outcome has
already occurred.
13 The number of years a village has been implementing the program was also statistically insignificant in all models, providing
evidence that household responses are not reflecting lack of information about the program.
14 Household characteristics included are household head age and years of education, household population, labor, per capita
income, per capita land, non-agricultural share of income and labor, and whether or not the household head is a party member.
Village characteristics are village population, average per capita income, average per capita agricultural land, share of village
population engaged in non-farm work, village leader and secretary age and years of education, number of years the village has
implemented SLCP, share of village land with slope >15o, whether or not the village leader or village secretary worked before in a
forestry department, and number of villagers working in county-level forestry departments. Village characteristics were only
significant in the model with county indicators; these were household population (-), share of village population in off-farm work (+),
and whether or not the village secretary worked before in a forestry department (-).
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Table 2 below compares the 1999 (pre-SLCP) net income per hectare of retired plots, used as a
rough measure of plot opportunity cost, with the real value of SLCP compensation standards.15 In
general, the SLCP standard was below pre-SLCP net income of enrolled plots in a number of cases.
This does not indicate problematic implementation per se; one year’s observation of net income is
an imperfect measure of plot opportunity cost, and poorer households might prefer a low guaranteed
subsidy over a high but highly variable expected plot income. In fact, average per ha disparity
between subsidy standard and 1999 net income from retired plots is not significantly different
between the enrolled plots of autonomous and non-autonomous households.
At the same time, however, households in the sample had on average 43% of their cropland enrolled
in the program, 19% had more than 70% of their land enrolled, and about 4% (10 households) had
100% of their land enrolled. This suggests that participants could be exposed to greater production
risk post-program if other sources of income are insufficient to supplant forgone cropping income
due to participation. Moreover, the subsidy standard is still below 1999 net income of retired plots
for a number of non-autonomous households. This group makes up 26% of the Gansu Province
sample, in fact. For these households, 1999 net income from retired plots was, in total, RMB 8,503
larger than the SLCP subsidy standard, equaling on a household basis about 5% of average total
1999 household net income. In Sichuan, 9% of sample participants are non-autonomous households
that are net-losers, totaling a loss of RMB 7386, or roughly 15% of average total 1999 household
net income. This suggests that these households would not have willingly participated had they
been given a choice.
<Table 2>
Even more troubling are the findings, detailed in Table 3 below, that subsidies actually received by
participants in the sample generally fell short of SLCP compensation standards. Non-autonomous
15 This is calculated as the cash standard plus the monetized grain subsidy standard.
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households received on average only 46% of their owed subsidies in 2002, compared with the
average 62% received by autonomous households, with this difference significant at 1%. In terms of
the cash subsidy alone, non-autonomous households received on average only 21%, compared with
34% for autonomous households, with this difference significant at 5%. This suggests, at minimum,
that significant problems in implementation exist. There were two main reasons for such shortfalls.
The first is that local governments, in program implementation, have retained some subsidies to
make up for expenditure shortfalls and tax arrears.16 The second is that program expansion had
been so fast that local government agencies responsible for program supervision have not had
sufficient manpower to check whether the converted land satisfies government requirements (such
as tree types and survival rates). Therefore, delivery of compensation was delayed in many
regions.17 Though it is worth examining whether these factors are related to the fact that
non-autonomous households in the sample appear to be carrying the brunt of these shortfalls,
analysis of this is beyond the scope of this paper. It is a fact, however, that many farmers have not
received the full amount of subsidies owed them.
<Table 3>
Overall, the State Forestry Administration and provincial and sub-provincial forestry departments
have been primarily responsible for targeting general areas of land for enrollment in SLCP, as well
as in setting and distributing enrollment quotas to local governments (Zuo, 2002).18 This top-down
approach raises the question of whether participant welfare is being adequately considered when
choosing land to enroll, and whether political/institutional factors unrelated to participant welfare,
16 This is related to recent rural tax reforms that deprived local governments of the power to levy various taxes and fees on farmers
and also according to the SLCP plan, local governments were no longer able to levy agricultural taxes and fees on the retired land.
Consequent high local government budget deficits combined with serious rural tax incompliance in many regions have thus created
incentives for governments to expand their SLCP enrollment quotas so as to increase inflows of subsidies, a portion of which they
can then retain for program costs and tax arrears (Tao, Xu and Xu, 2004).
17 We observed in our survey in the three pilot provinces, and in visits to other provinces (e.g. Hunan and Hebei) that retention of
SLCP subsidy funds by local governments is prevalent. In many regions, the cash subsidies never reached participating farmers.
Again, this was related to the huge fiscal pressures local governments faced after the rural tax reform and the fact that no agricultural
tax could be levied on retired land so that local governments in SLCP areas lost a significant share of their revenue and had to resort
to retention of upper level transfers such as SLCP subsidy.
18 In practice, bargaining between the central and the local governments on the land conversion quota has always been a part of the
program. Given that subsidies is in most cases higher than forgone income of cultivation and need to go through the hands of local
implementing agencies and local governments, such agencies usually overreached the land retirement quota set by the center to
bargain for more subsidies.
G4G_3rdworld_final.doc 12
environmental or economic conditions could be influencing this choice. Shortfalls in subsidies
actually received suggest that this could be a concern. Fundamentally, to evaluate implementation it
is important to look both at the targeting of land for enrollment, as well as program impact on
participant income, since it is only by helping participants shift to other sufficiently lucrative and
sustainable income-generating activities that program goals will be achievable in the long run. We
examine this in the following sections.
The survey finds evidence of significant mistargeting of plots for retirement in terms of the SLCP’s
stated target of highly sloping land. Low-sloping plots (with slope < 15o) were enrolled in the
program in 26 of the sample villages, comprising on average 21% of total sample retired land. On
average 71% of this land in each village (100% in 17 villages) could have been replaced with
unenrolled highly sloping land (slope > 25o) in the sample. This indicates that considerations other
than plot slope have been important in the enrollment choice in these villages. Plot quality and
opportunity cost for the household is likely an important factor, and is not necessarily directly
associated with slope.19 In villages that enrolled low-sloping land, an average 37.4% of it was
described as “low quality” by the household, and 36.4% was affected by a disaster in 1999.20 The
program’s other goal of poverty alleviation also raises the possibility that enrollment targeting
might also be influenced by household characteristics independent of plot traits. The transaction
costs of program implementation and the political economy of the village, where considerations of
equity likely come into play when deciding who gets program subsidies, could also play a part, as
could upper-level pressure to enroll land as well as rent-seeking behavior by local governments.
19 In the sample, the correlation coefficients between whether a plot is highly sloping (slope > 25o), and two measures of plot
quality – 1999 per ha net income from the plot, and whether or not the household considers the plot to be “high quality” – are -0.19
and -0.33, respectively, both significant at 0.1%.
20 This includes drought, flood, windstorm, hail or pest infestations.
G4G_3rdworld_final.doc 13
To examine these issues, we model enrollment of plot i in SLCP by the end of 2002 as the outcome
of a latent choice process,
iiiiii ε dz z x VVHH ++++=
that is a function of 1999 plot characteristics (xi), the 1999 characteristics of the household (ziH) and
village (ziV) associated with the plot, as well as provincial and township indicators (di) and other
unobserved aspects of the choice (εi). Whether or not plot i is enrolled in SLCP is thus the observed
outcome of this process whereby,
0. . if SLCPin enrollednot
0. if SLCPin enrolled
is plot
Assuming that the εi are distributed i.i.d. logistic, the probability that plot i is enrolled in SLCP is,
iP d z z xΛ SLCPin enrolled is Plot VVHH +++=
where Λ(.) denotes the logistic cdf.
After data cleaning, 345 households were selected for the analysis, with a total of 2004 plots. Table
4 below details the characteristics of these sample plots. Overall, 27% of the sample plots were
enrolled in SLCP by 2002, ranging from 48% in Shaanxi Province, to 20.9% in Sichuan and 18% in
Gansu. Plot characteristics included in the model are pre-SLCP (1999) plot slope, size, land quality,
irrigation conditions, land rights held by the household over the plot, and plot accessibility.
Generally speaking, the Shaanxi and Sichuan samples have a large share of highly sloping plots and
land that is low quality, whereas the Gansu sample has a much higher share of low-sloping,
high-quality plots. The Shaanxi plots are also, on average, almost twice as large as those in Gansu
and Sichuan, and were much more often affected by a disaster in 1999.
<Table 4>
Table 5 below details the characteristics of the households and villages in the sample. Program
G4G_3rdworld_final.doc 14
implementation generally began earliest in Shaanxi Province (where 67% of the sample villages
started SLCP in 1999) followed by Sichuan (where 83% of the sample villages started in 2000) and
then Gansu (where 50% of the sample villages did not start until 2001 or 2002). To examine the
influence of household characteristics on plot choice, the model includes as explanatory variables
household population, household head age and years of education, per capita income and per capita
land, household labor, the share of income from off-farm sources, and the share of labor engaged in
part-time or full-time off-farm work. Households in Shaanxi Province are generally the poorest in
the sample (1999 per capita income of RMB 991) and have the highest share of steeply sloping land
(72%). At the same time, they also have the highest per capita land area and lowest degree of land
fragmentation.21 In comparison, Sichuan and Gansu households are richer (RMB 1,435 and RMB
1,566 per capita, respectively), have lower per capita land and a greater degree of land
fragmentation. Gansu households also have a relatively low average share of land that is steeply
sloped (31%).
