Effects of conformity tendencies on households’ willingness to adopt energy utilization of crop straw: Evidence from biogas in rural China

Renewable Energy 138 (2019) 573e584

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Effects of conformity tendencies on households’ willingness to adopt energy utilization of crop straw: Evidence from biogas in rural China Yangmei Zeng a, b, Junbiao Zhang a, b, *, Ke He a, b, ** a b

College of Economics & Management, Huazhong Agricultural University, Wuhan, Hubei 430070, China Hubei Rural Development Research Center, Wuhan, Hubei 430070, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 May 2018 Received in revised form 2 January 2019 Accepted 1 February 2019 Available online 1 February 2019

This paper examines the impacts of conformity tendencies (including conformity to rich villagers, conformity to relatives, conformity to neighbors and conformity to village cadres) on households’ willingness to adopt energy utilization of crop straw exampled by biogas in rural areas in China. Particularly, to address estimation errors caused by possible sample self-selection biases, propensity score matching is employed to further ensure the robustness of the regression results obtained by Binary Logistic model. The empirical results highlight that, in contrast to the significantly negative impact of conformity to rich villagers, conformity to relatives, neighbors and village cadres all have positive and statistically significant influences on households’ willingness to adopt energy utilization of crop straw exampled by biogas. These findings suggest the potential importance of providing households with quick and convenient access to the relatives’, neighbors’ and village cadres’ adoption information of energy utilization of crop straw exampled by biogas but carefully filtering out rich villagers’ adoption information in promoting energy utilization of crop straw exampled by biogas in rural areas. © 2019 Published by Elsevier Ltd.

Keywords: Conformity tendency Willingness to adopt Energy utilization of crop straw Biogas Propensity score matching

1. Introduction With the growing shortage of global energy and resources and the aggravation of environmental pollution, developing and promoting alternative energy technologies is considered vital to green and sustainable development [1]. As an important organic resource in many developing countries, crop straw could also be utilized as an energy source, which could not only solve the problem of environmental pollution caused by straw burning but also “turn waste into treasure” to achieve the recycling of agricultural waste [2]. Specific to developing countries, biogas produced from crop straw, as a kind of energy utilization of crop straw, has been commonly existed [3]. As an increasingly attractive renewable energy resource, biogas made from crop straw has great health, social and economic benefits [4], and more importantly, the application of biogas made from

* Corresponding author. College of Economics & Management, Huazhong Agricultural University, Wuhan, Hubei 430070, China. ** Corresponding author. College of Economics & Management, Huazhong Agricultural University, Wuhan, Hubei 430070, China. E-mail addresses: [email protected] (J. Zhang), [email protected] (K. He). https://doi.org/10.1016/j.renene.2019.02.003 0960-1481/© 2019 Published by Elsevier Ltd.

crop straw has obvious environmental benefit on the grounds that it could alleviate environmental pollution and forest destruction caused by the use of biomass fuel sources, such as wood [5]. However, despite the diverse benefits of biogas made from crop straw, the adoption rate of it in rural areas in developing countries is still low [6], which could affect the advancement of energy utilization of crop straw and go against the agricultural sustainable development. Until now, research efforts have led to many studies on biogas made from crop straw to explore the determinants of households’ adopting biogas technology especially in developing countries. Firstly, individual characteristics (e.g. gender, age, educational attainment, etc.) have been identified as influencing households’ choices of biogas technology [7e9], though the research results are not consistent. For example, the studies by Kabir et al. [7] on Bangladesh and Kelebe et al. [8] on Tigray in Ethiopia have found the significantly positive effects of age of household head, level of education, and female-headed households on households’ biogas adoption decisions; in contrast, Mengistu et al. [9] have concluded that female-headed households are less likely to adopt biogas technology than male-headed households. Secondly, household characteristics (e.g. total household annual income, land acreage, etc.) have been proved to play a role in households’ responses to the

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biogas technology [9e11]. For instance, both Mengistu et al. [9] and Mwirigi et al. [10] have revealed that family income and farmland size could greatly encourage households’ participation in biogas utilization; the works of Mengistu et al. [9] and Katuwal and Bohara [11] even confirm that household size, representing householdlabor power, is an important predictor of the use of biogas technology. Thirdly, personal perceptions and aversions are also important determinants [12,13]. The literature as exemplified by Hassan et al. [12] indicates that personal perceptions of various interventions and alike may help to explain households’ use of bioenergy resources (e.g. biogas), and Shi and Gill [13] have found that households’ risk aversion is a major barrier to the adoption of agricultural practice like biogas project. Fourthly, situational factors such as external institution and infrastructure conditions and some other factors are also ascribed to households’ use of biogas technology [9,14e16]. Specifically, distance to fuelwood sources [9], access to credit [14] and zero grazing farming systems [15] as well as the institutional structure and maintenance service gaps [16] significantly influence households’ adoption of biogas technology. As alluded to above, previous studies have mainly focused on the influences of individual and household characteristics, individuals’ perceptions and aversions, and situational factors on households’ adoption of biogas technology, with little work addressing the impacts of conformity tendencies on households’ willingness to adopt energy utilization of crop straw exampled by biogas, and especially the effects of different types of conformity tendencies are unclear with significantly less research focus. In fact, in many developing countries, especially in China, information asymmetry resulting from difficult access to relevant extension services and other information sources is of particular existence [17,18]. Under this circumstance, individuals’ opinions or behaviors are likely to be influenced by the behaviors and opinions of others such as neighbors in their immediate environment [19]. As a consequence, conformity tendencies or behaviors tend to arise to reduce the uncertainty of individual’s decisions [20e22]. To our best knowledge, prior studies have already confirmed the conformity effect on the adoption of other kinds of technologies [23,24]. For example, conformity behavior may arise not only in IT technology adoption because of informational cascades [23], but also in seed choice due to the impossibility of trialing the seeds [24]. So, do conformity tendencies influence households’ willingness to adopt energy utilization of crop straw exampled by biogas? This is a relatively overlooked research question in the context of promoting energy utilization of crop straw exampled by biogas in rural areas in China with difficulties. Therefore, to bridge this gap, household data surveyed from rural areas in eight provinces in China are used to study the impacts of conformity tendencies, including conformity to relatives, neighbors, rich villagers and village cadres, on households’ willingness to adopt energy utilization of crop straw exampled by biogas. This paper provides three possible contributions: First, on the basis of representative data at the household level, this study incorporates conformity tendencies (including conformity to relatives, neighbors, rich villagers and village cadres) into factors influencing households’ willingness to adopt energy utilization of crop straw exampled by biogas and provides fresh insights into the link between them. Second, to avoid estimation errors caused by sample selective biases, propensity score matching (PSM) is employed to compare households with conformity tendencies to otherwise similar ones without conformity tendencies. Third, the focus of this study is the biogas made from crop straw in China, one of the developing countries in the world, which is quite different from that in developed countries [25]. Therefore, despite the fact that the results concluded in this paper are somewhat specific to the rural areas under consideration, the policy suggestions put

