Impact of Forest Eco-compensation Subsidy on Household Forest Input:Evidence from Jiangxi Province,China

DU Juan HU YapingXIE Fangting ZHU Shubin

1 College of Economics and Management,Jiangxi Agricultural University,Nanchang 330045,China 2 College of Computer and Information Technology,Jiangxi Agricultural University,Nanchang 330045,China 3 Research Center of Jiangxi Rural Development,Jiangxi Agricultural University,Nanchang 330045,China

Abstract:Larger amount of national and provincial forest eco-compensation funds in China have been distributed to farmers annually,which aims to encourage farmers input more labor and fund in daily forestry management. We selected 503 household from 50 villages of 10 counties in Jiangxi Province in the paper. Household labor and cash input responded negatively towards forest eco-compensation fund in forestry management. Forest eco-compensation subsidy (FECS) granted to the household in the rural mountain area didn’t stimulate the household labor and cash input in forestry management. It implies that it is not a wise way to distribute FECS equally to the rural household,so as to promote the forestry ecological quality. The current forest eco-compensation policy(FECP)need modifying urgently.

Key words:forest eco-compensation subsidy(EFCS);household forestry input;logit regression;tobit regression;policy modification

Introduction

Forest eco-compensation subsidy (FECS) is the money rewards distributed to forest farmers by the environmental beneficiary through administration or market measurements with the comprehensive consideration of ecological protection cost[1].A huge concentrated area of eco-forest has been arranged without proper valuation by government,with a uniform FECS standard in China.

The FECS,as the important component of forest eco-compensation policy (FECP),has undergone for 10 years on both national and provincial level[2-4].By 2004-2008,Jiangxi,as representative of southern provinces pilots the national collective forest reform,has taken many measurements to protect household property rights and promote their income.Forest logging forbidden policy was designed to recover the forest;by 2009-2014,collective forest reform made a significant progress,while national forest tenure was affirmed.Forest eco-compensation formulated in specific key ecological protection zones,assuring the rural household income;from 2015 till now,National Five-Sphere Integrated Plan dominate the rural policies[5-8].Forest eco-compensation classified reimbursement and stricter ecological forest evaluation were performed cautiously.

Actually,implementation of the FECP was not only helpful to forest ecological recovery,but also achieved some economic and social goals,such as poverty alleviation[9-10].In China,governmental purchase was the primary way of FECP[11-12],with market-based compensation case as supplements,for example,basin inter-provincial water compensation[13-14]and carbon sequestration[15].Forest classified eco-compensation based on discrepant ecological location has been performed recent years[16-17].In 2018,Jiangxi government distributed 397.5 RMB per hectare to household.Forest ecological service valuation[18-20]and household opportunity cost were the most important considerations for reasonability of forest eco-compensation standard.FECS absolutely doesn’t meet the household expectations,causing the farmers’ dissatisfaction.Thus,farmers were unwilling to input more labor and cash into forest management,due to the less income from FECS.

However,10-year experience showed that,China’s FECP still had some problems.The evaluation of the forest eco-compensation policy in domestic research has been made from several aspects using performance analysis,performance prism,expert scoring and hierarchical scoring such quantitative and qualitative research methods[21-24].Chinese government has input 12.26 trillion RMB till now,while national funds take 91.7%.Farmers gained only 397.5 RMB per hectare in Jiangxi Province 2019,equivalent of the sale revenue of a bamboo.Farmers were willing to accept cash compensation and technical assistance,in order to offset household self-employment expenditure.

Household forest input was one of the most arguably studied areas within the field of forest economics.Possible elements influence the forest input would be demographic characteristics,forest resource endowment,different forest policies and geographic environment,etc.[25-30].The impact mechanism varied according to different regions and forest types[31-33]To promote the forest quality,enough forest labor and cash input is essential.This paper constructed regression models to identify the effect of increasing labor and cash input pushed by FECS on the household view.We tried to propose some possible suggestion to modify current FECP.

1 Methodology

1.1 Study area and data collection

Jiangxi Province,a pioneer of the forest reform in Southern China,plans to construct an integrate and comprehensive forest ecological compensation system in 2020[20].This paper detected the impact of FECP on rural input labor and cash.The former data of Jiangxi Province intended to be a nice evidence.

According to the diversified economic and forestry status in different regions of Jiangxi Province,ten counties,five villages in each county,and about ten households from the household registration list in each village were chosen by a stratified random sampling method.Thus,there were 503 households selected from 50 villages in ten counties for survey.Household might be substituted by another with similar socioeconomic conditions randomly selected in the same village if the original one loss contact to ensure the sample size throughout.In order to compare the different labor and cash input behaviors among different groups who gained FECS or not,157 households gained FECS,346 households never gained FECS,and consisting of total 503 household.