To control for and examine the impacts of heterogeneity in local conditions, model explanatory
variables also include village 1999 per capita income, per capita land, and share of village
population in off-farm wage work.22 Villages in Shaanxi have lower population density, but depend
more heavily on agriculture for their livelihood, as reflected in their low average number of rural
enterprises in the village (0.25) and low average share of village population engaged in non-farm
wage work (11%).23 Gansu villages have on average 0.33 rural enterprises and fully 23% of village
population engaged in non-farm wage work in 1999, and Sichuan villages have 1.75 rural
enterprises and 15% of village population engaged in non-farm wage work. Villages in Shaanxi are
also generally smaller, with the average 1999 population being 510, as compared with 1,177 and
21 Taking into account regional differences in land quality, Shaanxi households likely have low effective per capita land endowment.
22 This includes part- or full-time off-farm day work, both in and out of the village, as well as work that involves leaving the village
without returning for at least a week.
23 This is defined as work that involves leaving the village for a week or more.
G4G_3rdworld_final.doc 15
684 for Gansu and Sichuan, respectively.
The effects of lag time and program transactions costs are captured with the number of years the
village has been implementing SLCP, and village population, since implementation and monitoring
likely take more time in larger villages. The model also includes share of village agricultural land
with slopes greater than 15o to instrument for SLCP quotas distributed to villages, since though
quota determination involves a degree of negotiation between local governments and forestry
officials, local geographic conditions are an important baseline determinant. Finally, we include
variables to control for institutional heterogeneity that could influence program implementation.
These include whether or not the village leader and whether or not the village secretary worked
before at a forestry department, the number of villagers that work in the county forestry department,
and village leader and village secretary age and years of education.
<Table 5>
To gain insight into the influence that households versus local governments have in the plot
enrollment choice, the model is estimated on the full sample as well as the subsamples of
‘autonomous’ and ‘non-autonomous’ households. In addition, two different forms of the model are
estimated to examine the degree to which the systematic regional variation in plot and household
characteristics could be picking up other regional effects unrelated to plot traits. The first model
characterizes targeting as a direct function of plot, household and village characteristics, while the
second model includes interaction terms between provincial dummies and Plot Size, 1999
Income/Ha, Land Quality, Distance to Nearest Gully or Ditch, HH per capita income and HH per
capita land.24 Model marginal effects and significance levels are presented in Table 6 below.25
Model results provide evidence that while plot targeting has been strongly influenced by program
24 These interactions, as well as the provincial and township fixed effects, are not reported in the table.
25 The results use robust standard errors, clustered at the household level.
G4G_3rdworld_final.doc 16
goals, other factors have also been important. Overall, highly sloping, low-quality plots that are the
least accessible to households are much more likely to be enrolled in SLCP. That a plot has a slope
greater than 25o increases probability of enrollment by 11% to 27%, and if it is irrigated with
surface water, its probability of enrollment is reduced at the margin by 5% to 13%. Whether or not a
plot was affected by a disaster in 1999 is even more important, since this increases the probability
of enrollment by fully 34% to 48%. Larger plots and plots with shorter-term, more flexible
household property right types (i.e. either “responsibility,” “ration”, or “contract” land) are also
much more likely to be retired, suggesting that transaction costs are being minimized in
implementation.26 The significant and negative effect of distance to nearest road in four of the six
models also suggests this, since plots close to roads are easier to monitor.27
<Table 6>
Though we are troubled by the lack of household autonomy in participation choice seen in the
sample, it is encouraging to see that household characteristics are statistically insignificant in the
non-autonomous subsample, since this suggests that selection of households into SLCP for this
group has been based primarily on land characteristics.28 That said, comparison between the
autonomous and non-autonomous subsamples is revealing, and indicates that when households have
greater decision-making power in implementation they favor retirement of plots with lower
opportunity costs. This can be seen in the indicator for low land quality (the household’s subjective
evaluation), distance to home, the irrigation condition variables, and the land rights variables, which
26 Land in most villages can be divided into two types: private plots (ziliu di, around 6%) and collectively controlled land (jiti di,
more than 90% percent). In most villages, leaders do not intervene into decisions on private plots and farmers enjoy a fairly high
degree of security. Collectively controlled land includes three different tenure forms: ration land (kouliang tian), which goes to
farmers mainly to meet household subsistence requirements with no tax obligations; responsibility land (zeren tian), which goes to
farmers on the condition that farmers deliver a low-priced grain or cotton quota to the state; and contract land (chengbao tian), which
is auctioned off or allocated by village leaders for a fee (Rozelle, et al, 2002, Liu et al 1998).
27 However, this could also be viewed as supporting anecdotal evidence found in other case studies of one aspect of potentially
non-optimal implementation, whereby local leaders target plots close to roads in order to “showcase” implementation for higher-level
officials (Zuo, 2001; Xu and Cao, 2001).
28 This interpretation is strengthened by evidence that land distribution within villages in China in terms of effective per capita land
is generally based on considerations of equity, suggesting that correlation between plot characteristics and household characteristics
within villages is low (Rozelle et al., 2002; Kung, 1995).
G4G_3rdworld_final.doc 17
are generally more often significant and larger in magnitude for the autonomous subsample overall,
and especially in comparison to the non-autonomous subsample.
In the non-autonomous subsample, for example, land rights do not appear to play any significant
role in enrollment targeting. From the perspective of the household, however, the degree to which
rights over a plot are more stable and longer-term has direct bearing on its position in household
input and investment choices.29 Similarly, the effect of a plot’s distance to home, a clear indicator
of its opportunity cost to the household, is highly significant and ten times larger for the
autonomous than for the non-autonomous households. In sum, these results are important; they
suggest that increasing household autonomy in participation choice could improve program cost
effectiveness by improving the likelihood that – pending eligibility – those plots of least-cost for
households will be chosen.
Central to the realization of SLCP’s long-term goals is whether or not it is adequately
incentive-compatible for participants. Most immediately, program subsidies need to at minimum
offset each participant’s opportunity cost of the enrolled land during the subsidization period.
Beyond that, the economic gain to farmers from the timber forests, orchards or pastures planted –
and from other activities engaged in – as a result of SLCP participation needs to be large enough by
the end of the subsidy period to ensure that participants do not return enrolled land back to
cultivation. Post-program land use decisions of participating farmers, in fact, have been one of the
biggest concerns in conservation set-aside programs elsewhere (Cooper and Osborn, 1998).
Though official reports and news in government publications on SLCP implementation, progress
29 For more evidence, please refer to Li and others (1998), Carter and Yao (1998) and Rozelle and others (2002).
G4G_3rdworld_final.doc 18
and socio-economic impact are abundant (e.g. SFA, 2004; China Green Times, 2003, 2004, 2005),
rigorous analyses are rare. Not surprisingly, government reports all claim that SLCP has had a
significant positive impact on program areas. However, we doubt the validity of these official
statements due in part to questions about the quality of the survey data used, since it has been
gathered via the government reporting system and thus contains substantial bias in favor of program
implementation agencies. The only rigorous analysis of program impact to date is Uchida and
others (2006), which uses propensity scoring matching to evaluate the social and economic impacts
of the program. Overall, they find evidence of a significant negative impact on cropping income, a
significant positive impact on husbandry income and inventories, and a significant positive impact
on productive and housing assets. At the same time, however, impact on total household per capita
income is estimated to be small and statistically insignificant. Overall, Uchida and others (2006)
interprets these results optimistically due to evidence of improvement in household assets.
Table 7 below presents 1999 and 2002 components of total income for participant and
non-participant households, by province.30 These numbers suggest that SLCP has indeed induced a
restructuring of agricultural production, whereby participants have shifted relatively more of their
inputs out of cropping and into husbandry. In Shaanxi Province, growth rates for cropping income
were 35% for non-participants compared with only 12% for participants (including subsidies
received). In Gansu, these were -26% and -32%, respectively, and in Sichuan cropping income
declined by 30% for both groups. Conversely, growth rates for husbandry were higher for
participants than for non-participants. In Shaanxi, average household per capita husbandry income
for participants increased more than tenfold, compared to only 175% for non-participants. In Gansu,
participants’ husbandry income grew by 1744%, compared with only 586% for non-participants,
30 Cropping income consists of total crop production valued at average village market price, net of materials and hired labor costs.
Husbandry income includes both sales income and own consumption, valued at market prices. Off-farm income includes all
non-agricultural production activities, comprised mainly of sideline activities and wage labor income. Income from sideline activities
is net of production costs and other business-related expenditures, while wage income includes both cash and in-kind income, valued
at market prices. Other income consists of aquaculture, rental and interest income, gifts, pension income and government subsidies
and transfer payments. The SLCP subsidy is calculated as the subsidy received by the household for 2002.