forward apply equally to other rural areas in other developing countries where the promotion of energy utilization of crop straw exampled by biogas is an issue. Specifically, it is found that conformity to relatives, neighbors and village cadres all could significantly increase the likelihood of households’ willingness to adopt biogas made from crop straw, while conformity to rich villagers has a significantly negative impact. The rest of this paper is organized as follows: Section 2 describes the situation of biogas as a kind of energy utilization of crop straw in China; Sections 3 defines the definition of energy utilization of crop straw and biogas made from crop straw and then puts forward the hypotheses; Section 4 describes the data and introduces the relevant estimation procedure; Section 5 reveals the estimation results and the final section concludes and puts forward some policy implications. 2. Energy utilization of crop straw exampled by biogas in China China offers a particularly interesting case study for presenting and discussing the concerns. As a large agricultural country in the world, China is abundant in crop straw resources, with the fact that the yield of crop straw has increased at a rate of 1.4% annually [26], and that in 2014, around 0.62 billion tons of crop straw resources can be collected [27]. Therefore, the potential to produce biogas from crop straw is significant. According to The National Rural Biogas 13th Five-year Plan, 0.18 billion tons of crop straw resources in 2015 in China could be used to produce biogas, and the capacity of biogas production could reach 50 billion cubic meters. Being a renewable energy source, biogas made from crop straw has been attached great importance to by the Chinese government. For instance, in 2014, The Energy Development Strategy Action Plan (2014e2020) released by the General Office of the State Council explicitly proposes to develop the energy utilization of crop straw, notably biogas production, according to local conditions; in 2017, Guiding Opinions on the Construction of Clean Energy Utilization Project of Crop Straw for Gasification clearly emphasizes that the pyrolysis technology and anaerobic fermentation technology should be used to produce biogas (heat, electricity) from agricultural residue with crop straw as the main materials. Besides, the government sectors have set great targets for the rural biogas consumption. Specifically, according to China’s Renewable Energy Medium and Long Term Planning, by 2020, the goal that around 80 million households use biogas should be achieved. In practice, the Chinese government has invested 2.5 billion RMB annually in a rural biogas program [28], and consistent with national policies, the provincial governments of the mainland China, have started the construction of biogas pools and provided special subsidy to participants since 2003 [29]. With the numerous efforts made by national and provincial sectors, although so far the biogas production in China has seen a rapid rise, yet the biogas use in rural China still remains below expectations [26,27]. For instance, Chen et al. [30] have found that, in rural China, about 19% of the potential of biogas has been used; Zhang et al. [31] have revealed that, in 2015, only 14.39% of households surveyed in Wuhan City, Hubei province, adopted biogas made from crop straw. These figures signify that biogas made from crop straw has not fully played its proper role. Just as Li et al. [32] note, due to the poor environment in China’s rural areas, training and mentoring are difficult in rural regions with few personnel engaged in the promotion of biogas made from crop straw and the inadequate service system. The result is that households in rural areas have little knowledge about the use of biogas [32], under the circumstance of which, conformity effect is likely to happen [21]. Therefore, this paper attempts to explore the

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impacts of conformity tendencies on households’ willingness to adopt energy utilization of crop straw with biogas as an example with propensity score matching employed in the hope of offering valuable references for the establishment and implementation of related policies. It should be noted that, in rural China, the relevant government plans implemented in each village are not only the same, but also fixed and homogenous for each household. Therefore, it is assumed that the related governmental plans can not interfere in the current approach in this paper. 3. Conceptual definition and hypotheses 3.1. Definition of energy utilization of crop straw like biogas Energy utilization of crop straw refers to that wheat, rice and other crop straw resources are transformed into convenient and accessible energy for diverse applications through a series of scientific processes. In this paper, biogas made from crop straw is the focus. Biogas produced from crop straw, as an attractive renewable energy source, is just one kind of energy utilization of crop straw, and it particularly refers to a methane-rich gas generated through thermo chemical breakdown of crop straws in anaerobic conditions [33]. It is functionally diverse for various purposes including cooking, heating, fueling, power generation etc., and it exerts a substantial role in improving energy structure, environmental quality and health conditions, and reducing greenhouse gases emissions as well as promoting economic and social sustainable development in rural areas [34]. 3.2. Hypotheses Research on social influence has proved that individual’s intentions, judgments and decisions have been often significantly influenced by the decisions of others in the same environment [35,36]. In other words, individual’s willingness and choices are more or less impacted by the willingness or choices of other people in many situations ranging from economic circumstances to social circumstances. In the setting of information asymmetry and uncertainty of choice, private information individuals hold could be regarded as a signal about the utility of their actions, and so the decisions individuals make are the result of their beliefs revised by integrating the perspectives and actions of others. As Abrahamson [37] notes, conformity effects occur when people use their observations of others to update their own private beliefs and to take actions. Conformity means that all decision-makers rapidly converge toward the same decision simply because they saw others make that decision [38], and it is a typical mechanism to explain the phenomenon of similar observed outcomes, especially the general adoption of agricultural technologies in rural areas. However, the influence of different referents such as relatives, neighbors, rich villagers and village cadres on individual’s conformity tendency has been proved to be different. For instance, in Central Mexico, it is relatives that some farmers relied heavily on as a source of promotion [39]; in India, farmers are more likely to do what a small set of progressive farmers do in the choice of BT cotton [40]. Hence, this paper attempts to explore the effects of conformity to relatives, neighbors, rich villagers and village cadres on households’ willingness to adopt energy utilization of crop straw exampled by biogas. 3.2.1. Conformity to relatives It is common that relatives have the blood relationships with household members, and to a large extent, households generally refer to their relatives’ opinions and behaviors when making decisions. The literature as exemplified by Kassie et al. [41] views that

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households seem to be more likely to adopt new technologies when they have a large number of relatives who have experimented with those technologies without excessive exposure to risk. Similarly, Mengistu et al. [9] confirm that, with a “wait and see” principle in mind, farmers seem to be quite conscious of the use of biogas technology by others such as relatives. Therefore, in this paper, the impact of conformity to relatives on households’ willingness to adopt energy utilization of crop straw exampled by biogas is explored and hypothesis is put forward as follows: H1. Conformity to relatives could exert a significant influence on households’ willingness to adopt energy utilization of crop straw exampled by biogas.

