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2444-8656
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Research on the willingness of Forest Land’s Management Rights transfer under the Beijing Forestry Development

Published Online: 25 May 2022
Volume & Issue: AHEAD OF PRINT
Page range: -
Received: 21 Aug 2021
Accepted: 14 Nov 2021
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

The research on land-use transfer has always been a hot spot for domestic and foreign scholars, and it is also the focus of practical works. In China, the Beijing Municipal Government has put forward many forestry policies, emphasising the need to improve the collective forest land transfer system against the new background of the capital’s forestry development. This paper built a binary logistic regression model and structural equation modelling (SEM) based on the theory of planned behaviour (TPB), collected research data through questionnaires and explored the factors that affect the willingness of the transfer of forest land. The research showed that (1) farmers’ attitudes and behaviours had significant negative impacts on the willingness to transfer forest land. When farmers are assured that they know more about the forest land’s management rights transfer policy, and the forest land’s management rights transfer can bring more income to them, they will be satisfied with the existing forest land policy, which is more conducive to the forest land’s management rights transfer; (2) the perceived behaviour control of farmers had no significant impact on the willingness of forest land’s management rights transfer, that is, difficulties that farmers perceived in the implementation of forest land’s management rights transfer did not significantly affect their decision-making willingness and (3) the different results of the two models’ analysis on the impact of subjective norm on the willingness of forest land’s management rights transfer need further study and verification.

Keywords

Introduction

‘Opinions of the State Council on Improving the Collective Forest Right System’ (GBF [2016] No. 83), ‘Opinions of the General Office of the People’s Government of Beijing city on the Implementation of Improving the Collective-owned Forest Right System and Promoting the Development of Beijing’s Forestry’ (JZBF [2018] No. 17) and other documents all put forward steps for improvement of the collective forest land circulation system [1]. The forest land management rights transfer can accelerate the development of moderate scale management in various forms, and optimise the allocation of forest land resources and scale effect, which is of great significance [2].

The forest land’s management rights transfer is an important part of the forest right transfer and land (the forest rights transfer refers to the transfer of forest, wood and forest land, including management rights transfer of forest land, the wood ownership transfer under the contracted system or collective economic organisations). It is different from the forest ownership transfer belonging to the same forest right circulation. At the same time, it is also different from the general land management rights transfer due to the distinct use and function of forest land [3]. Farmers’ production and operating activities are related to the local conditions in rural regions and the problems encountered in forestry management activities vary among regions [4]. In addition, for a region, the forest land’s management rights transfer is periodical to some degree, and it is of unique characteristics in different periods [5]. Therefore, it is of great theoretical and practical significance to study the forest land’s management rights transfer and identify the key factors that affect the willingness of the forest land’s management rights transfer based on the latest research data, combining the relevant researches of forest rights transfer with those of land circulation.

China’s forest tenure system took shape in the household contract responsibility system, which started in the early 1880s, greatly mobilising the enthusiasm of farmers and laid a foundation for economic growth in the rural areas. However, it led to the decentralised management of agricultural land. As early as the 20th century, scholars in China studied the forest rights. Since a new round of collective-owned forest rights system reform in China, agricultural modernisation has continued to develop, the process of rural land transfer has accelerated, and the development of appropriate economies of scale in forest land has also become an inexorable trend. A large number of scholars have taken up the study of willingness of forest rights transfer and their influencing factors [6]. William and Gregory studied the benefits and costs of operating forestland, believing that the comparative relationship between the benefits and input costs of forestland was the key factor affecting the transformation of forestland [7]. Darla and Abigail analysed the reasons why rural land in the south of Indiana was converted to private forest land in the process of urbanisation [8]. Gerald C. Nelson believed that the stability of property rights could promote the sustainable development of forestry and help improve the utilisation efficiency of forest land [9]. Most scholars studied the willingness of forest land transfer and its influencing factors, and adopted the logistic model in econometrics. For example, the logistic model was used to analyse the influence of the different farmers’ resource endowments on their forest land circulation behaviours [10], and the influencing factors and levels that affected farmers’ willingness to transfer forest land after the collective forest right reform had also been analysed [11]. Heckman’s two-stage model was also used to study the impact of human capital on the inflow behaviour and inflow scale of farmers’ forest land [12]. Game theory is also an analysis tool that is often used to study land circulation. It could conduct game analysis on the decision-making of different types of farmers on forest land circulation [13].