G4G_3rdworld_final.doc 19
and in Sichuan these numbers are 845% and 514%, respectively. Differences between participants
and non-participants in change of total income are less systematic across regions. In Shaanxi, total
income (including subsidies received) increased by 41% and 42% for participants and
non-participants, respectively. For Gansu these numbers are 2.3% and 12%, respectively, and for
Sichuan they are 26% and 17%, respectively.
<Table 7>
Since such numbers could be the result of factors unrelated to SLCP implementation, we use a
first-differences model explaining change in household per capita net income between 2002 and
1999 to more rigorously estimate program impact on income. A simple regression specification for
explaining change in income is,
Δ++Δ+Δ+++=Δ γd x x VVHH
where Δyik denotes the change in farm household i’s per capita net income component k between
1999 and 2002, progi = 1 if household i is a participant (i.e. is in the treatment group), ΔxiH and
ΔxiV are vectors of changes in household and village-level characteristics, respectively, between the
1999 and 2002, di is a vector of provincial and county-specific time trend dummies, and Δμi is the
difference in idiosyncratic disturbances across periods. We include in one specification the number
of years household i has been in SLCP before the end of 2002 (pyrsi) to control for lagged program
impacts not picked up in the first difference. Household characteristics included are 1999 per capita
income, 1999 per capita number of plots affected by disaster, and changes in household population
and labor share of population.31 Village characteristics are included to instrument for changes in
village per capita income not related to SLCP implementation. These include change in village
population, in the number of village enterprises, in the number of daily long-distance buses that run
through the village, in the share of village households that have a telephone and in the share that
31 Neither variables capturing household per capita land nor household program enrolled area were included due to concerns that
participation effects would be confounded with impacts of changing per-capita land. Relatively little substantive change in model
results and significance levels were found when these variables were included.
G4G_3rdworld_final.doc 20
have piped water.
Of interest for program evaluation is δ, generally referred to as the difference in differences (DD)
estimator. This captures the difference, controlling for household and village-level factors, in
average income change between participants and non-participants that can be attributed to program
participation.32 A central concern in the program evaluation literature is the impact of selection bias
on estimates of program impact. If selection bias is an issue, then the outcomes of non-participants
cannot be used to estimate the counterfactual outcomes for participants were they not to have
participated. In the case of our survey, we believe we can in large part control for the effects of
selection bias due, ironically, to the predominantly top-down approach towards implementation
observed in the survey. This gives us a subsample of households that have not self-selected to be
participants or non-participants, allowing for empirical examination of selection bias in the sample.
Our baseline for comparison is the full sample of households. Model estimates for the subsample of
non-autonomous households are presented for comparison, as well as to serve as our best estimate
of program impact were households to be “randomly” selected for treatment. Though we argue that
no self-selection bias exists in the non-autonomous subsample, another potentially important source
of selection bias might come into play via the selection of certain types of individuals into the
program by government officials and village leaders.
We test this by comparing 1999 household characteristics between participants and non-participants
in the full sample, and in the two subsamples of autonomous and non-autonomous households. We
generally find in all three groups no statistically significant differences between the household
characteristics of participants and non-participants, including characteristics likely to be the focus of
32 Key for OLS parameter estimates for model (3) to be consistent is that E(ΔμΔx) = 0 (where Δx represents the vector of all
explanatory variables in (3)), and that E(ΔxΔx) is full rank (Wooldridge, 2002).
G4G_3rdworld_final.doc 21
selection, such as share of land that is highly sloping. The one important exception to this is already
controlled for in the model: per capita number of plots affected by disaster in 1999, which is
significantly larger for participants than for non-participants in all three groups.33 The results of the
land targeting analysis regarding household characteristics also support this conclusion. This
suggests that if any additional selection bias exists, it comes into play through unobserved variables
not correlated with model explanatory variables. Unfortunately, we have no basis to evaluate the
degree to which such bias exists and cannot rule out the possibility that model results for the
non-autonomous subsample still contain bias.
Table 8 below presents the results from the models. Note that estimates for the full sample are, in
most cases, larger than for the non-autonomous subsample, providing evidence that selection bias is
inflating the full-sample estimates of program impact.34 Overall, these results provide evidence that
the program is indeed inducing participants to shift their structure of agricultural production (i.e.
towards husbandry and away from cropping). At the same time, however, they provide mixed
results regarding program impact on total income. To begin with, impact on cropping income is
significant in all specifications. Using as a basis for comparison the average 2.8 and 2.6 years that
full-sample and non-autonomous subsample households were in SLCP, respectively, results from
the model controlling for program lag indicate that participation has induced a reduction in
per-capita net cropping income (without subsidy) of RMB 94 based on the non-autonomous
household sample and RMB 92 based on the full sample. For the model with no lag, the reduction is
RMB 87 and RMB 81, respectively. Change in husbandry income associated with participation is
weakly significant in three of the four specifications. The model with a program lag effect estimates
33 This is found to be 0.26-0.32 larger for participants, significant at 0.1%. Total household land is also found to be significantly
different, tending to be 0.24-0.27 ha larger for participant households, significant at between 1% and 5%, depending on the
subsample. However, since differences in per-capita household land and total household population are insignificant, we do not
believe that this latter is a concern.
34 We have ruled out the existence in the sample of reporting bias that is systematically related to household autonomy, wherein
non-autonomous participants report inflated estimates of their 1999 income due to dissatisfaction with the program. As mentioned,
we find no statistically significant difference in household per capita income between autonomous and non-autonomous households.
Added to this, we also find no statistically significant difference between the 1999 (i.e. pre-program) per ha net income of enrolled
plots between these two groups.
G4G_3rdworld_final.doc 22
that the SLCP has induced an increase in husbandry income of RMB 130 using the non-autonomous
sample, and RMB 186 using the full sample. The model with no lag estimates an increase of RMB
129 and RMB 194, respectively.
<Table 8>
Estimates of program impact on total income are statistically insignificant. As our best estimate of
program impact, however, the results are informative. The model with program lag effects estimates
that SLCP participation has induced an average decrease in total household per capita net income of
RMB 69 based on the non-autonomous sample, and an increase of RMB 35 based on the full
sample. In the model with no program lag effect these estimates are a decrease of RMB 60 and an
increase of RMB 57, respectively. The lack of statistical significance might simply be because it is
yet too early for impact on total income to have clearly emerged. The program could also be having
systematically different impacts on different subgroups in the sample – beneficial in some cases,
harmful in others – resulting in large standard errors for the estimates.35 This would not be
surprising given the large diversity of economic, natural and institutional conditions encompassed
by the sample, as with the program in general.
Overall, however, these results provide little evidence supporting the government’s contention that
SLCP has had a large, positive impact on participant income. Taken at face value, estimates based
on the non-autonomous subsample would suggest the opposite. This is not implausible based on the
survey fieldwork observations. Farmers in the sample reported they had to spend considerable time
planting and caring for trees on enrolled land so as to guarantee that they received subsidies. This
has meant that an additional cost of program participation has been the opportunity cost of labor
spent managing retired land, in this case in terms of foregone crop income on remaining plots or
other activities. Though statistically insignificant, the estimated negative program impact on
35 A version of the model was examined that interacted program participation with disaster-affected number of plots and with total
income. Program impact on total income remained statistically insignificant in this model, as were the interactions.
G4G_3rdworld_final.doc 23
off-farm income in all models is also suggestive of this. In light of this, foregone income on retired
plots might indeed be a reasonable estimate of total opportunity cost to farmers.
Furthermore, though production risk is likely a significant concern for households in the sample,
participation in SLCP might simply be replacing one type of risk for another. Specifically, program
stipulations requiring a minimal survival rate of trees or grass planted on retired land in order for
subsidies to be delivered, combined with the uncertainties and lag time involved with
implementation and inspection by village and higher level authorities, suggest that participation
itself entails a form of non-negligible income risk.36 Given this, it is very possible that participants
are adjusting their consumption, savings and investment choices to cope with this risk, resulting in
reduced program impacts on income. It is also possible that lack of household choice is reducing
program impact on income. Households, as the core agents engaged by the government to
implement SLCP, are in the best position to know whether or not program participation will benefit
them. Lack of choice could thus be increasing the likelihood that households not well positioned to
benefit from SLCP, or whose income prospects would suffer as the result of participation, are being
selected into the program.
The aim of this paper has been, in part, to demonstrate that government claims about the success of
the pilot phase of the Sloping Land Conversion Program have been, at minimum, inflated. Though
the representativeness of the data set used for this analysis is open to debate in light of the huge
number of local governments and rural households participating in SLCP, it is nonetheless the best
36 Our survey tells us that in only 5 of the 18 sample townships were survival rates consistently above the government standard
(70% in Gansu and Shaanxi, 85% in Sichuan). In Li county in Sichuan, survival rates were below the standard in all but one
inspection in one township, and this have been declining in recent years. According to farmers surveyed, survival rates were even
below 40% in many places and significant replanting has had to be done. Pervasive replanting has lead to shortfalls in government
seedling subsidies of around 50 Yuan per mu according to our survey. Moreover, in many regions even replanting cannot guarantee
survival rates due to lack of water, and in many cases is simply done to pass government inspections so as to obtain program
subsidies (Du and Xu, 2003).