3.2.2. Conformity to neighbors Neighbors under similar circumstances are another important source of information about technology adoption. As Rogers [42] notes, it is the “hemophilic neighbors” that households tend to learn from because they are individuals with whom households have close social ties and share common professional and personal characteristics. Also, Conley and Udry [43] have demonstrated that, in the absence of learning, households may also act like their neighbors on the grounds that they have interdependent preferences, or for they are subject to related unobservable shocks. Actually, Krishnan and Patnam [44] have already proved that learning from adopting neighbors is an crucial driver of the spread of fertilizer and improved seeds. Therefore, in this paper, the impact of conformity to neighbors on households’ willingness to adopt energy utilization of crop straw exampled by biogas is explored and hypothesis is put forward as follows: H2. Conformity to neighbors could exert a significant influence on households’ willingness to adopt energy utilization of crop straw exampled by biogas.

3.2.3. Conformity to rich villagers Rich villagers are a typical group of villagers who are usually regarded as able persons, and they are easily to became the object others in the same village would learn from since getting rich is the common goal of the majority of households in rural areas in developing countries. Among the study of technology adoption, Foster and Rosenzweig [45] have found that the rate of households technology adoption is importantly influenced by the production wealth of other relatively rich farmers in the village, which is also consistent with a view that the poor farmers generally follow the rich ones in this process of capacity building [46]. Therefore, in this paper, the impact of conformity to rich villagers on households’ willingness to adopt energy utilization of crop straw exampled by biogas is explored and hypothesis is put forward as follows: H3. Conformity to rich villagers could exert a significant influence on households’ willingness to adopt energy utilization of crop straw exampled by biogas.

3.2.4. Conformity to village cadres Village cadres, as the representative of the Government, is a small group of villagers with special status who are normally engaged in more social events than other farmers in the village. As the objects that individuals conform to are usually those who are considered to be able to make better decisions and have more information [47], the adoption behaviors of village cadres are likely to be the object that households in village conform to in that village cadres often have relatively more technology information than other farmers and that their actions are usually perceived to be

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legitimate. In fact, it has already been proved that, in the process of adopting sustainable production technologies, households are largely influenced by such factors as village cadres’ behaviors [48]. Therefore, in this paper, the impact of conformity to village cadres on households’ willingness to adopt energy utilization of crop straw exampled by biogas is explored and hypothesis is put forward as follows: H4. Conformity to village cadres could exert a significant influence on households’ willingness to adopt energy utilization of crop straw exampled by biogas. 4. Data and methodology 4.1. Source of data 4.1.1. Questionnaire design The data set used in this study is from surveys in rural areas in China. To obtain the data, a detailed and structured questionnaire for this study had been carefully designed and modified repeatedly by relevant experts invited. Additionally, a pre-test was carried out and some of the interview questions were revised based on the results of the pre-test to ensure the validity, reliability and accuracy of the final version. With the focus of biogas made from crop straw, the final questionnaire provides detailed information about the attributes of individuals including gender, age and educational attainment etc. and features of households like household labor, land acreage, total household annual income, etc. Furthermore, more emphasis in the final version is given to individual’s conformity tendencies and their willingness to adopt energy utilization of crop straw exampled by biogas. As for individual’s conformity tendencies, respondents are asked to answer questions like whether they would like to conform to their relatives, neighbors, rich villagers and village cadres respectively in terms of adopting energy utilization of crop straw exampled by biogas. Besides, the final version not only evaluates the general situation of crop straw management in rural areas, but also provides further information about individual’s perceptions of environmental protection, income and rural developing and their time and cost risk aversions towards energy utilization of crop straw exampled by biogas. 4.1.2. Data collection The data used in this paper are derived from face-to-face surveys in rural areas randomly selected in Liaoning, Jilin and Heilongjiang provinces in Northeastern region, Shandong province in Eastern region, and Hubei and Anhui provinces in Central region as well as Sichuan and Guizhou provinces in Western region in China. The areas surveyed are randomly selected for two reasons: (1) distributed in the eastern, central, western and northeastern regions, these provinces where the rural areas surveyed have covered the main types of landform in China, ranging from mountainous regions, hills, plains to basins; (2) with energy utilization of crop straw into consideration, the cities where the rural areas surveyed in each of these provinces are selected for their great representativeness since the crop straw resources in them are quite rich. Specifically, ①Northeastern region. Dalian City, situated in the south of the Liaodong Peninsula, has implemented the key project of straw utilization early in the Twelfth Five-Year; Jinzhou City in the southwest of Liaoning Province, has promoted the utilization of crop straw with which yielding over 3 million tons annually [49]; Located in the central hinterland of the Songliao Plain, Siping City is one of the three largest granaries in the Northeastern region with the emphasis on the energy utilization of plentiful crop straw; Jilin City in the east of Jilin Province is rich in crop straws, especially corn