Scholars have studied the influencing factors of forest land circulation willingness from different perspectives, including the influence of farmers’ decision-making behaviour [14], policy information cognition [15], household income level [16], family labour force investment [17], farmers’ career differentiation and income differentiation [18] and individual endowment and cognition [19] on forest land circulation of farmers. John selected two villages in India to conduct field investigations, studied the factors affecting the farmland transfer behaviour of farmers, and finally classified the factors affecting farmland transfer into three categories: farming choices, agricultural loans and land investment [20]. It can be concluded that the factors that affect farmers’ willingness and behaviour of forest land transfer can be roughly divided into farmers’ individual characteristics, family economic characteristics, forest land natural conditions, forestry management status, forest rights and logging system arrangements, circulation market conditions and forest land transfer policies.

Existing research results provide important references for further reform of the collective forest rights system and continuous improvement of the forest land circulation system. However, few studies were carried out on Beijing’s forestry development, and there is a lack of research on the influencing factors from the psychological cognitive level of the subject behaviour. Therefore, it is necessary to conduct research on Beijing’s forest tenure system; especially, Beijing emphasises the construction of ecological civilisation and ecological environment for the lives of urban and rural people in its development plan. Against this background, this paper studied the forest land’s management rights transfer in Beijing based on the theory of planned behaviour (TPB), which is an important theoretical support to study the decision-making of forest land’s management rights transfer. The common models based on the theory are mainly logistic regression model and structural equation modelling (SEM). Logistic regression model is widely used for the identification of objective factors affecting the transfer willingness, especially when the data volume is small; the latter is often used to study the behaviour decision of farmers [22], which is more suitable for the identification of subjective factors affecting the transfer willingness of forest land’s management rights. This paper built both logistic regression model and SEM for comparative analysis.

Materials and methods
Overview of the theory of planned behaviour

TPB is an important theory widely used in management, psychology and sociology to explain the influence mechanism of psychological cognition on behavioural intention and decision-making.

According to the theory, the willingness of the subject’s behaviour is key to the decision-making. Whether the residents are willing to participate in the forest land’s management rights transfer is essentially a production decision-making factor of the individual. The traditional theory holds that the goal of individual production decision is simply to pursue profit maximisation, while modern research holds that the individual goal of production decision-making may be diversified. Therefore, individual economic rationality cannot be simply determined only through cost–benefit analysis; other aspects such as identification of others, risk aversion and personal capital should also be taken into consideration.

The TPB provides only a classical research framework for explaining the general decision-making process of individuals and studying the relationship between the influencing factors of behaviour intention and willingness. According to it, an individual’s behavioural intention is mainly determined by three factors: individual’s behavioural attitude, subjective norm and perceived behavioural control. The more positive the attitude is the greater the support of important external factors, while the stronger ability of the individual’s perceived behavioural control is the greater the behavioural intention is, and vice versa. According to this point of view, in the behaviour decision-making of forest land’s management rights transfer, different farmers’ psychological cognition differences on forest land’s management rights transfer lead to behaviour intention differences. Especially, the former is the source of the latter.

Therefore, the willingness of farmers on the management rights transfer of forest land is the driving force to promote the transaction of the management rights transfer of forest land, which depends on the behavioural attitude, subjective norm and perceived behavioural control of farmers on the forest land management rights transfer. These three aspects are explained in the TPB: (1) behavioural attitude is an individual’s cognition and evaluation on the implementation of the decision-making, which is determined by the belief in the behaviour result and the importance of the result; (2) the external ‘subjective norm’, the code of conduct existing in people’s mind, is the social pressure that individuals feel when they don’t know whether or not to carry out this act. It reflects the influence of important people and systems on individual decision-making and (3) perceived behavioural control refers to the individual’s consideration of the factors that promote or hinder the decision-making, which is the degree of difficulty that the individual perceives when performing certain behaviour. The TPB is used in various fields of behavioural intention with its strong ability to predict behaviour.