G4G_3rdworld_final.doc 24
available to evaluate the program’s implementation over a relatively long period. Overall, our
results fail to support the government’s rosy picture of program implementation and outcome. Our
targeting analysis, while indicating that land enrollment choice has indeed been strongly influenced
by program goals, also points to significant mistargeting of flatland for retirement. The income
impact analysis, furthermore, does not provide evidence to support the government’s claims that
SLCP has had a large positive impact on participant income. Income impact analysis results, in fact,
suggest that significant disregard for the program’s principals of volunteerism could be reducing its
effectiveness. It might, of course, still be too early for the impact of program participation to have
emerged, and the lack of statistical significance of program impact on total income could also be
due to significant local and regional differences in program impact. However, given that claims
made by the government about the huge success of the pilot phase have served to justify the
break-neck speed of expansion seen during full implementation, these results are important.
Most troubling is the finding that, though on paper a payment for environmental services scheme,
SLCP appears to be in practice just another in a long line of top-down, campaign-style programs
implemented by the Chinese central government. The importance of farm households as the key
long-term actors in implementation makes participant willingness and choice necessary conditions
to program success. Otherwise, in absence of significant gains in the development of sources of
non-cropping income, participants who originally did not wish to participate, or who are not being
adequately compensated for their opportunity costs of participation, will simply return land to
cultivation upon subsidy period end. And results from the survey indicate that this is not a small
share of participants.37
Survey evidence, in fact, suggests that farmers could face significant added income risk due to
37 Bennett (Forthcoming) calculates from the survey data that only about 20.8% of participant households (representing 23.7% of
retired area) indicated that they would allow program-planted trees to reach maturity. Another 36.7% of participant households
(representing 30.5% of retired area) said that they could maintain their livelihood with revenue from current activities.
G4G_3rdworld_final.doc 25
program participation. For participants in the sample, per capita cropland decreased by 43% on
average overall, and by 57% for households in Shaanxi Province in particular (from 0.24 ha in 1999
to only 0.09 ha in 2002). Considering the adverse climate conditions and frequent natural disasters
in this region, reducing both household per capita land area and the number of plots that households
have distributed across local micro-climates could significantly impact their ability to hedge against
significant production risk in absence of alternative sources of income.38 The stability of such
alternative sources of income, furthermore, is questionable. First of all, the future value and
shorter-term income generating capacity from timber forests (i.e. “ecological forests”) and orchards
(i.e. “economic forests”) planted under SLCP does not look promising. For timber forests, this is
due to low timber forest survival and growth rates in many regions due to lack of rainfall and
unsuitable conditions for timber trees (especially in the arid northwest), the uncertainties in the
future of China’s forest sector reforms, and the potential oversupply of timber due to large-scale
plantations in the south. Regarding economic forests, the fast expansion in SLCP has led to many
different regions in China planting similar orchard crops, raising concerns about future oversupply
and dampened economic value of these forests.39
Overall, SLCP, a program implemented in a period of grain surplus, was expanded too fast as a
result of the interplay between the central government’s underlying goal of reducing State Grain
Bureau grain stocks, and local government interest in increasing subsidy inflows. Lack of
successful experiences in the past and excessively fast program expansion has created significant
fiscal risk for governments, income risks for participants and ecological risks for program covered
areas.40 In fact, due to such risks, the government significantly slowed the rate of expansion in
38 Fieldwork in Shaanxi and Gansu found that one “normal” year of production can be extremely important for a household’s
longer-term livelihood and ability to hedge against future production shocks, since it is usually followed by 2-3 years of drought.
39 Evidence from 2005 fieldwork of the same sample villages suggests that such effects are already evident. Interviews with farmers
and officials in Yanchuan County in Shaanxi Province, for example, found that the price of Jujube fruit, an orchard crop (a.k.a.
“economic forest” plantation) widely planted in the region under SLCP, dropped from RMB 14/kg in 1997 to RMB 3/kg in 2004.
40 Most of the SLCP covered area in northwestern China is arid or semi-arid land suitable only for grass or shrub plantation. Timber
tree plantation would indeed hamper the water conservation function of soil or even lead to further land desertification because
G4G_3rdworld_final.doc 26
2005, and is currently discussing how to scale back the program. This is in line with the findings of
this paper. Though the government’s growing largesse towards environmental initiatives is
encouraging, large-scale campaign-style programs are not the way to reverse adverse environmental
outcomes stemming from a complex combination of factors. Only by positioning SLCP within an
array of policies focused at the rural economy can the duel goals of environmental amelioration and
poverty alleviation be realized.
timber trees need much more water than grass or shrubs. The low survival rates of timber trees has been very apparent in China’s
other ecological programs implemented in early periods, such as the Northeast, North and Northwest China Green Belt Program. One
example is Mingqin County, Gansu Province, where the area of government afforestation was as high as 87,000 hectare but only
20,000 hectare survived (Jiang, 2003).
G4G_3rdworld_final.doc 27
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Environment and Development, Western China Forests and Grassland Task Force). China
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Xu, Jintao, and Yiying Cao. 2001. “The Socioeconomic Impacts and Sustainability of the SLCP.” In:
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Xu, Zhigang, Jintao Xu, Xiangzheng Deng, Jikun Huang, Emi Uchida, and Scott Rozelle. 2006.
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China.” World Development 34(1): 130-148.
ZGTJNJ [China Statistical Yearbook]. 2001. Beijing: China Statistical Press.
Zhong, Funing. 2001. “The Political Economy of the Chinese Grain Marketing System.” Technical
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G4G_3rdworld_final.doc 29
Figure 1. SLCP Total Converted Area, 1999-2003 (Million Ha)
Implemented in
Sichuan, Shaanxi and
Gansu provinces.
Expanded to a total
of 188 counties in
13 provinces.
Expanded to
a total of 20 provinces,
400 counties and
27,000 villages.
Expanded to a total of
1897 counties across
25 provinces.
Expanded to more than
2000 counties in 25
SOURCE: Xu and others (2004b), Uchida, Xu and Rozelle (2005), Internal Chinese Government
ALL Shaanxi
(n=103) Gansu
(n=85) Sichuan
Were the villagers asked their opinion about the project
and how it could be best designed prior to the time that
the project was implemented? 42.8% 41.7% 41.2% 46.1%
When your village began SLCP, did your household have
autonomy to choose whether or not to participate? 61.7% 72.8% 43.5% 67.1%
Did you have autonomy in choosing
the types of trees to plant? 36.0% 47.6% 34.1% 22.4%
Did you have autonomy in choosing
which areas to retire? 34.5% 53.4% 15.3% 30.3%
Did you have autonomy in choosing
which plots to retire? 29.9% 40.8% 12.9% 34.2%
ALL Shaanxi
Could you participate in SLCP if you wanted to? 25.9% 45.5% 29.4% 16.7%
Table 1. Farmer autonomy in SLCP participation (n=345)
Source: Adapted from Table 5, Xu and others (2004) with a different subsample.
G4G_3rdworld_final.doc 30
All w/o
Autonomy All w/o
Autonomy All w/o
Number of Households 7 0 7 96 28 68 103 28 75
1999 Average Net Income from Enrolled Land
4833 - 4833 181 173 186 507 173 673
Total Converted Land Area (Ha) 5.13 - 5.13 68.11 24.28 43.83 73.24 24.28 48.95
Average Difference b/w SLCP Standard &
1999 Net Income (RMB/Ha)
-3,033 -3,033 1,619 1,627 1,614 1,293 1,627 1,127
Share of Subsidy Beyond 1999 Net Income
-1.68 - -1.68 0.90 0.90 0.90 0.72 0.90 0.63
Number of Households 42 22 20 43 26 17 85 48 37
1999 Average Net Income from Enrolled Land
3485 3946 3085 940 974 895 2026 2102 1944
Total Converted Land Area (Ha) 8.52 3.96 4.56 11.44 6.48 4.96 19.97 10.44 9.53
1999 Net Income
-1,685 -2,146 -1,285 860 826 905 -226 -302 -144
Share of Subsidy Beyond 1999 Net Income
-0.94 -1.19 -0.71 0.48 0.46 0.50 -0.13 -0.17 -0.08
Number of Households 23 7 16 53 18 35 76 25 51
1999 Average Net Income from Enrolled Land
5371 5918 5143 1031 785 1157 2457 2319 2523
Total Converted Land Area (Ha) 7.47 2.19 5.28 15.26 5.14 10.12 22.73 7.34 15.40
1999 Net Income
-2,821 -3,368 -2,593 1,519 1,765 1,393 93 231 27
Share of Subsidy Beyond 1999 Net Income
-1.11 -1.32 -1.02 0.60 0.69 0.55 0.04 0.09 0.01
SHAANXI (n = 103)
Table 2. Participant 1999 Net Income from Enrolled Land Versus SLCP Compensation Standards
All Participants
Source: 2003 Survey Data
This is the difference between the SLCP standard and 1999 net income , as a share of the SLCP standard. Thus, for example, 0.90 means that 90% of th e
subsidy standard compensates a household beyond 1999 net income.  Subsidy grain was converted to cash based on the national market price of RMB 1/kg.