straw [50]; As an important commodity grain base, Jiamusi City in the northeast of Heilongjiang province has constructed straw silage cellars. ②Eastern region. Weifang City, seated in the middle of the Shandong Peninsula, attempts to construct the comprehensive utilization system of “collection-conversion-utilization” of crop straw. ③ Central region. Zhongxiang City in the north end of Hanjiang plain in Hubei Province is a typical model of the intensive land and resource conservation; Hefei City and Lu’an City are all typical cities in Anhui province in that the former has three national commercial grain bases and the output of agricultural and sideline products including grain, cotton, fruit etc. in the latter ranks top provincially. ④ Western region. Deyang City in the northeast of the Chengdu plain in Sichuan Province is a great agricultural production base; Neijiang City in the southeast of the Sichuan Basin is the base for national commercial grain production; Straw yield of Zunyi City in the north of Guizhou Province presently occupies first in the whole province, and Bijie City in the northwest of Guizhou produces 1.612 million tons of crop straws annually [51], and Qiandongnan Miao and Dong Autonomous Prefecture in the southeast of Guizhou Province is a environmental-friendly prefecture with the environmental quality index ranked first in the province in 2016. The survey was conducted by team members from School of Economics and Management, Huazhong Agricultural University, between December 2014 and July 2015. It is worth noting that all the survey members are carefully selected and all had rich rural research experience, and they are professionally trained before the formal investigation is carried out. With the random sampling method, villages in each city of these provinces are randomly selected and survey members randomly select rural households for investigation face to face. Finally, 908 questionnaires are obtained. Questionnaires with key information missing and inconsistent information being excluded, 784 with detailed content are valid for this study, and the effective rate is 86.34%. Areas surveyed are shown in Fig. 1. 4.2. Model selection 4.2.1. Base model It has been demonstrated that, within a utility maximization framework, discrete-choice models are generally used to analyze farmers’ behaviors. Specifically, when the dependent variable is binary i.e. households’ willingness to (or not to) adopt energy utilization of crop straw exampled by biogas, binary choice Logit/ Probit models are usually employed [52]. With this regard, Binary Logistic model, a probability estimation model, is used in this paper to estimate the effects of conformity tendencies (including conformity to relatives, neighbors, rich villagers and village cadres) on

Fig. 1. Distribution of areas surveyed.

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households’ willingness to adopt energy utilization of crop straw exampled by biogas. If y is the dependent variable, which takes values of either 1 or 0.



yi ¼ 1 if respondent i is willing to adopt yi ¼ 0 if respodent i is not willing to adopt

(1)

Then, P, the probability of households’ willingness to adopt energy utilization of crop straw exampled by biogas estimated by the logistic regression model, can be given by:

Pðyi ¼ 1Þ ¼ 4ða þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4 þ D þ C þ uÞ

(2)

Where i denotes the ith household; Pðyi ¼1Þ denotes the probability of theithhousehold’s willingness to adopt energy utilization of crop straw exampled by biogas; a is the regression intercept; x1 ; x2 ; x3 ; x4 denote conformity to relatives, neighbors, rich villagers and village cadres, respectively; D is a vector of control variables; C denotes regional dummies; m is the usual error term.

4.2.2. Propensity score matching method (PSM) Even though the influence of conformity tendencies including conformity to relatives, neighbors, rich villagers and village cadres on households’ willingness to adopt energy utilization of crop straw exampled by biogas could be estimated by the regression models, however, individuals’ conformity tendencies are not stochastic and may be the result of self-selection. Under this situation, the results of Binary Logistic model are generally biased [53]. Therefore, in order to avoid estimation errors caused by sample selective biased, PSM developed by Rosenbaum and Rubin [54] is employed to estimate the average treatment effect of conformity tendencies on households’ willingness to adopt energy utilization of crop straw exampled by biogas. First of all, the propensity score is estimated. Specifically, a Logit model is employed to predict the probabilities of individuals’ conformity to relatives, neighbors, rich villagers and village cadres. Second, according to the propensity score, different matching algorithms are used to match the sample to control for the selection

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biases. Finally, on the basis of the matched sample, the average differences in households’ willingness to adopt energy utilization of crop straw exampled by biogas between the treatment group and control group are compared, and then the causality coefficients are obtained, that is, average treatment effect on treated (ATT), which is defined by:

ATT ¼ E½ðY1i  Y0i ÞjDi ¼ 1 ¼ EfE½ðY1i  Y0i ÞjDi ¼ 1; PðXi Þg ¼ EfE½Y1i jDi ¼ 1; PðXi Þ  E½Y0i jDi ¼0; PðXi ÞjDi ¼ 1g

(3)

Where Di is a binary variable reporting whether individual i belongs to the treatment group or not. Specifically, Di ¼ 1 represents that individual i belongs to the treatment group, and Di ¼ 0, the control group. PðXi Þ denotes the propensity score; Y1i and Y0i denote the estimation results of the treatment group and control group, respectively. 4.2.3. Variable description The purpose of this paper is to explore the effect of conformity tendencies on households’ willingness to adopt energy utilization of crop straw exampled by biogas. Besides, in consideration of other factors that may condition households’ willingness to adopt energy utilization of crop straw exampled by biogas, in terms of the existing research [55,56], factors including two categories: individual and household features, and individual perceptions and risk aversions, are selected as the control variables or covariates for the conditional density of the treatment variable and the outcome variable. In addition, with the consideration of other regional factors excluded in the estimation such as specific provincial measures but may influence households’ willingness to adopt, regional dummy variable is set in this paper to control regional (province) effect so as to weaken the error in the regression analysis caused by the disunity of regional factors. The definition of the variables is given in Table 1. Table 2 shows the differences in the features of households with conformity tendencies and those without conformity tendencies as a result of t-tests. Generally speaking, the t-tests results suggest

Table 1 Definition of variables. Variable

Description

Willingness to adopt Are you willing to adopt energy utilization of crop straw? Conformity tendencies Conformity to relatives If my relatives have adopted energy utilization of crop straw, I will adopt it. Conformity to neighbors If my neighbors have adopted energy utilization of crop straw, I will adopt it. Conformity to rich villagers If rich villagers have adopted energy utilization of crop straw, I will adopt it. Conformity to village cadres If village cadres have adopted energy utilization of crop straw, I will adopt it. Individual and household features Gender Respondent’s gender Age The age of the respondent Educational attainment The years of schooling Household labor Number of household labor in 2014 Land acreage Household’s land acreage in 2014 Total household income Total household income in 2014 Individual perceptions and risk aversions Environmental perception Adopting energy utilization of crop straw will help protect the environment. Income perception Adopting energy utilization of crop straw will increase farmers income. Rural-developing perception Adopting energy utilization of crop straw is contributed to rural development. Cost risk Though adopting energy utilization of crop straw takes money, I will still adopt it. Time risk Though adopting energy utilization of crop straw takes time, I will still adopt it. Regional dummies (with Northeastern region as the reference) Eastern region Dummy variable Central region Dummy variable Western region Dummy variable

Unit 1 ¼ Yes; 0 ¼ No 1 ¼ very 1 ¼ very 1 ¼ very 1 ¼ very

or or or or

rather rather rather rather

agree; agree; agree; agree;

0 ¼ otherwisea 0 ¼ otherwisea 0 ¼ otherwisea 0 ¼ otherwisea

1 ¼ male; 0 ¼ female in years in years Number in hectares Ten thousand Yuan 1 ¼ very 1 ¼ very 1 ¼ very 1 ¼ very 1 ¼ very

or or or or or

rather rather rather rather rather

agree; agree; agree; agree; agree;

0 ¼ otherwisea 0 ¼ otherwisea 0 ¼ otherwisea 0 ¼ otherwisea 0 ¼ otherwisea

1 ¼ yes; 0 ¼ otherwise 1 ¼ yes; 0 ¼ otherwise 1 ¼ yes; 0 ¼ otherwise

Note. a As suggested by Ziegler [57], we take the value one if the respondent very or rather agrees to these nine statements and take value zero if the respondent neither disagrees nor agrees, rather disagrees or very disagrees to these statements, respectively.