Binary logistic regression model

On the basis of the TPB and existing research, seven variables are defined as the factors influencing the transfer willingness of forest land’s management rights in Beijing, including behavioural attitude, subjective norm, perceived behavioural control, gender, age, family population and average annual household income. Binary logistic model and SEM are used for data analysis. The model is as follows: Y=i=1naixi+u Y = \sum\limits_{i = 1}^n {{a_i}{x_i} + u} where Y is the dependent variable, ai is the coefficient of the i-th influencing factor, xi is the i-th influencing factor and u is the constant term of the regression equation.

Structural equation modelling

In quantitative research, SEM aims to solve practical problems through certain statistical analysis technology dealing with the theoretical model of complex phenomena and to evaluate the theoretical model according to the consistency between the theoretical model and the actual data. It is divided into the measurement and structural models. The measurement model is used to analyse the relationship between the observation index and latent variable, while the structural model deals with the relationship among different latent variables. For the studied problems, the phenomena that could not be measured directly are recorded as latent variable or hidden variable, while the variables that can be measured directly are manifest variable or explicit variable. The measurement model, also known as the confirmatory factor analysis model, mainly represents the relationship between the manifest variable and latent variable.

The measurement model is generally composed of two equations, which respectively shows the relationship between potential endogenous variable η and explicit endogenous vector Y (i.e. manifest variable), and between potential exogenous variable ξ and explicit exogenous vector X. The mathematical expression of SEM is: x=Xξ+δ {\rm{x}} = {\wedge _X}\xi + \delta y=Yη+ε {\rm{y}} = {\wedge _Y}\eta + \varepsilon where X, y ξ and η are the vectors of p × 1 order exogenous manifest variable, q × 1 order endogenous manifest variable, m × 1 order exogenous latent variable (potential independent variable), and n × 1 order endogenous latent variable (potential dependent variable) respectively; ΛX is the p × m order matrix, which is the factor loading matrix of the exogenous manifest variable x on the exogenous latent variable ξ; ΛY is the q × n-order matrix, which is the factor loading matrix of the endogenous manifest variable y on the endogenous latent variable η; δ is the p × 1-order measurement error vector, ε is the q × 1-order measurement error vector, δ and ε shows that which cannot be explained by the latent variables.

The structural equation model, also known as the causality model among latent variables, mainly demonstrates the relationship among latent variables. It defines the causality between the exogenous potential variables and the endogenous potential variables in the studied system. The model form is as follows: η=βη+Γξ+ς \eta = \beta \eta + \Gamma \xi + \varsigma where η is the endogenous latent variable vector; ξ is the exogenous latent variable vector; β is the coefficient matrix of the endogenous latent variable η, and the path coefficient matrix among the endogenous latent variables, Γ is the coefficient matrix of the exogenous latent variable ξ, and the path coefficient matrix of the exogenous latent variable to the corresponding endogenous latent variable; and ζ is the residual vector, which cannot be explained in the model [22].

In this study, 409 valid questionnaires from all districts in Beijing were collected. Based on the questionnaires, binary logistic regression analysis and SEM analysis were carried out.

Results
Binary logistic regression analysis
Variable assignment

In order to improve regression analysis, seven independent variables are assigned with the willingness of transfer as the dependent variable, as shown in Table 1.

Variable assignment

Variable Meaning Value range Variable definition
x1 Behavioural attitude 1–5 Individual cognition and evaluation of decision execution
x2 Subjective norm 1–5 Social pressure on individual when it comes to perform this behaviour
x3 Perceived behavioural control 1–5 The difficulty the individual perceives when taking an action
x4 Gender 1–2 Male = 1, female = 2
x5 Age 1–6 Under 30 years old = 1; 30–40 years old = 2; 40–50 years old = 3; 50–60 years old = 4; 60–70 years old = 5; over 70 years old = 6
x6 Family population 1–7 1 person = 1; 2 persons = 2; 3 persons = 3; 4 persons = 4; 5 persons = 5; 6 persons = 6; 6 persons or more = 7
x7 Average annual household income 1–4 <20,000 yuan = 1; 20,001–30,000 yuan = 2; 30,001–50,000 yuan = 3; 50,000 yuan above = 4
Analysis of logistic regression results

In this section, SPSS19.0 was used for data calculating and processing, and logistic model regression test was carried out. The test results are shown in Table 2.