GANSU (n = 85)
SICHUAN (n = 76)
Net Losing Households Net Gaining Households
Yanshuiguan 506 1299 391 25 0 29 1269 501 1380
Majiahe 466 238 545 59 38 66 1276 1524 1189
Yuju 94 23 193 8 13 0 1698 1763 1607
Yanxia 1074 863 1122 112 0 137 614 937 542
Jianling 1500 1500 1500 48 0 71 252 300 229
Chigen 1471 1500 1468 78 96 76 251 204 257
Zhiping 574 517 638 104 114 94 1122 1169 1068
Gangou 957 639 1179 137 94 167 707 1067 454
Lingzhi 1170 1100 1217 201 198 203 429 502 380
Zhangzigou 499 602 0 86 83 100 1215 1114 1700
Tiezhai 0 0 0 5 0 56 1795 1800 1744
Hexi 588 270 1131 36 14 74 1176 1516 595
Datan 1849 2250 1408 87 117 54 614 183 1088
Zhongzi 2050 2020 2122 0 0 0 500 530 428
Shahe 2177 2128 2250 39 50 24 334 372 276
Shangmeng 2160 2250 2150 107 0 118 284 300 282
Puxi 2250 2250 2250 231 300 225 69 0 75
Guergou 618 618 50 - 50 1882 1882
- 856 705 1343
70 49 111 1021 1177 1602
Table 3. Average Shortfalls in Grain and Cash Subsidy in Surveyed Areas, 2002
Grain (kg / ha)
Actual Delivery
Total Shortfall
Actual Delivery
1500 300
Source: 2003 SLCP Survey Data. + This is a sum of corn, wheat, white and paddy rice, and wheat flour subsidies. Both white rice and wheat flour were converted to unhusked weight
equivalents at a factor of 1:1.4. ++ This values grain at the national pric of RMB 1/kg.
Cash (RMB / ha)
Standard SLCP
SHAANXI (n=103)
SICHUAN (n=76) GANSU (n=85)
G4G_3rdworld_final.doc 31
0.15 (0.17) 0.22 (0.21) 0.12 (0.11) 0.12 (0.18)
4,473 (10260) 2,312 (6445) 3,830 (6647) 6,935 (14628)
Nearest road (Km) 0.76 (1.34) 0.82 (1.18) 0.82 (1.65) 0.65 (1.03)
Nearest gully / ditch (Km) 1.03 (2.19) 1.44 (2.85) 1.07 (2.24) 0.66 (1.25)
Home (Km) 0.88 (1.08) 0.97 (0.87) 0.91 (1.11) 0.76 (1.17)
Share of plots …
…enrolled in SLCP.
> 25
< 15
No irrigation.
Private land.
Responsibility or ration land.
Contract land.
1999 Net Income/Ha (RMB)
Plot size (Ha)
47.9% 17.9%
This is defined as an exogenous negative production shock, incl uding drought, flood, severe insect infestation, windst orm and hail. This includes paddy and
terraced fields, which comprised 5.8% of the plot s in the sample. This includes developed wasteland, and land transferred into or out of the household.
Plot Characteristics
...affected by a disaster in 1999.
Table 4. Sample Plot Characteristics, 1999
84.6% 96.6%
4.4% 3.8% 1.6% 8.1%
70.6% 35.8%
29.1% 34.3% 26.8% 27.4%
G4G_3rdworld_final.doc 32
44.3 (11.4) 44.4 (10.6) 44.9 (11.7) 43.7 (11.9)
4.67 (3.4) 5.53 (3.3) 4.57 (3.7) 3.89 (3.1)
4.81 (1.6) 4.75 (1.7) 5.11 (1.6) 4.56 (1.4)
1,330 (1212) 991 (1053) 1,435 (1236) 1,566 (1271)
0.39 (0.39) 0.40 (0.46) 0.44 (0.32) 0.32 (0.36)
0.19 (0.141) 0.23 (0.128) 0.16 (0.115) 0.18 (0.170)
3.51 (1.32) 3.67 (1.59) 3.56 (1.23) 3.29 (1.06)
0.34 (0.26) 0.30 (0.26) 0.39 (0.26) 0.34 (0.25)
5.81 (2.23) 4.91 (1.83) 6.34 (2.24) 6.15 (2.32)
Share of agricultural land with slope > 15
0.57 (0.357) 0.72 (0.297) 0.31 (0.299) 0.68 (0.318)
790.5 (586.2) 510.2 (221.8) 1,177.2 (797.1) 684.2 (384.0)
661 (366) 535 (221) 672 (251) 776 (532)
0.15 (0.107) 0.19 (0.156) 0.15 (0.090) 0.12 (0.025)
0.16 (0.112) 0.11 (0.101) 0.23 (0.073) 0.15 (0.126)
0.13 (0.099) 0.06 (0.054) 0.20 (0.080) 0.11 (0.103)
0.78 (1.569) 0.25 (0.622) 0.33 (1.155) 1.75 (2.137)
7.28 (2.7) 7.75 (2.8) 6.67 (3.2) 7.42 (2.3)
7.29 (3.6) 8.46 (3.7) 7.08 (3.9) 6.33 (3.2)
40.00 (7.4) 41.92 (6.7) 40.75 (8.2) 37.33 (7.0)
45.08 (8.3) 43.50 (6.7) 47.58 (7.8) 44.17 (10.1)
2000 (0.96) 1999 (0.67) 2000 (1.24) 2000 (0.43)
0.58 (0.318) 0.81 (0.170) 0.24 (0.203) 0.69 (0.229)
...village leaders…
...village secretaries…
0.19 (0.62) 0.33 (0.89) 0.25 (0.62)
Number of rural enterprises
Share of village agricultural land with slope > 15
This is the calculated using the number of HH laborers working part-time or fulltime off-farm, and so is not mutually exclusive with agricultural labor.  This includes
migrant labor and day workers working both outside and within the village.
Year village began SLCP.
% of sample participating in SLCP
Age of household head
Household head years of education
Household population
Migrant labor as share of village population
Village average per capita cropland (Ha)
Number of Plots
Household per capita income (RMB)
Non-ag share of household per capita income
Household per capita arable land (Ha)
Household labor
Table 5. Household and Village Characterstics, 1999
Household Characteristics
Village Characteristics
(n=114) (n=119) (n=112)
(n=12) (n=12)
Share of village population working in non-farm wage work
(n = 345)
(n = 36) (n=12)
Non-agricultural share of household labor
Village average per capita income (RMB)
Village population
% of participants with sloping land (slope > 15
% of non-participants with sloping land (slope > 15
90.2% 96.1%
97.1% 76.5%
Institutional Factors
# of villagers working in the county forestry department
Village Leader Age
Village Leader Years of Education
Village Secretary Age
Village Secretary Years of Education
…that worked before in
a forestry department
% of … 8.3%
16.7% 33.3%
G4G_3rdworld_final.doc 33
0.313 **** 0.444 **** 0.137 *** 0.337 *** 0.704 *** 0.079 *
-0.000 *** -0.000 *** -0.000 -0.000 -0.000 *-0.000 *
0.459 **** 0.449 **** 0.359 **** 0.479 **** 0.460 **** 0.341 ****
0.247 **** 0.274 **** 0.123 **** 0.246 **** 0.267 **** 0.105 ***
0.091 ** 0.082 0.060 *0.095 ** 0.089 0.045
-0.034 -0.062 -0.004 -0.079 * -0.059 -0.023
0.053 ** 0.127 *** 0.000 -0.040 0.022 -0.022
Responsibility or ration land 0.109 *** 0.163 ** 0.023 0.102 ** 0.149 ** 0.026
Contract land 0.201 *0.274 *0.021 0.184 * 0.241 0.077
Other -0.062 -0.194 **** -0.012 -0.057 -0.196 **** 0.003
Surface-water. -0.129 **** -0.125 *-0.064 **** -0.134 **** -0.135 ** -0.051 ****
Groundwater. -0.067 -0.198 **** 0.073 -0.079 -0.212 **** 0.028
Other. 0.037 -0.170 ** 0.172 0.023 -0.187 *** 0.067
Nearest road (Km) -0.014 *-0.043 ** -0.001 -0.018 ** -0.049 ** 0.001
Nearest gully or ditch (Km)
0.014 *** 0.019 *** 0.003 0.009 0.020 -0.003
To home (Km) 0.041 **** 0.095 **** 0.009 *0.041 **** 0.094 **** 0.006
-0.001 -0.001 0.000 -0.001 -0.001 0.000
-0.006 *-0.014 ** 0.000 -0.006 -0.012 * 0.000
-0.013 -0.008 -0.001 -0.015 -0.016 0.003
0.000 ** 0.000 *** 0.000 0.000 0.000 0.000
-0.067 ** -0.051 -0.028 -0.075 ** -0.045 -0.022
-0.166 -0.198 -0.012 -0.328 * -0.488 0.051
0.010 -0.004 0.004 0.009 0.002 0.002
0.075 * 0.054 0.018 0.078 * 0.070 0.028
0.000 0.000 0.000 0.000 0.000 0.000
0.001 **** 0.001 **** 0.000 ** 0.001 **** 0.001 *** 0.001 **
0.865 ** 0.553 0.960 *** 0.945 ** 0.387 1.153 ***
1.178 **** 0.778 1.349 *** 1.033 *** 0.577 1.570 ***
0.015 ** 0.010 -0.001 0.015 ** 0.006 -0.003
0.006 -0.011 0.055 *** 0.002 -0.014 0.061 ***
0.010 *0.017 *-0.016 *** 0.012 ** 0.020 ** -0.017 ***
0.007 *0.004 0.020 *** 0.006 0.002 0.023 ***
0.149 **** 0.175 ** -0.008 0.139 **** 0.148 * -0.003
0.294 ** 0.434 0.069 0.307 * 0.520 0.179
0.052 0.098 -0.038 0.046 0.021 -0.017
0.384 **** 0.296 0.592 *** 0.314 *** 0.164 0.902 ****
0.010 0.102 -0.189 *** 0.013 0.124 -0.209 ***
Levels Levels w/ Regional Interactions
Full Sample
Full Sample
Land Rights
Land Quality is "Low"
Plot Size (Ha)
1999 Income/Ha (RMB)
Plot affected by a disaster in 1999 (1 = yes)
Land Quality is "High"
Slope 15
HH Labor
Non-agricultural share of Labor
HH head age
HH head years of education
HH population
HH per capita income (RMB)
Village secretary worked before at a forestry department (1=yes)
Village leader years of education
Village secretary years of education
Village leader age
Village secretary age.