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Table 2 Descriptive statistics of variables. Variable

Conformity to relatives

Conformity to neighbors

Conformity to rich villagers

Conformity to village cadres

Yes(n ¼ 535)

Yes(n ¼ 471)

Yes(n ¼ 395)

No(n ¼ 389)

Yes(n ¼ 546)

No(n ¼ 238)

(0.447)

0.835 (0.371)

0.825 (0.380)

0.907 (0.291)

0.655*** (0.476)

0.339*** (0.474) e 0.182*** (0.387) 0.406*** (0.492)

0.889 (0.315) 0.856 (0.352) e 0.896 (0.305)

0.473*** (0.500) 0.342*** (0.475) e 0.494*** (0.501)

0.863 (0.345) 0.767 (0.423) 0.648 (0.478) e

0.269*** (0.444) 0.218*** (0.414) 0.172*** (0.378) e

0.837 (0.370) 51.326 (9.203) 7.879 (3.433) 2.770 (1.189) 0.662 (2.039) 4.752 (4.078)

0.871 (0.336) 51.696 (9.126) 8.327 (3.327) 2.628 (1.158) 0.608 (0.783) 4.973 (4.458)

0.833 (0.374) 50.206** (9.536) 7.817** (3.266) 2.779* (1.187) 0.659 (1.877) 4.680 (4.080)

0.848 (0.359) 50.925 (9.072) 8.145 (3.296) 2.643 (1.128) 0.634 (1.667) 4.785 (4.160)

0.861 (0.346) 51.029 (9.994) 7.912 (3.326) 2.840** (1.266) 0.633 (0.629) 4.927 (4.534)

0.393 (0.489) 0.562*** (0.497) 0.604*** (0.490) 0.220*** (0.415) 0.259*** (0.439)

0.395 0.846 0.861 0.570 0.582

(0.489) (0.362) (0.347) (0.496) (0.494)

0.404 (0.491) 0.573*** (0.495) 0.589*** (0.493) 0.206*** (0.405) 0.278*** (0.448)

0.405 0.793 0.800 0.449 0.485

(0.491) (0.405) (0.017) (0.498) (0.500)

0.387 (0.488) 0.521*** (0.501) 0.555*** (0.498) 0.252*** (0.435) 0.307*** (0.462)

0.173*** (0.378) 0.454 (0.499) 0.240** (0.428)

0.291 (0.455) 0.534 (0.499) 0.119 (0.324)

0.177*** (0.382) 0.378*** (0.485) 0.285*** (0.452)

0.262 (0.440) 0.469 (0.499) 0.181 (0.386)

0.172*** (0.378) 0.429 (0.496) 0.248** (0.433)

No(n ¼ 249) ***

Willingness to adopt 0.899 (0.302) 0.683 (0.466) 0.900 (0.300) Conformity tendencies Conformity to relatives e e 0.911 (0.285) Conformity to neighbors 0.802 (0.399) 0.169*** (0.024) e *** Conformity to rich villagers 0.656 (0.475) 0.177 (0.382) 0.718 (0.451) Conformity to village cadres 0.880 (0.325) 0.301*** (0.460) 0.890 (0.314) Individual and household features Gender 0.854 (0.353) 0.847 (0.360) 0.862 (0.345) Age 51.028 (9.224) 50.803 (9.649) 50.711 (9.457) Educational attainment 8.095 (3.245) 8.028 (3.434) 8.204 (3.213) Household labor 2.700 (1.176) 2.711 (1.173) 2.658 (1.163) Land acreage 0.594 (0.774) 0.718 (2.277) 0.614 (0.815) Total household income 4.817 (4.326) 4.851 (4.170) 4.878 (4.404) Individual perceptions and risk aversions Environmental perception 0.400 (0.490) 0.400 (0.490) 0.403 (0.491) Income perception 0.783 (0.412) 0.554*** (0.498) 0.809 (0.394) *** Rural-developing perception 0.787 (0.410) 0.594 (0.492) 0.807 (0.395) Cost risk 0.464 (0.500) 0.229*** (0.421) 0.501 (0.501) Time risk 0.514 (0.500) 0.253*** (0.436) 0.546 (0.498) Regional dummies (with Northeastern region as the reference) Eastern region 0.256 (0.437) 0.189** (0.392) 0.276 (0.447) Central region 0.462 (0.499) 0.446 (0.498) 0.459 (0.499) Western region 0.202 (0.402) 0.201 (0.401) 0.176 (0.381)

No(n ¼ 313) 0.725

***

Note. This table aims to compare the differences between the groups of households who have conformity tendencies and those who do not have conformity tendencies from four perspectives. Standard deviations are in parentheses. *,** and *** indicate statistical difference at 10%, 5% and 1%, respectively, based on t-tests.

that, with regard to willingness to adopt, there are some differences between households’ conformity to relatives and those not conformity to relatives, between households’ conformity to neighbors and those not conformity to neighbors, between households’ conformity to rich villagers and those not conformity to rich villagers, between households’ conformity to village cadres and those not conformity to village cadres. 5. Empirical results and discussion 5.1. Willingness to adopt energy utilization of crop straw in the study areas Figs. 2e5 are about the proportions of households’ willingness to adopt energy utilization of crop straw exampled by biogas according to conformity to relatives, neighbors, rich villagers and village cadres, respectively. Through the drawing of the surveyed data, it is noted that, 89.91% with conformity to relatives are willing to adopt energy utilization of crop straw exampled by biogas, which is higher than that (68.27%) of those not conformity to relatives. Besides, 90.02% conformity to neighbors and 72.52% not conformity to neighbors are all willing to adopt energy utilization of crop straw exampled by biogas. In addition, there is not much difference between the proportion (83.54%) of those conformity to rich villagers who are willing to adopt energy utilization of crop straw exampled by biogas and that (82.52%) of those not conformity to rich villagers. 90.66% conformity to village cadres are willing to adopt energy utilization of crop straw exampled by biogas, which is higher than that (65.55%) of those not conformity to village cadres. 5.2. Binary Logistic model estimation It is noteworthy that this paper holds that there are different aspects linked to subjectivity as: conformity to relatives, neighbors, rich villagers and village cadres. In order to reduce the natural biases associated to this data, during the investigation, respondents were reminded that “relatives”, “neighbors”, “rich villagers” and