Regression test of logistics model

Observed Predicted
Transaction intention Percentage correction
1.00 2.00

Transaction intension 1.00 353 4 98.9
2.00 52 0 .0
Total percentage 86.3

Table 2 shows that based on the established model, the prediction accuracy of the data is 86.3%, which is far greater than the recommended value of 60%, indicating that the model has a high fitting degree and accuracy. The multicollinearity test was carried out for the multivariables in Table 1, and the results are shown in Table 3.

Analysis of variance inflation factor

Variable Collinearity statistics

Tolerance VIF value

Behavioural attitude 0.371 2.693
Subjective norm 0.363 2.758
Perceived behavioural control 0.373 2.678
Gender 0.860 1.163
Age 0.986 1.014
Family population 0.990 1.010
Average annual household income 0.984 1.016
Mean value 1.762

From Table 3, it can be seen that the mean value of the variance inflation factor (VIF) is 1.762, and that no independent variable VIF value is >10, so there is no big multicollinearity problem among the above model variables.

Finally, according to the variable setting in Table 1, the binary logistic regression analysis of the dependent variable, the willingness of the farmers’ forest land management rights transfer, is carried out and the regression results are shown in Table 4.

Regression results of logistics model of circulating will of forest land’s management right

Variable Coefficient Standard error Wals value Sig. value Exp (B)

Behavioural attitude −0.723** 0.245 8.711 0.003 0.485
Subjective norm −0.477** 0.236 4.069 0.044 0.621
Perceived behavioural control 0.017 0.253 0.004 0.947 1.017
Gender −0.283 0.346 0.672 0.412 0.753
Age 0.131 0.193 0.463 0.496 1.140
Family population 0.044 0.145 0.091 0.763 1.045
Average annual household income −0.019 0.131 0.021 0.885 0.981
Constant term −2.508*** 0.759 10.916 0.001 0.081

<10% significance level

<5% significance level

<1% significance level

It can be seen from Table 4 that the p-values of behavioural attitude and subjective norm are 0.003 and 0.044, <0.05, indicating that behavioural attitude and subjective norm have a significant impact on the willingness of forest land management rights transfer; while the p-values of gender, age, family population and perceived behavioural control are >0.05, indicating that these factors do not have a significant impact on the willingness of forest land management rights transfer.

The coefficient of behavioural attitude x1 is −0.723, and the wals value is 8.711, which shows that the variables are statistically significant at the level of 5%. When the rating of farmers’ behavioural attitude increases by 5%, the possibility of farmers increasing their willingness of the forest land management rights transfer is reduced by 0.723%. The estimated coefficient of subjective x2 norm is −0.477, and the wals value is 4.069, which is also statistically significant at the level of 5%. When the score of farmers’ subjective norm increases by 5%, the possibility of their willingness of the forest land management rights transfer is reduced by 0.477%.

The estimated value of perceived behavioural control x3 is 0.017, and the wals value is 0.004, which is not significant at the level of 10%. This shows that the willingness of transfer does not change significantly with the change in perceived behavioural control. Similarly, the regression results show that the willingness transfer is not significantly affected by the changes in gender, age, family population and average annual household income. This shows that the perceived difficulty of farmers in the implementation of the forest land management rights transfer does not significantly affect their transfer. The regression results show no significant effect of average annual household income level on the willingness of transfer, which is not consistent with the existing research conclusions. This may be because the age and income varying from the respondents are not random enough.

After the non-significant factors are eliminated, the final regression results of the model are as shown in Table 5.