Institutional Factors
1999 Plot Characteristics
Number of Years village has been implementing SLCP
Share of village agricultural land with slope > 15
Village leader worked before at a forestry department (1=yes)
Village population
Village per capita income (RMB)
Village per capita agricultural land (Ha)
Share of village population in non-farm wage work
Non-ag share of HH per capita income
HH per capita land (Ha)
Table 6. Plot Enrollment in SLCP by the End of 2002, Binomial Logit Marginal Effects
Pseudo R-Squared
% Correctly Predicted 0.386
84.6% 0.399
82.0% 0.419
# of villagers working in the county-level forestry department
Explanatory Variables
Significant at * 10%, ** 5%, *** 1% , **** 0.1%. Statistical results are based on robust standa rd errors clustered at the household level. / Provincial and T ownship dummies were used in the model, but
are not reported. Marginal eff ects for 0,1 variables are for a discrete change from 0 to 1.  To examine the affects of regional dif ferences in natural and economic conditions, Plot Size, Inco me/Ha,
Land Quality, Distance to Nearest Gully or Ditch, HH per capita income and HH per capita land were interacted with provincial dummies. The marginal effects of these interaction termsare not reported
above. This is the calculated using th e number of HH laborers working part-time or f ulltime off-farm.
85.6% 0.413
1999 Household Characteristics
1999 Village Characteristics
G4G_3rdworld_final.doc 34
Without Subsidy 940 (777) 1335 (930) 986 (1077) 1325 (1874)
With Subsidy Received 1394 (1877)
465 (521) 626 (429) 420 (672) 401 (622)
470 (628)
Husbandry 6 (23) 17 (63) 18 (78) 208 (916)
Off-Farm 388 (623) 590 (947) 401 (554) 525 (680)
Other 82 (233) 101 (234) 147 (686) 191 (826)
Without Subsidy 1803 (1681) 2021 (1741) 1287 (980) 1287 (942)
With Subsidy Received 1317 (942)
484 (350) 360 (246) 589 (523) 370 (320)
399 (345)
Husbandry 17 (53) 119 (220) 6(30) 113 (222)
Off-Farm 1192 (1570) 1346 (1624) 633 (679) 681 (647)
Other 110 (515) 196 (541) 59 (204) 124 (393)
Without Subsidy 1419 (1425) 1654 (1271) 1635 (1195) 1961 (1524)
With Subsidy Received 2067 (1514)
721 (938) 506 (633) 829 (931) 472 (590)
577 (583)
Husbandry 33 (42) 202 (200) 49 (75) 459 (1187)
Off-Farm 543 (953) 714 (987) 674 (897) 869 (971)
Other 122 (295) 232 (476) 83 (251) 161 (375)
Non-Participant Households Participating Households
1999 2002 1999 2002
Mean (Std Dev)
Cropping Without Subsidy
Cropping With Subsidy Received
Cropping Without Subsidy
Cropping With Subsidy Received
Cropping Without Subsidy
-- --
Cropping With Subsidy Received
-- --
Table 7. Per Capita Net Income of Participant and Non-Participant Households, 1999 and 2002
TOTAL ----
Source: Table 6, Xu and others (2004).
All units are in 1999 RMB, adjusted using the Rural Consumer Price Index.
Income Component
-- --
-- --
-- --
G4G_3rdworld_final.doc 35
before subsidy 37.86 (58.35) -81.11 (36.12)** 41.25 (73.72) -87.30 (41.83)**
with subsidy 18.81 (56.49) -128.42 (35.19)*** 2.92 (70.06) -135.57 (39.92)***
Husbandry 277.67 (162.70)* 193.52 (99.77)* 123.34 (110.92) 128.88 (61.99)**
before subsidy 315.53 (175.18)* 112.41 (107.71) 164.59 (132.83) 41.58 (74.56)
with subsidy 296.48 (173.38)* 65.10 (106.72) 126.26 (132.00) -6.68 (74.15)
Off-farm -37.67 (99.44) -38.20 (60.94) -103.46 (160.08) -68.78 (89.49)
Non-Cropping 240.00 (191.10) 155.32 (117.17) 19.88 (198.39) 60.10 (110.90)
Other 18.51 (60.73) -16.90 (37.25) 53.08 (98.58) -32.88 (55.31)
before subsidy 296.37 (216.83) 57.31 (133.27) 114.21 (233.08) -60.08 (130.63)
with subsidy 277.32 (215.26) 10.00 (132.41) 75.89 (231.79) -108.35 (129.96)
before subsidy -46.52 (18.02)** -51.83 (24.61)**
with subsidy -57.58 (17.44)*** -55.84 (23.39)**
Husbandry -32.91 (50.24) 2.24 (37.03)
before subsidy -79.44 (54.09) -49.60 (44.35)
with subsidy -90.49 (53.53)* -53.61 (44.07)
Off-farm -0.21 (30.70) 13.98 (53.44)
Non-Cropping -33.12 (59.00) 16.22 (66.23)
Other -13.85 (18.75) -34.67 (32.91)
before subsidy -93.49 (66.95) -70.28 (77.81)
with subsidy -104.54 (66.46) -74.29 (77.38)
Program Impact δ
Program Lag δ
Total Agricultural
Cropping -
Income Component
Table 8. Program Impact on Participant Income
All Households (n=345) Households w/o Autonomy (n=161)
With LagWith Lag No LagNo Lag
* Significant at 10%. ** Significant at 5%. *** Significant at 1%.
Total Agricultural
Total -
... Socio-ecological factors influencing decision-maker preferences. Based on previous fine-scale studies, we found that local governments prefer to enroll high-slope, low-quality, and contiguous land near roads [23,34,35]. Therefore, we selected socialecological variables, including biophysical (slope and average grain yield from 2010 to 2015) and socioeconomic factors (distance of croplands to roads and distance of croplands to households), as key factors influencing decision-maker preferences. ...
... In our study, distance to the household was highly related to the local government's preference for target plot selection. Similar studies have shown that most village and township government decision-makers prefer the easier-to-implement method of simply targeting all steep croplands in a densely populated area rather than targeting enrollments based on conditions across the entire catchment [34]. Therefore, the target plots far from households might increase the opportunity costs for CCFP management and thus were less likely to be chosen as CCFP plots, even if they met the current CCFP standard. ...