“village cadres” in the questionnaire specially refer to four types of generic and group concepts rather than specific individuals. Moreover, before the Binary Logistic regression analysis, the existence of multicollinearity among variables selected is checked and no serious problem was found. As Gaur and Gaur [58] note, the multicollinearity exists when a value of variance inflation factor (VIF) is greater than 5. While according to the multicollinearity test, the maximum VIF value for this study is 2.363, far less than 5, which means that the multicollinearity test of variables is acceptable for this study. To examine how conformity to relatives, neighbors, rich villagers and village cadres related to households’ willingness to adopt energy utilization of crop straw exampled by biogas, the base model, Binary Logistic model, is conducted. The regression results of Binary Logistic model are illustrated in Table 3. As shown in Table 3, column (1) reports the results only with core explanatory variables into the estimation, and column (2) reports the results with core explanatory variables and control variables into the estimation, while column (3), the results with all variables into the estimation including regional dummy variables controlled. From the table, it can be observed that conformity tendencies including conformity to relatives, neighbors, rich villagers and village cadres do exert a significant influence on households’ willingness to adopt energy utilization of crop straw exampled by biogas even if control variables and regional dummy variables are added, which is in line with our expectation. Compared with column (1) and (2), the model fit of (3) with regional dummy variables controlled is much more acceptable. For instance, LRchi2 ¼ 114.030 (p ¼ 0.000 < 1) indicates that explanatory variables are jointly statistically significant. Hence, regression results in column (3) are mainly analyzed. Specifically, the regression results in column (3) reveal a significantly positive effect of conformity to relatives on households’ willingness to adopt energy utilization of crop straw exampled by biogas. Combined with the results of the marginal effects in the fifth column, it is found that a 1% increase in the value of the variable conformity to relatives will result in an increase in the

Y. Zeng et al. / Renewable Energy 138 (2019) 573e584

100.00%

579

89.91%

90.00% 80.00%

68.27%

70.00% 60.00% 50.00% 40.00%

31.73%

30.00% 20.00%

10.09%

10.00% 0.00% Conformity to relatives

n=535

Willingness to adopt

Not conformity to relatives

n=249

Not willingness to adopt

Fig. 2. Proportions of households’ willingness (not) to adopt (conformity to relatives).

100.00%

90.02%

90.00%

90.00%

72.52%

80.00%

70.00%

70.00%

60.00%

60.00%

50.00%

50.00%

40.00%

40.00%

27.48%

30.00% 20.00%

82.52%

83.54%

80.00%

30.00% 20.00%

9.98%

16.46%

17.48%

10.00%

10.00% 0.00%

0.00% Conformity to neighbors n=471

Willingness to adopt

Not conformity to neighbors n=313

Not willingness to adopt

Fig. 3. Proportions of households’ willingness (not) to adopt (conformity to neighbors).

probability of households’ willingness to adopt by 11.718%. The possible reason to explain this result is that households’ conformity to relatives is possibly the reflection of the support of the blood relations on the grounds that they have a relatively close relationship with their relatives [59]. Conformity to neighbors has a significantly positive effect on households’ willingness to adopt energy utilization of crop straw exampled by biogas, as is shown in regression results in column (3). In combination with the results of the marginal effects in the fifth column, it shows that a 1% increase in the value of the variable conformity to neighbors will result in an increase in the probability of households’ willingness to adopt by 10.626%. One possible explanation for this finding is that, in villages in developing countries, a society of acquaintances, the behaviors of immediate neighbors in technology adoption are often the references of others, which often results in conformity tendencies and behaviors [19]. As is revealed in regression results in column (3), conformity to rich villagers exerts a significantly negative effect on households’

Conformity to rich villagers n=395

Willingness to adopt

Not conformity to rich villagers n=389

Not willingness to adopt

Fig. 4. Proportions of households’ willingness (not) to adopt (conformity to rich villagers).

willingness to adopt energy utilization of crop straw exampled by biogas. Combined with the results of the marginal effects in the fifth column, it is found that a 1% increase in the value of the variable conformity to rich villagers will result in an decrease in the probability of households’ willingness to adopt by 19.131%. It is possibly because adopting energy utilization of crop straw exampled by biogas needs money and the return of this action is slow and uncertain [2], all of which rich villagers could shoulder, while it goes against the purpose of getting rich for common farmers. Thus, conformity to rich villagers could decrease the likelihood of households’ willingness to adopt energy utilization of crop straw exampled by biogas. Regression results in column (3) show that conformity to village cadres is significantly positively correlated with households’ willingness to adopt energy utilization of crop straw exampled by biogas. In combination with the results of the marginal effects in the fifth column, it shows that a 1% increase in the value of the variable conformity to village cadres will result in an increase in the

580

Y. Zeng et al. / Renewable Energy 138 (2019) 573e584

100.00%

exampled by biogas. However, as is shown in Table 2, t-tests results show that there are many differences between those who conform to relatives, neighbors, rich villagers and village cadres and those who do not, which means that individual’s conformity tendencies are likely to be the result of one’s self-selection and that there may be selective biases in direct regression of Binary Logistic model. Against this background, PSM put forward by Rosenbaum and Rubin [54] is employed to construct an anti-factual framework to avoid selective biases.