Regression results of logistics model of forest land’s management rights circulating will

Variable Coefficient Standard error Wals value Sig. value Exp (B)

Behavioural attitude −0.701 0.229 9.367 0.002 0.496
Subjective norm −0.499 0.218 5.223 0.022 0.607
Constant term −2.312 0.203 129.397 0.000 0.099

The regression coefficients of behavioural attitude and subjective norm are −0.701 and −0.499. The two variables have a negative significant impact on the willingness of forest land management rights transfer. The transfer of forest land management rights includes the inflow and outflow of forest land. In order to facilitate the willingness of farmers’ forest land management rights transfer in Beijing, we need to reduce their behavioural attitude scores, that is, reduce farmers’ expectations in the results of the transfer and their estimation of the importance of the results. We also need to reduce their subjective norm scores, that is, reduce the social pressure of farmers on whether to implement the transfer of forest land management rights.

Structural equation modelling analysis
Model building

AMOS22.0 and SPSS19.0 were applied for data calculation and processing. The constructed SEM is shown in Figure 1, including four potential variables: behavioural attitude, subjective norm, perceived behavioural control and willingness of transfer, which correspond to multiple observation variables, respectively. The questionnaire of this study was designed based on this model.

Fig. 1

SEM model. SEM, structural equation modelling

Descriptive statistics of measured variables

The descriptive statistics of potential variables of SEM are shown in Table 6.

Descriptive statistics of measured variables

Measured variables Minimum Maximum Mean Standard deviation Skewness Kurtosis Mean of variables

Willingness of forest land management rights transfer Intention of forest land management rights transfer 1 5 2.74 0.816 0.665 0.221 2.558
Willingness to transfer forest rights 1 3 1.47 0.741 0.549 1.197
Mode of willingness 1 7 2.6 1.717 2.947 1.225
Difficulty degree of forest land management rights transfer 1 6 3.42 1.109 1.229 0.506
Behavioural attitude A better understanding of the transfer of forest land management rights 1 5 2.9 1.008 −0.172 −0.527 3.387
Thinking of the transfer of forest land management rights can bring more income 1 5 3.53 0.773 −0.526 0.363
Satisfied with the existing forest land policy 1 5 3.3 0.798 −0.1 0.196
Considering the problems encountered in the process of forestry production are difficult to solve, and flowing out of the forest land 1 5 3.27 0.827 −0.391 0.249
Considering the existing forest land does not meet its own needs, and flowing into the forest land 1 5 3.52 0.968 −0.078 −0.417
Believing the transfer of forest land management rights will be an important development trend 1 5 3.8 0.903 −0.321 −0.415
Subjective norm Simplicity and convenience of the transfer procedure of forest land management rights is 1 5 3.24 1.032 0.26 −0.54 3.285
Willingness of relatives and friends to transfer the management rights of forest land 1 5 3.16 0.964 −0.427 −0.057
Encouragement from governments and communities on the transfer of forest land management rights 1 5 3.52 0.985 −0.073 −0.441
Relatives and friends’ rewards from the transfer of forest land management rights 1 5 3.22 0.922 −0.459 −0.058
Perceived behavioural control Easy to find the information about the transfer of forest land management rights 1 5 3.14 1.16 0.076 −0.652 3.243
High forest land management technology to support forest land inflow 1 5 3.13 0.975 −0.304 −0.293
Sufficient labour to support forest land inflow 1 5 3.31 1.108 0.015 −0.696
Able to withstand the risks brought by the transfer of forest land management rights 1 5 3.15 0.913 −0.261 −0.129
Forced to flow out forest land due to insufficient labour 1 5 3.42 1.05 −0.095 −0.504
Less forest resources and poor quality 1 5 3.31 0.925 −0.332 −0.031
Data analysis of structural equation modelling
Reliability test

The combined reliability test was performed based on the questionnaire data. According to Table 7, the composite reliability coefficient (CR) value of each variable is >0.6, and the other variables have passed the reliability test requirements except for the average variance extracted (AVE) of behavioural attitude value <0.5. Although the AVE value of behavioural attitude is <0.5, its Cronbach’s α is close to 0.8, hence its reliability is relatively acceptable.