1.2 Variables specification

To investigate whether the farmer’s forestry input behavior varies in response of FECS distribution,we set the participation and volume of the input of the labor and fund these four independent variables for family forest landowners.Farmers were assumed to benefit from forest management against the minimum cost.Earlier studies showed that the farmer’s forestry input behavior is influenced by social,economic,demographic and policy variables.The conceptual variables were specified as shown in Tables 1-2.

Table 1 Definition and assignment of the dependent variables

Table 2 Definition and assignment of the independent variables

(Table 2 continued)

For independent variables,three concepts about the labor and cash input in household forest management were concerned:the possibility of anticipation,total volume and unit input degree.Because of the inherent forest input cycle,all of the independent variables were got by former four years data using the simple average method.FECP was the core dependent variable settled in this paper,so as to show the relationship between the actual FECS obtain by the farmer and their forest management input behavior.To define the household characteristics carefully,demography,forestry resource endowment and financial ability，were designed as the important covariates.The other two independent variables,forest reform policy and geographic and environment characteristics,showed the reliable external situation for research.Cross variables connected FECP were also taken into consideration.

1.3 Model specification

The analysis in this paper completed by 3 stages.

Firstly,key elements influenced the participation possibility of the labor and cash input in the forest management were considered.All the available elements and key elements were brought to the binary logit regression models.The model was

(1)

where,i=1,2;P(e1) andP(e2) refer to whether the farmer input labor or cash in forest management.X1,X2,X3,andX4represent FECS,household resource endowments,forest reform policy elements and regional environment.Complete model and simplified model,which stepped in backward using 0.2 as selecting reference,both were constructed to certify their stability.

Secondly,core elements influenced the volume of labor and cash input in household forest management were discussed in this paper.Especially,how did the actual forest ecological compensation fund influence the volume of the labor and cash input in household forest management was the focus for the research.To make the result more reliable,the total and volume per area were both concerned in the models.The Tobit model was settled as

Yi=β0i+β1iX1+β2iX2+β3iX3+…+β6iX6+μi,

(2)

where,i=1,2;Y1andY2refer to the volume of labor and cash input in household forest management.βandμrepresent the matrix correlation and random error.

Finally the impact of EFCP towards the labor and cash input in household forest management also varied by the corresponding policy and environment factors.Crossed variables with forest reform policy and geographic elements were examined respectively in this paper.

1.4 Hypothesis

According to rural production theory,the forest stock,and household forestry income as well,depends on the relative input factors:labor,forestland and cash,which were substitutable and complementary to each other.FECP implemented accompanied with forest logging ban,made less forestland input to forest production.In order to maintain original household forestry income,all the forestry input factors may rematch.For short term,forestry income reduction last cycle definitely resulted forest input factors minimizing.For long term,forest input factors may increase if traditional forest production pattern replaced by new production pattern.

Due to the present FECS distribution method,no matter how the ecological forest managed,the FECS per unit was the same.Farmer’s sensitive to manage ecological forest cannot ideally stimulate by current forestry ecological policy.Forest ecological compensation distribution standard needs modifying on the basis of ecological forest management quality.

H1:the volume of forest eco-compensation fund has negative impact on the possibility and volume of the household forestry labor input.

H2:the volume of forest ecological compensation fund did not have significant impact on the possibility and volume of the household forestry cash input.

H3:a series of forest policies and forest ecological compensation policies did not coordinate well.The corresponding cross variables does not significantly increase the household forest labor and cash input.

2 Empirical Results

2.1 Characteristics of the sampled households

2.1.1Descriptivestatisticsofthevariablesconcerned

In Table 1,sample farmers whose average age was 56,acquired no more than nine years education,regarding the farming as their part work now.And 74% farmers input more or less labor in their forest,only 20.9% farmers actually input cash in their forest.For forest resource endowment,the sample family has 4.65 pieces forest,including 0.189 hm2timber,0.078 hm2economic forest and 0.184 hm2bamboo.The huge standard deviation shows that the forest resource among the farmers differs a lot.For farmers’ financial ability,the sample family earned 269.4 RMB from forest management and 42 191.6 RMB from non-forest production in 2016.Even in the mountain areas,forest income only took little proportion in the total family income.Forest reform policy,farmers were moderately satisfied with the forest logging restriction policy.A few farmers obtained forest loan and ecological forest insurance.For geographic and environment element,the average distance from the nearest town is 7.34 km,the average population of the sample village is 1558 and the average income of the village is 5 824.2 RMB per person.