90.66%

90.00% 80.00%

65.55%

70.00% 60.00% 50.00%

34.45%

40.00% 30.00% 20.00%

9.34%

5.3. Estimation results of PSM

10.00% 0.00% Conformity to rich villagers n=546

Not conformity to rich villagers n=238

Willingness to adopt

Not willingness to adopt

Fig. 5. Proportions of households’ willingness (not) to adopt (conformity to village cadres).

probability of households’ willingness to adopt by 19.857%. The possible reason is that, to minimize costs and to avoid the costs of experimentation, farmers tend to conform to peers who are perceived to be successful and the actions of whom are perceived to be legitimate [60] and village cadres are exactly this kind of peers in villages. In addition, with respect to the control variables, factors including gender, educational attainment, land acreage and cost risk all have significantly positive effects on households’ willingness to adopt energy utilization of crop straw exampled by biogas, while age is significantly and negatively correlated with households’ willingness to adopt energy utilization of crop straw exampled by biogas. In sum, the estimation results confirm our main expectations, i.e. a significant effect of conformity tendencies including conformity to relatives, neighbors, rich villagers and village cadres on households’ willingness to adopt energy utilization of crop straw

First, to predict the probability of conformity tendencies (including conformity to rich villagers, conformity to relatives, conformity to neighbors and conformity to village cadres), a logit model is employed, and the results are reported in Table 4. A glance at Table 4 shows that a number of variables do influence the likelihood of households’ conformity tendencies. Since the propensity scores are only used to balance the observed distribution of covariates across the treated and the untreated groups, and it is through the resultant balance to assess the success of the estimation of propensity score [61,62], here thorough analyses of the propensity score estimations do not need to be made. Second, the distributions of the propensity scores and the regions of common support are reflected in Figs. 6e9. These figures demonstrate the bias in the distribution of the propensity scores between the groups with conformity tendencies and those without conformity tendencies, and they clearly reveal the significance of proper matching and the imposition of the common support condition to avoid bad matches. From the figures, it is clear that after matching, all the differences between the two groups of samples are obviously weakened. Third, in order to further ensure the robustness of the regression results, several matching algorithms are utilized to further estimate the impacts of conformity tendencies on households’ willingness to adopt energy utilization of crop straw exampled by biogas. Table 5 reports the estimate results of the average treatment effects by the

Table 3 Regression results of Binary Logistic model. Variable Conformity tendencies Conformity to relatives Conformity to neighbors Conformity to rich villagers Conformity to village cadres Individual and household features Gender Age Educational attainment Household labor Land acreage Total household income Individual perceptions and risk aversions Environmental perception Income perception Rural-developing perception Cost risk Time risk Regional dummies Constant Log likelihood Prob > chi2 Pseudo R2 LRchi2

(1)

(2)

(3)

Marginal effects of (3)

0.910**(0.373) 0.969***(0.364) 1.789***(0.381) 1.575***(0.333)

1.101***(0.376) 0.982***(0.374) 1.893***(0.409) 1.786***(0.370)

1.125***(0.366) 1.020**(0.394) 1.836***(0.423) 1.906***(0.371)

11.718%***(0.037) 10.626%***(0.040) 19.131***(0.039) 19.857%***(0.033)

0.494*(0.292) 0.039***(0.013) 0.054*(0.029) 0.138 (0.091) 1.218***(0.420) 0.032 (0.021)

0.574*(0.309) 0.033**(0.014) 0.067**(0.030) 0.124 (0.091) 1.341***(0.447) 0.014 (0.023)

5.976%*(0.032) 0.343%**(0.001) 0.693%**(0.003) 1.290% (0.009) 13.968%***(0.045) 0.150% (0.002)

No 0.571***(0.153)

0.402*(0.241) 0.020 (0.354) 0.401 (0.377) 0.498 (0.345) 0.532 (0.339) No 0.979 (0.841)

0.124 (0.247) 0.123 (0.382) 0.329 (0.390) 0.692*(0.365) 0.420 (0.340) Yes 1.617*(0.963)

1.290% (0.026) 1.286% (0.040) 3.427% (0.041) 7.213%*(0.038) 4.375% (0.035) Yes

298.434 0.000 0.164 90.630

272.653 0.000 0.236 119.26

264.650 0.000 0.259 114.030

Note. N ¼ 784. Standard errors are in parentheses.*,** and *** indicate statistical difference at 10%, 5% and 1%, respectively. With Stata 13 software, all regression commands are performed with “robust” added. Numbers in this table are round up and round down numbers.

Y. Zeng et al. / Renewable Energy 138 (2019) 573e584

581

Table 4 Propensity score for different types of conformity tendencies (Logit estimates). Variable

Conformity to relatives

Individual and household features Gender 0.112 (0.234) Age 0.003 (0.009) Educational attainment 0.005 (0.027) Household labor 0.012 (0.073) Land acreage 0.056 (0.061) Total household income 0.011 (0.021) Individual perceptions and risk aversions Environmental perception 0.098 (0.183) Income perception 0.645***(0.234) Rural-developing perception 0.243 (0.241) Cost risk 0.455*(0.251) Time risk 0.629***(0.238) Regional dummies (with Northeastern region as the reference) Eastern region 0.747**(0.317) Central region 0.598**(0.286) Western region 0.951***(0.303) Constant 0.840 (0.659) Log likelihood Prob > chi2 Pseudo R2 LRchi2

446.426 0.000 0.089 87.220

Note. N ¼ 784. Standard errors are in parentheses.*,** and

***

Conformity to neighbors

Conformity to rich villagers

Conformity to village cadres

0.042 (0.226) 0.010 (0.009) 0.008 (0.026) 0.043 (0.070) 0.029 (0.056) 0.009 (0.020)

0.181 (0.239) 0.017*(0.010) 0.028 (0.027) 0.059 (0.072) 0.027 (0.058) 0.000 (0.021)

0.363 (0.244) 0.004 (0.010) 0.003 (0.027) 0.095 (0.072) 0.030 (0.058) 0.023 (0.021)

0.093 (0.177) 0.722***(0.229) 0.146 (0.237) 0.658***(0.237) 0.557**(0.222)

0.345*(0.188) 0.733***(0.245) 0.510**(0.253) 0.972***(0.236) 0.252 (0.225)

0.138 (0.186) 0.804***(0.233) 0.457*(0.240) 0.428*(0.255) 0.039 (0.240)

0.638**(0.314) 0.190 (0.285) 0.342 (0.296) 0.388 (0.636)

1.403***(0.341) 1.432***(0.322) 0.701**(0.336) 3.383***(0.696)

1.020***(0.328) 0.718**(0.293) 0.610**(0.303) 0.092 (0.659)