Composite reliability test

Measured variables Z-value p Std SMC CR AVE Cronbach’s α

Behavioural attitude Understanding the transfer of forest land management rights 0.596 0.404 0.777 0.374 0.777 0.745
Increasing income 0.101 *** 0.434 0.566 0.766
Satisfied with existing policies 0.081 *** 0.719 0.281 0.753
Production problems 0.079 *** 0.632 0.368 0.753
Unsatisfied demand 0.074 *** 0.806 0.194 0.709
Important development trend 0.102 *** 0.57 0.43 0.732
Subjective norm Simple and convenient procedure 0.05 *** 0.532 0.468 0.825 0.543 0.831 0.790
Willingness of the relatives and friends 0.051 *** 0.399 0.601 0.792
Encouragement from the government on the transfer of forest land management rights 0.053 *** 0.572 0.428 0.773
Benefits for relatives and friends 0.326 0.674 0.789
Perceived behavioural control Relevant information 0.337 0.663 0.862 0.515 0.864 0.835
Business and technical support 0.052 *** 0.358 0.642 0.841
Labour support 0.044 *** 0.502 0.498 0.829
Risks 0.047 *** 0.506 0.494 0.839
Labour shortage 0.051 *** 0.504 0.496 0.839
Less forest resources and poor quality 0.047 *** 0.706 0.294 0.860

Total 0.924

AVE, average variance extracted; CR, composite reliability coefficient

Validity test

Table 8 shows the KMO value of the scale and the p value of Bartlett spherical test, both of which have passed the test, so the scale has good validity.

KMO value and Bartlett spherical test

Latent variable Number of terms KMO value Bartlett spherical test value p

Willingness of forest land’s management rights transfer 4 0.589 48.101 ***
Behavioural attitude 6 0.809 582.585 ***
Subjective norm 4 0.783 609.625 ***
Perceived behavioural control 6 0.851 1080.041 ***
Total 20 0.923 3624.319 ***
Path analysis

Based on TPB, path analysis is conducted between the three latent variables of behavioural attitude, subjective norm and perceived behavioural control and the variables of transfer willingness. The revised path coefficients and significance are as shown in Table 9. The path coefficients in Table 9 show that behavioural attitude has a significant negative impact on the willingness while the perceived behavioural control does not have a significant impact on the willingness, which is consistent with the results of logistic regression analysis. However, according to the structural equation analysis results, the conclusion that the subjective norm obtained by Logistic regression analysis has a significant negative effect on the willingness of forest land management rights transfer is verified.

Revised path coefficient and its significance level

Path Standardised path coefficient Significant

Behavioural attitude Transfer willingness −0.752 ***
Subjective norm Transfer willingness Not significant
Perceived behavioural control Transfer willingness Not significant
Discussion and conclusion

Based on the TPB, this paper conducted a questionnaire survey on farmers’ willingness for forest land management rights transfer in Beijing, and 409 valid data were obtained. Using two analytical methods of binary logistic regression and SEM, this paper explored the influence mechanism of such variables as farmers’ behavioural attitude, subjective norm and perceived behavioural control on their willingness of transfer.

The main conclusions are as follows:

The behavioural attitude of farmers has a significant negative impact on the willingness of forest land management rights transfer. The less the farmers underestimate the results of the forest land’s management rights transfer, the more conducive it is to the inflow and outflow of forest land. Therefore, when farmers have a better understanding of the transfer policy of forest land management rights, and believe that it can bring more income to them, they are very satisfied with the existing forest land policy. When they think the problems encountered in the process of forestry production are difficult to solve, and the existing forest land does not meet their own needs, they will think that the transfer will be an important development trend. These above factors will increase the willingness of forest land management rights transfer. We suggest that we should pay attention to the change in farmers’ behavioural attitude, try to reduce the impact of the results of transfer on them, weaken their dependence on the ownership of forest land, improve the transfer rate of forest land management rights and better allocate the forest land management rights.