Ecological restoration policies and their implementation are influenced by ecological and socioeconomic drivers. Top-down approach-based spatial planning, emphasizing hierarchical control within government structures, and without a comprehensive consideration of social-ecological interactions may result in implementation failure and low efficiency. Although many researchers have indicated the necessity to engage social-ecological interactions between stakeholders in effective planning processes, socioeconomic drivers of ecological restoration on a large scale are difficult to quantify because of data scarcity and knowledge limitations. Here, we established a new ecological restoration planning approach linking a social-ecological system framework to large-scale ecological restoration planning. The new spatial planning approach integrates bottom-up approaches targeting stakeholder interests and provides social considerations for stakeholder behavior analysis. Based on this approach, a meta-analysis is introduced to recognize key socioeconomic and social-ecological factors influencing large-scale ecological restoration implementation, and a stochastic model is constructed to analyze the impact of socioeconomic drivers on the behavior of authorities and participants on a large scale. We used the Yangtze River Basin-based Conversion of Cropland to Forest Program (CCFP), one of the largest payments for ecosystem service programs worldwide, to quantify the socioeconomic impacts of large-scale ecological restoration programs. Current CCFP planning without socioeconomic considerations failed to achieve large-scale program goals and showed low investment efficiency, with 19.71% of the implemented area reconverting to cropland after contract expiry. In contrast, spatial matching between planned and actual restoration increased from 61.55% to 81.86% when socioeconomic drivers were included. In addition, compared to that with the current CCFP implementation, the cost effectiveness of spatial planning with social considerations improved by 46.94%. Thus, spatial optimization planning that integrates both top-down and bottom-up approaches can result in more practical and effective ecological restoration than top-down approaches alone. Our new approach incorporates socioeconomic factors into large-scale ecological restoration planning with high practicality and efficiency.
... According to the evaluation results of DEA model, the EEoSLCP in the Loess Plateau has been at a low level, and the average value from 2002 to 2015 is only 0.357, which may be closely related to the fact that the SLCP is a top-down and government-led FERP [73,74]. First, SLCP adopts a "top-down" implementation model. ...
... First, SLCP adopts a "top-down" implementation model. After the central government determines the task indicators of SLCP, the provincial, municipal, and county forestry departments allocate the task indicators at each level, which has significant characteristics of a socialist state planning system and has certain compulsory aspects [74]. Before the implementation of SLCP, farmers did not participate in the discussion and planning of SLCP implementation, but were only passively included in the implementation of SLCP, which made SLCP unable to precisely select compensation targets (identify the largest potential ecosystem service providing farmers), coupled with the high compensation standard of SLCP, thus leading to the low EEoSLCP. ...
Full-text available
The Forestry Ecological Restoration Program (FERP) aims to restore the world’s degraded forest landscapes to restore biodiversity and mitigate climate change. Scientific evaluation of eco-efficiency of forestry restoration programs (EEoFERPs) is the basis for developing and implementing inclusive and sustainable development policy measures. We take the world's largest FERP—China’s Sloping Land Conversion Program (SLCP)—as an example. Using 314 county-level panel data in the Loess Plateau, the core area for the implementation of the SLCP, during 2002–2015, this study aims to evaluate the eco-efficiency of the Sloping Land Conversion Program (EEoSLCP) based on a DEA model and to measure the eco-efficiency dynamic changes through a Malmquist index model. The results show that: (1) The overall EEoSLCP of the Loess Plateau is at a low level, with an average efficiency of only 0.357 from 2002 to 2015. EEoSLCP is on an overall upward trend, mainly influenced by technical efficiency. (2) There are significant spatial differences in EEoSLCP among counties on the Loess Plateau, with an overall decreasing distribution trend from south to north and from southeast to northwest. (3) The Malmquist index of the EEoSLCP grew at an average annual rate of 17.7%, with technical efficiency changes being the most important factor driving its growth. Our results suggest that in the future, when implementing or designing FERPs, it is necessary not only to select the appropriate restoration plan precisely while respecting the laws of nature, but also to improve the management and technical level of FERPs accordingly.
... Using matching control techniques, they found that much of the enrolled area would have remained forested even in the absence of payments. Xu et al. (2010) addressed the targeting effectiveness of the Sloping land Conversion Program, launched in 1999, which sought to afforest Chinese land with slopes above 25 degrees. Their findings reveal significant mistargeting of flat fertile land and plots with relatively high opportunity costs that received program payments. ...
... For competitive bidding programs, like the CRP, the offered parcel for enrollment provided untreated observations (Babcock et al. 1997;Yang, Madhu, and Farnsworth 2005;Jacobs, Thurman, and Marra 2014). In the evaluation of the Sloping Land Conversion Program, the plots of a sample of participating and nonparticipating landholders were used (Xu et al. 2010), and point and area sampling data from satellite images were obtained for the evaluation of the Costa Rica and Mexican PES programs, respectively (Alix-Garcia, Shapiro, and Sims 2012; Robalino and Pfaff 2013). ...
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We analyze the targeting and additionality of the Chilean afforestation program reforms implemented in the mid-1990s. Propensity score matching (PSM) estimates are obtained by potential erosion categories using random area sample data. The additional afforestation percentage is estimated to be larger for none or low and very severe categories of potential erosion parcels. Even though the program helped maintain forest cover to highly erodible land, afforested parcels with high opportunity cost and possible negative amenity benefits are also attributed to the program. To improve the cost-effectiveness and to avoid misallocation of land and water resources, more effective targeting is required.
... Mexico introduced a PES program in 2003, allocating 60 million USD annually to it between 2003 and 2009 (Shapiro, 2010). China introduced its Sloping Land Conversion Program in 1999, spending 360 million USD annually during the first three years, and with the ambitious goal of spending 48 billion USD once the program will be at scale (Xu et al., 2010). PES often face low enrollment rates which are blamed on a lack of information about the program and on large subscription costs. ...
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Payments for Environmental Services (PESs) are increasingly used to foster farm- ers’ adoption of greener practices, but their effectiveness is often undermined by low enrollment. In a large randomized field experiment (N = 20,000), we test several non-monetary incentives to increase enrollment into the French implementation of the Agri-Environmental Schemes program of the European Union. We find that sending 10,000 information letters triggers the enrollment of 75 ± 52 farmers at a cost of 102 ± 71 Euros/new enrollee. Pre-stamped reply forms increase contacts with caseworkers but fail to raise enrollment. Testimonies by other farmers seem to decrease the effec- tiveness of the letters. Diffusion effects could increase the impact of our non-monetary incentives by 40% to 50% and decrease the cost of an additional enrollee by around 30%.
... It is widely recognized that an increase in the slope gradient results in more surface runoff and soil loss [12,15], causing lower soil fertility and a higher risk of eutrophication in rivers, lakes, and coastal areas [2]. Soil loss especially from sloping cropland is considered to be the main source of river sediment [15], so the Chinese central government has made great efforts to control water erosion and reduce cultivation in sloping areas, such as the Sloping Land Conversion Program, which is believed to be the world's largest payment for ecosystem services program for improving ecological conditions and farmers' livelihood [16,17]. Even though farmers are willing to give up cultivation of sloping land by diversifying their livelihood options [18], sloping croplands are still commonly found in central and southern China due to the bottom line of grain security and local terrain restrictions. ...
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With the rapid urban expansion and extensive occupation of cropland, sloping cropland has become an important cropland resource across China. How sloping cropland will change under different socioeconomic scenarios is poorly understood. Therefore, we modeled land-cover change using SSP-RCP multi-scenario simulations and analyzed the evolution and driving factors of sloping cropland change in the middle reaches of the Yangtze River Region (MRYRR). The results indicate the following: In the past twenty years, the cropland and sloping cropland areas in this region declined but the proportion of sloping cropland in total area has been increasing. The average slope of sloping cropland has increased from 7.95° to 8.28°. By 2035, the sloping cropland and total cropland areas will continue to decrease according to the current trend (SSP2-4.5). The average slope will increase maximally to 8.63° under the SSP4-3.4 scenario and minimally to 8.45° under the SSP4-6.0 scenario. Under SSP4-3.4, the extent of slope increase will exceed that in 2005–2010, when regional cropland slope showed the strongest increase in the past. Among 14 social, economic, and ecological factors, average annual precipitation and GDP contributed the most to the change in sloping cropland. This study provides support for decision-making in sustainable land resource allocation to balance urban expansion and cropland conservation.
... There may be gains to trade, if areas offer subsidies to assure that environmental damages do not flow to cities. That is precisely the motivation for a growing suite of new institutions, worldwide, for payments from urban to rural areas conditioned on flows of ecoservices [e.g., conservation of erodible land in agriculture (Xu et al. 2010) or limits on agricultural run-off into rivers such as preserving private forest land ( Jayachandran et al. 2017) habitats creating private reserves (Smith & Shogren 2001)]. While their impacts have not always been high (Pattanayak et al. 2010, Robalino & Pfaff 2013, if urban interests are sufficient transfers can benefit all parties. ...
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For over a century, starting with the work of Alfred Marshall (and also in resource economics), economic geography has emphasized the productivity of dense urban agglomerations. Yet little attention is paid to one key policy implication of economic geography's core mechanisms: Environmental policies can aid economic development, per se—not hurting the economy to help the environment but advancing both objectives. We review mechanisms from economic geography that imply that environmental policies can deliver such win-wins: influences upon agglomeration of long-standing natural conditions, like usable bays, which long were perceived as fixed yet now are being shifted by global environmental quality; agglomeration's effects on other influential conditions, like urban environmental quality; and the effects of rural environmental quality on the flows to cities of people and environmental quality. Finally, we consider a geographic policy typology in asking why society leaves money on the table by failing to promote environmental policies despite the potential win-wins that we highlight. Expected final online publication date for the Annual Review of Resource Economics, Volume 14 is October 2022. Please see for revised estimates.