2.1.2Comparisonoffactorsinputaccordingtotheforestecologicalcompensationfund

The purpose of the EFCS was to push the farmers protecting the forest resource and promoting the forest quality.Considering the fact that only 31.2% farmers gained forest ecological compensation fund,the forest factor input hasn’t been stimulated as we imaged.

In Table 3,attitude of those farmers who got FECS towards cash input is slightly negative than others (possibility is 0.197 and 0.214,respectively).

Table 3 Comparison of forest input factor between FECS gainers and others

Note:*indicates significant difference at 5% between two mean values.

The willingness of household labor input seems undifferentiated (possibility is 0.745 and 0.737 respectively).The total forest labor input of the farmers who gained the forest ecological compensation fund are slightly less than others (69.25 and 71.24 respectively).While,the farmers who never gained the forest ecological compensation fund input 5.8 times forest cash of those gained (1 909.75 and 11 191.28 respectively).If forest area is taken into consideration,the results tend to be interesting.The unit forest labor input of the farmer who gained forest ecological compensation fund significantly less than those never gained (0.65 and 1.17 respectively).As well,the forest unit cash input of the farmers who gained forest ecological compensation takes only 1/7 of others.

2.2 Econometric analysis of farmers’ forest factors input

2.2.1Econometricanalysisoffarmers’participationinforestmanagement

To reduce the data heteroscedasticity problem and facilitate model analysis,logarithmic transformation was performed on six dependent variables,namelyLp,Cp,Ltotal,Ctotal,Lunit,andCunit.Table 4 shows the logit regression results.Model 1 explains the main reasons influenced the farmers’ possibility of labor input in forest investment.Model 3 explains the main reasons influenced the farmers’ possibility of cash inputted in forest investment,as well.Model 2 and model 4 were simplified models of model 1 and model 3 running with the backward step method respectively.The results seemed reliable,due to the consistence of the coefficients and directions of significant independent variables from two group of models.

Table 4 Logit regression results of household forest input possibilities

Note:*significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

The actual value of FECS was the focus of this paper.The result shows that,the forest eco-compensation value farmers gained imposes significant negative impact on their labor anticipation in forest investment,while almost no impact on their cash anticipation.The more forest eco-compensation value gained,the less possibilities of farmers’ labor anticipation in forest management,which contrary to our inherent imagination.

For household labor input possibility in forest management,both the household resource endowment and geographic characteristics are essential.Area of timber and economic forest positively influence the household labor anticipation in forest management.The actual household forest income and average income of the local village have positive impact on household labor anticipation in forest management.Distance from the nearest town has negative impact on household labor anticipation in forest management.The demographic background of the farmer,including age,education,real work and leader experience,has no impact on the labor anticipation in forest management (details shown in models 1 and 2).

For household labor input possibility in forest management,household financial ability seems important.Area of timber,household forest income,forest loan and population of the local village positively influence household cash anticipation in forest management.Distance from the nearest town has negative impact on household cash anticipation in forest management (details shown in models 3 and 4).

2.2.2Factorsinfluencingtheforesthouseholdlaborandcashinput

Tobit models constructed to explain the factors which influence the forest household labor and cash value input.The forest household labor and cash value inputted in Table 5 was indicated by total labor and cash value input,and Table 6 is labor and cash value inputted per forest unit.Models 6,8,10 and 12 were the simplified models of models 5,7,9 and 11.The result of each pair was coherent,conforming the robustness of the models.

Actual value of forest eco-compensation fund made significantly negative influence to the household labor value input in forest management,while almost no impact on the household cash value input,no matter total or unit value.Household input different volume of labor and cash due to the different forest types (timber,economic forest and bamboo).

Table 5 Tobit regression results of household forest input volume

Note:*significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

For the household total labor value input in forest management,besides the actual forest eco-compensation value,area of different types of forest (timber,economic forest and bamboo),household forestry income and the average income of their village had positive impact on it.For the household total cash value input in forest management,area of timber,household forestry income,loan availability and local village population had positive impact on it.Distance from the nearest town had negative influence to both household total labor value (details in models 5,6,7,and 8).

Table 6 Tobit regression results of household forest input per unit

Note:*significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

For the household unit labor value input in forest management,besides the actual forest eco-compensation value,economic forest area and household forestry income had positive impact on it.The forest plot had negative influence on it.For the household unit cash value input in forest management,area of timber,household forestry income,loan availability and local village population had positive impact on it.The forest plot has negative influence on it.Distance from the nearest town have negative influence on it (details in models 9,10,11,and 12).