There is no significant effect of farmers’ perceived behavioural control on the willingness of forest land management rights transfer. Farmers’ perception of the difficulty of transfer would not significantly affect their decision-making. Age, family population and average annual household income do not significantly affect the willingness of transfer, which is not consistent with the results of previous studies that family situation will affect their willingness. This may be due to the lack of randomness of the questionnaire sample in this study, or the different circumstances of the transfer of forest land management rights in Beijing, which requires further research.

The results for analysing the impact of subjective norm on the willingness of forest land management rights transfer in the two models are different. The results of binary logistic regression show that the subjective norm has a significant negative impact on the willingness of forest land management rights transfer. However, the results of SEM show that the subjective norm has no significant impact on the willingness of transfer. In the analysis, the model fitting degree of binary logistic regression analysis is higher, while that of SEM is relatively moderate. The former results are more reliable and more consistent with the TPB. However, the impact of subjective norm on the willingness of transfer still needs further verification. The government should try to reduce the pressure that farmers feel when they carry out transfer, be it from relatives, friends or the government policies. The government needs to standardise and simplify the flow of forest land management rights, encourage the transfer of forest land’s management rights, reduce the pressure of farmers and protect their interests.

Fig. 1

SEM model. SEM, structural equation modelling
SEM model. SEM, structural equation modelling

Composite reliability test

Measured variables Z-value p Std SMC CR AVE Cronbach’s α

Behavioural attitude Understanding the transfer of forest land management rights 0.596 0.404 0.777 0.374 0.777 0.745
Increasing income 0.101 *** 0.434 0.566 0.766
Satisfied with existing policies 0.081 *** 0.719 0.281 0.753
Production problems 0.079 *** 0.632 0.368 0.753
Unsatisfied demand 0.074 *** 0.806 0.194 0.709
Important development trend 0.102 *** 0.57 0.43 0.732
Subjective norm Simple and convenient procedure 0.05 *** 0.532 0.468 0.825 0.543 0.831 0.790
Willingness of the relatives and friends 0.051 *** 0.399 0.601 0.792
Encouragement from the government on the transfer of forest land management rights 0.053 *** 0.572 0.428 0.773
Benefits for relatives and friends 0.326 0.674 0.789
Perceived behavioural control Relevant information 0.337 0.663 0.862 0.515 0.864 0.835
Business and technical support 0.052 *** 0.358 0.642 0.841
Labour support 0.044 *** 0.502 0.498 0.829
Risks 0.047 *** 0.506 0.494 0.839
Labour shortage 0.051 *** 0.504 0.496 0.839
Less forest resources and poor quality 0.047 *** 0.706 0.294 0.860

Total 0.924

Descriptive statistics of measured variables

Measured variables Minimum Maximum Mean Standard deviation Skewness Kurtosis Mean of variables

Willingness of forest land management rights transfer Intention of forest land management rights transfer 1 5 2.74 0.816 0.665 0.221 2.558
Willingness to transfer forest rights 1 3 1.47 0.741 0.549 1.197
Mode of willingness 1 7 2.6 1.717 2.947 1.225
Difficulty degree of forest land management rights transfer 1 6 3.42 1.109 1.229 0.506
Behavioural attitude A better understanding of the transfer of forest land management rights 1 5 2.9 1.008 −0.172 −0.527 3.387
Thinking of the transfer of forest land management rights can bring more income 1 5 3.53 0.773 −0.526 0.363
Satisfied with the existing forest land policy 1 5 3.3 0.798 −0.1 0.196
Considering the problems encountered in the process of forestry production are difficult to solve, and flowing out of the forest land 1 5 3.27 0.827 −0.391 0.249
Considering the existing forest land does not meet its own needs, and flowing into the forest land 1 5 3.52 0.968 −0.078 −0.417
Believing the transfer of forest land management rights will be an important development trend 1 5 3.8 0.903 −0.321 −0.415
Subjective norm Simplicity and convenience of the transfer procedure of forest land management rights is 1 5 3.24 1.032 0.26 −0.54 3.285
Willingness of relatives and friends to transfer the management rights of forest land 1 5 3.16 0.964 −0.427 −0.057
Encouragement from governments and communities on the transfer of forest land management rights 1 5 3.52 0.985 −0.073 −0.441
Relatives and friends’ rewards from the transfer of forest land management rights 1 5 3.22 0.922 −0.459 −0.058
Perceived behavioural control Easy to find the information about the transfer of forest land management rights 1 5 3.14 1.16 0.076 −0.652 3.243
High forest land management technology to support forest land inflow 1 5 3.13 0.975 −0.304 −0.293
Sufficient labour to support forest land inflow 1 5 3.31 1.108 0.015 −0.696
Able to withstand the risks brought by the transfer of forest land management rights 1 5 3.15 0.913 −0.261 −0.129
Forced to flow out forest land due to insufficient labour 1 5 3.42 1.05 −0.095 −0.504
Less forest resources and poor quality 1 5 3.31 0.925 −0.332 −0.031