... That is to say, the cultivation of steep slopes prone to soil erosion is stopped, and then vegetation cover and ecosystem services are increased by converting the slopes into forest land and grassland [11,12]. Therefore, the SLCP will essentially only have an impact on land use change but will then indirectly affect vegetation cover and ecosystem services through the resulting land use change [43,44]. Land use change is the basis for changes in vegetation cover and ecosystem services, and there is a coupling relationship among these three aspects [45,46]. ...
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The Sloping Land Conversion Program (SLCP) is the largest ecological restoration program in the world. Evaluating the ecological effects of the SLCP not only provides a scientific basis for China to improve the SLCP but also provides a reference for other countries in the world to evaluate the ecological effects of ecological restoration programs being implemented or to be implemented. To this end, we took the Loess Plateau, the core area for the implementation of the SLCP, as an example and, based on multi-source remote sensing data and GIS technology, we conducted a comprehensive evaluation of the ecological effects of the implementation of the SLCP on the Loess Plateau. The results showed that, first, from 2000 to 2018, a total of 12,372.05 km2 of cultivated land was converted into forest land and grassland on the Loess Plateau, and this contributed to an increase in vegetation cover from 45.09% in 2000 to 64.15% in 2018, and a decrease in the soil erosion modulus from 26.41 t·hm−2·yr−1 in 2000 to 17.92 t·hm−2·yr−1 in 2018. Second, the 6–25° slope range is the core area of the Loess Plateau for implementation of the SLCP. In this range, the area of cultivated land converted into forest land and grassland accounts for 60.16% of the total area of transferred cultivated land. As a result, the 6–25° slope range has become the most significant area for improving vegetation cover and reducing the soil erosion intensity, and it is mainly concentrated in the southwestern, central and central-eastern hilly and gully areas of the Loess Plateau. Third, from 2000 to 2018, the climate of the Loess Plateau tended to be warm and humid and was conducive to the implementation of the SLCP. Among these factors, precipitation is the dominant factor in determining the spatial distribution of vegetation on the Loess Plateau, and the increase in precipitation is also the main reason for the promotion of vegetation growth. Fourthly, from 2000 to 2018, the ecological environment of the Loess Plateau was significantly improved as a result of the combined effects of the implementation of the SLCP and climate warming and humidification, but the primary reason is still the implementation of the SLCP.
... However, most of them understand the land as a physical and economic object. In this case, the paddy field conversion is always associated with land availability (Axinn & Ghimire, 2011), economic value of land (Chakrabarti & Kundu, 2009), development impacts (Xu et al., 2010), implications on sustainable production (Quasem, 2011) and environmental degradation (Wang & Liu, 2013). In fact, in addition to containing physical and economic aspects, the land also has a cultural symbolic element (Plat, 2004). ...
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Experts from various perspectives have widely reviewed the patterns, processes, reasons, and impacts of paddy field conversion. However, most of these reviews tend to understand paddy fields from the physical-material dimension. By using the perspective of contemporary geography, this paper provides a critical conceptual overview of the conversion of paddy fields through the elaboration of human-nature dialectic as a central theme in the discipline of geography. The dialectic also contains identity, spatial awareness, and spatial-symbolic order issues that affect the existence of farmers and their paddy fields. This critical review results in the argument that the relationship between farmers and paddy fields represents a spatial-symbolic order that contains values, enthusiasm, identity, and living traditions. The identity and existence of farmers are part of the existence of paddy fields. Paddy fields have become part of the minds and consciousness of the farmers. The conversion of paddy fields will reduce the eco-cultural relations in this order and replace it with a capitalistic system. Keywords: contemporary geography, human-nature dialectic, paddy field conversion, spatial-symbolic order, sustainable agriculture
Economic incentives to simultaneously address poverty and biodiversity loss may fail if they do not align with local values or norms. Grazing livestock across the Tibetan Plateau's vast but fragile grasslands is often characterized as the area's primary driver of habitat degradation. China's grassland restoration policy provides eco-compensation subsidies for herders, with the assumption that providing extra cash income will help alleviating grazing pressure. We investigated potential impacts of this supplementary income, in combination with caterpillar fungus, a major local cash income source, on the pastoral livelihood on the Tibetan Plateau. Through field livestock census in seven communities and household livelihood interviews with 153 households we found that at both household and valley levels, eco-compensation didn't have the intended effect of reducing grazing pressure, while caterpillar fungus income has significant positive relationships on grazing intensity. Meanwhile, we found significant decrease of above-ground plant biomass after policy implementation was linked with low-elevation pastures with most intensive grazing, which indicated a negative impact of grazing on grassland condition at our study sites. Further, based on herdsmen's perceptions on different cash income sources we suspected those non-pastoral incomes might have even subsidized pastoralism. We suggested future economic incentives related to grassland restoration should be more targeted towards villages with low cash income and overgrazed grasslands, with clearly stated responsibilities and obligations, instead of standardized cash payment.
Chinese state authorities have successfully accelerated afforestation over large areas through the Returning Farmland to Forest Program (RFFP). However, variation in the implementation of the RFFP among villages may lead to uncertainty in forest transitions at multiple spatial levels. Here, we combined remote sensing analysis with field quadrats and questionnaire surveys to assess the RFFP's effectiveness in increasing vegetation cover at the Lancang watershed, township and natural village levels in Weixi County, a biodiversity hotspot in northwest Yunnan. From 2000 to 2010 and 2010 to 2014, forest coverage increased, and areas of shrubland and agricultural land decreased at both watershed and township levels, which reflected a positive impact of the RFFP. However, the new forests established under the RFFP in the study area mostly consisted of monocultures of walnut and pine trees, a situation that could threaten local biodiversity. Changes in forest and shrub coverage varied among natural villages—gain and loss trends both occurred, indicating a limited impact of the RFFP. Nonparticipating RFFP households also abandoned cropland, and agricultural land area in these villages declined over the study period. At the watershed and township levels, RFFP greatly promoted increases in forest coverage. Elevation, the behavior of village officials and household livelihoods were often the most relevant factors at the natural village level. Interactions among these multiple drivers resulted in cross-scale heterogeneity of vegetation cover changes in northwest Yunnan. Future forest policies need to be tailored to specific regions and be based on assessments of local natural and socioeconomic conditions.
The allocation of property rights is widely recognized to have important implications for resource use and the distribution of household welfare. The introduction of household farming in the early 1980s extended use rights to cultivated land on a fairly egalitarian basis. Over the past two decades, control over allocating farmland has remained in the hands of local leaders. In evaluating China's land tenure system from a policy perspective, the critical question is how effective the system has been in providing households with the necessary incentives to ensure rational land use and investment, while simultaneously helping local communities to meet households' needs. Looking past 2001, how well does the system fit the needs of China's rapidly evolving economy? Our survey work suggests enormous heterogeneity at the village level. In some villages farmers seem to hold relatively long tenures and have the right to select crops, convert land to alternative agricultural uses and rent out land, all of which are typically associated with a private property regime, albeit short of being able to buy or sell the land. In other villages, tenure is shorter and the use of the land is constrained in various ways.
This paper contributes to the debate over land tenure in rural China by conceptualizing and measuring multiple dimensions of property rights in a way that elucidates the competing interests that are affected by the property rights regime. Utilizing a unique village-level data set, this paper argues that the regional and temporal variation in rural property rights signals a pattern in which decentralized institutional innovation occurs in response to the competing interests of the national state, of local authorities, and of present and possible future individual land users. Unlike the earlier debate concerning the household responsibility system, the current property rights dilemma is intrinsically more complex because the potential conflicts of interest between individuals, local collectives and the state are greater. Resolution of that debate will ultimately require careful exploration of the reality and substance of the tradeoffs and competing interests that make further reform of rural property rights so difficult.
With a budget of RMB 337 billion (over US$ 40 billion), the Sloping Land Conversion Program (SLCP) is one of China's most ambitious environmental initiatives, and is one of the world's largest land-conservation programs. Pending successful completion, it will have significant implications for China's forests and remaining natural ecosystems, representing an almost 10 % increase in current national forest area. However, we provide evidence that it is in danger of failing to reach its goals due to flaws in design and implementation. Four years into the program, this paper uses a 2003 data set of 358 households to examine SLCP implementation and to suggest improvements. Of central concern has been overly quick expansion over the past few years, which has served to exacerbate problems already revealed, but not adequately addressed, during the pilot phase of the program.
How important are neighbourhood endowments of physical and human capital in explaining diverging fortunes over time for otherwise identical households in a developing rural economy? To answer this question we develop an estimable micro model of consumption growth allowing for constraints on factor mobility and externalities, whereby geographic capital can influence the productivity of a household's own capital. Our statistical test has considerable power in detecting geographic effects given that we control for latent heterogeneity in measured consumption growth rates at the micro level. We find robust evidence of geographic poverty traps in farm-household panel data from post-reform rural China. Our results strengthen the equity and efficiency case for public investment in lagging poor areas in this setting. Copyright © 2002 John Wiley & Sons, Ltd.