2.2.3Inputlaborandcashcomparisonamongdifferentsamplegroups

To define the labor input among different sample groups,additional four models are established in Table 7.Similar to model 6 and 10,the more forest eco-compensation value farmers gained,the less labor farmer actual input in real forest management,no matter considered by total value or unit value.The total labor input of the two groups whether gained forest eco-compensation fund,were significantly affected by area of timber,forest income and distance from the nearest town.Logging restrictions impose negative impact on farmer’s labor input who gained the forest eco-compensation fund.Comparatively,economic forest area and local average income are positively affected the labor input in forest management.For labor input per unit area,forest plot is a key negative element.Forest concentration is efficient to forest management.

Table 7 Tobit regression results of FECS gainers’ labor input

(Table 7 continued)

Note:*significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

As shown in Table 8,there was almost no impact on forest cash input,no matter how much forest eco-compensation fund farmers gain.Elements affected the forest cash input among different groups who gained forest eco-compensation or not were different.Household forestry income,loan availability and distance from the nearest town were essential to the forest cash input of both sample groups.However,for the farmer who did not gain forest eco-compensation,how much forest cash input also depended on timber area and population of local village.The result,detected by unit volume of input labor and cash,was similar to the relative result of total volume of input labor and cash,which verified the model robustness (details in models 17-20).

Table 8 Tobit regression results of FECS gainers’ cash input

Note:*Significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

2.2.4Crossvariableanalysis

FECP did not work alone.Current forestry policies interacted with each other.Models 21-24 in Table 9 were set to search the effect of those cross variables connected forest eco-compensation policy on the volume of forest labor and cash input.Forest loan policy was a beneficial assist to promote forest labor and cash input.

Table 9 Forest policy cross variable regression results

Note:*significance atα= 10%;**significance atα= 5%;***significance atα= 1%.

To investigate whether the forest eco-compensation policy varies in different geographic background,models 25-28 established in Table 10.It showed that geographic location was the key element to influence the effect of ecological forest policy on increasing forestry labor and cash inputted.Richer the village was,more labor and cash farmers inputted.The higher income of the farmer’s local village,more labor the farmer were willing to input.

Table 10Geographic elements cross variable regression results

VariablesActual labor inputActual cash inputAll samplesFECS gainersAll samplesFECS gainersModel 25Model 26Model 27Model 28Mdis-0.107(0.037)??-0.111(0.032)??-0.584(0.202)??-0.595(0.197)??Mincome0.101(0.034)??0.067(0.024)??0.21(0.078)??0.152(0.149)Mpopu0.003(0.004)-0.020(0.026)0.261(0.089)??0.085(0.107)Cons2.270(0.814)??3.151(1.157)??-7.726(2.765)??-3.96(7.534)

Note:*significanceatα= 10%;**significanceatα= 5%;***significanceatα= 1%.

3 Conclusions

Through the research,we found that the original purpose of FECS may not achieve.Correspondently,current FECP need modifying emergently.Farmers tended to input more labor than cash in forest management.Obviously,forest management was still at a household labor-based small-scale model stage,while labor was cheap enough to be the substitution of forestry fund.Farmers who gained EFCS were more likely to decrease the labor and cash input,which contrary to the policy goals of FECF.

From the analysis in the paper,we learned that,more EFCS the farmer gained,the less labor they input in the forest management.Actual cash input had no relevance with EFCS.Endowment of the forestland,for example forest plots,the area of different types of forest (timber,economic or bamboo) were important considerations for farmers’ labor input.More household forestry income and facilitated forest loan policy may encourage them invest more in forest management.Uniform forest eco-compensation standard can hardly stimulate the farmers input more in their forest management.Eco-forest is commonly lack of scientific operation without of follow-up supervision mechanism.

Compared two groups of sample farmers who gained forest eco-compensation fund or not,there were no statistical difference in forest labor input.The average forest input cash of the group who did not gain the FECF was 5.9 times of the other group.Geographic location was the key element influence the forestry labor and cash input.Current FECP may be disputable.It seemed equal but did not improve the farmers’ enthusiasm to anticipate in forest management,neither ecological forest nor other types of forest[21-23].More EFCS the farmers got,less labor input.The daily forest input cash was concentrated in planting and logging round,making the ecological forest no differences with other forest types.

In order to promote the eco-forest quality with limited EFCS,eco-forest maybe classified subject to its ecological location.The ecological region was the key index instead of the administration region.Additional,EFCS standard differs subject to forest types.It was reasonable that logging restraint varies according to different ecological regions[27-32].Thirdly,follow-up supervision was essential.Considered eco-forest circulation,professional forest firm establishment maybe the possible way.