Variable assignment

Variable Meaning Value range Variable definition
x1 Behavioural attitude 1–5 Individual cognition and evaluation of decision execution
x2 Subjective norm 1–5 Social pressure on individual when it comes to perform this behaviour
x3 Perceived behavioural control 1–5 The difficulty the individual perceives when taking an action
x4 Gender 1–2 Male = 1, female = 2
x5 Age 1–6 Under 30 years old = 1; 30–40 years old = 2; 40–50 years old = 3; 50–60 years old = 4; 60–70 years old = 5; over 70 years old = 6
x6 Family population 1–7 1 person = 1; 2 persons = 2; 3 persons = 3; 4 persons = 4; 5 persons = 5; 6 persons = 6; 6 persons or more = 7
x7 Average annual household income 1–4 <20,000 yuan = 1; 20,001–30,000 yuan = 2; 30,001–50,000 yuan = 3; 50,000 yuan above = 4

Regression results of logistics model of circulating will of forest land’s management right

Variable Coefficient Standard error Wals value Sig. value Exp (B)

Behavioural attitude −0.723** 0.245 8.711 0.003 0.485
Subjective norm −0.477** 0.236 4.069 0.044 0.621
Perceived behavioural control 0.017 0.253 0.004 0.947 1.017
Gender −0.283 0.346 0.672 0.412 0.753
Age 0.131 0.193 0.463 0.496 1.140
Family population 0.044 0.145 0.091 0.763 1.045
Average annual household income −0.019 0.131 0.021 0.885 0.981
Constant term −2.508*** 0.759 10.916 0.001 0.081

Revised path coefficient and its significance level

Path Standardised path coefficient Significant

Behavioural attitude Transfer willingness −0.752 ***
Subjective norm Transfer willingness Not significant
Perceived behavioural control Transfer willingness Not significant

Regression test of logistics model

Observed Predicted
Transaction intention Percentage correction
1.00 2.00

Transaction intension 1.00 353 4 98.9
2.00 52 0 .0
Total percentage 86.3

Regression results of logistics model of forest land’s management rights circulating will

Variable Coefficient Standard error Wals value Sig. value Exp (B)

Behavioural attitude −0.701 0.229 9.367 0.002 0.496
Subjective norm −0.499 0.218 5.223 0.022 0.607
Constant term −2.312 0.203 129.397 0.000 0.099

Analysis of variance inflation factor

Variable Collinearity statistics

Tolerance VIF value

Behavioural attitude 0.371 2.693
Subjective norm 0.363 2.758
Perceived behavioural control 0.373 2.678
Gender 0.860 1.163
Age 0.986 1.014
Family population 0.990 1.010
Average annual household income 0.984 1.016
Mean value 1.762

KMO value and Bartlett spherical test

Latent variable Number of terms KMO value Bartlett spherical test value p

Willingness of forest land’s management rights transfer 4 0.589 48.101 ***
Behavioural attitude 6 0.809 582.585 ***
Subjective norm 4 0.783 609.625 ***
Perceived behavioural control 6 0.851 1080.041 ***
Total 20 0.923 3624.319 ***

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