472.562 0.000 0.104 109.670

447.727 0.000 0.176 191.360

438.317 0.000 0.089 85.900

indicate statistical difference at 10%, 5% and 1%, respectively.

single and four nearest neighbor methods with replacement as well as the kernel estimator with bandwidth 0.06 and bootstrapped standard errors with 500 replications reported. The results of ATT show that, after avoiding the observable systematic differences among samples, conformity tendencies still have significant influences on households’ willingness to adopt energy utilization of crop straw exampled by biogas, which is just in line with the regression results of Binary Logistic model. Specifically, as for conformity to relatives, estimated ATTs are all positive across all matching techniques and statistically significant at the 1% level. Among the results, the numerical value of ATT obtained by the four nearest neighbor method is the largest, namely 0.441, followed by that from the single nearest neighbor method, 0.423 and that from Kernel-based matching method, 0.414. Although there is a slight difference among the numerical values of ATT obtained by different matching methods, the results show that conformity to relatives does increase the likelihood of households’ willingness to adopt energy utilization of crop straw exampled by

biogas. As for conformity to neighbors, estimated ATTs are all positive across matching techniques except for the single nearest neighbor matching method and all statistically significant at the 1% level. The numerical value of ATT obtained by Kernel-based matching method is the largest, 0.205, followed by that from the four nearest neighbor method, 0.180 and that from the single nearest neighbor method, 0.128. Though there is a slight difference among the significance and numerical values of ATT obtained by different matching methods, the results show that conformity to neighbors does tend to facilitate households’ willingness to adopt energy utilization of crop straw exampled by biogas. As for conformity to rich villagers, estimated ATTs are all negative across matching techniques and statistically significant at the 5% level. The absolute numerical value of ATT obtained by the single nearest neighbor method is the largest, 0.115, followed by that from the four nearest neighbor method, 0.102 and that from Kernel-based matching method, 0.099. Though there is a slight difference among the absolute numerical values of ATT obtained by

Fig. 6. Effects of conformity to relatives. Note. Treated on support indicates the individuals with conformity tendencies who find a suitable match, whereas treated off support indicates the individuals with conformity tendencies who did not find a suitable match.

Fig. 7. Effects of conformity to neighbors. Note. Treated on support indicates the individuals with conformity tendencies who find a suitable match, whereas treated off support indicates the individuals with conformity tendencies who did not find a suitable match.

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of ATT obtained by the four nearest neighbor method is the largest, namely 0.371, followed by that from Kernel-based matching method, 0.367 and that from the single nearest neighbor method, 0.238. Although there is a slight difference among the significance and numerical values of ATT obtained by different matching methods, the results show that conformity to village cadres tends to increase the likelihood of households’ willingness to adopt energy utilization of crop straw exampled by biogas. 6. Conclusions and policy implications 6.1. Conclusions

Fig. 8. Effects of conformity to rich villagers. Note. Treated on support indicates the individuals with conformity tendencies who find a suitable match, whereas treated off support indicates the individuals with conformity tendencies who did not find a suitable match.

This paper contributes to the literature on energy utilization of crop straw by analyzing the effects of conformity tendencies (including conformity to rich villagers, conformity to relatives, conformity to neighbors and conformity to village cadres) on households’ willingness to adopt biogas produced from crop straw in China. The data set from surveys in rural areas in eight provinces in China are used in this study to examine these effects. With Binary Logistic model directly estimating these effects, PSM technique is specially employed to mitigate the selective biases and to test the robustness of results [63]. Results indicate that conformity tendencies have generated a comprehensively significant impact on households’ willingness to adopt energy utilization of crop straw exampled by biogas, which complys with the results estimated by several matching algorithms, showing that the findings of this paper are robust and trustworthy. Specifically, it is found that conformity to relatives, neighbors and village cadres are all significantly and positively correlated with households’ willingness to adopt energy utilization of crop straw exampled by biogas, while conformity to rich villagers, significantly and negatively correlated with it. 6.2. Policy implications

Fig. 9. Effects of conformity to village cadres. Note. Treated on support indicates the individuals with conformity tendencies who find a suitable match, whereas treated off support indicates the individuals with conformity tendencies who did not find a suitable match.

different matching methods, the results show that conformity to rich villagers does reduce households’ willingness to adopt energy utilization of crop straw exampled by biogas. As for conformity to village cadres, estimated ATTs derived from the four nearest neighbor method and Kernel-based matching method are all positive and statistically significant at the 1% level, which are slightly different from that obtained by the single nearest neighbor method at the 5% significance level. The numerical value

Our findings provide important policy implications. Generally speaking, households’ conformity tendencies can be used as a means for the development of energy utilization of crop straw exampled by biogas in rural areas. Specifically: Firstly, considering the significant and positive impacts of conformity to relatives, neighbors and village cadres, on the one hand, government and extension services should intensify their efforts to encourage households to conform to their relatives, neighbors and village cadres in the adoption of energy utilization of crop straw exampled by biogas; on the other hand, an effective channeling of information exchange and a convenient conformity environment should be constructed in rural areas, which in turn could help to

Table 5 Average treatment effects of different matching algorithms. Matching algorithms

Influencing factors

ATT

Std. Err.

Treated

Controls

Nearest neighbor Matching (1:1)

Conformity Conformity Conformity Conformity Conformity Conformity Conformity Conformity Conformity Conformity Conformity Conformity

0.423*** (4.240) 0.128 (1.560) 0.115** (-2.090) 0.238** (1.980) 0.441*** (5.320) 0.180*** (2.780) 0.102** (-2.130) 0.371*** (3.980) 0.414*** (5.430) 0.205*** (3.320) 0.099** (-2.02) 0.367*** (4.420)

0.097 0.089 0.034 0.121 0.096 0.073 0.031 0.110 0.080 0.064 0.027 0.085

0.891 0.896 0.830 0.904 0.891 0.896 0.830 0.904 0.891 0.896 0.830 0.904

0.468 0.769 0.945 0.666 0.450 0.716 0.932 0.533 0.477 0.691 0.929 0.537

Nearest neighbor Matching (1:4)

Kernel-based Matching (bandwidth 0.06)

Note. Numbers of t-values are in parentheses.*,** and

to to to to to to to to to to to to

***

relatives neighbors rich villagers village cadres relatives neighbors rich villagers village cadres relatives neighbors rich villagers village cadres

indicate statistical difference at 10%, 5% and 1%, respectively.

Y. Zeng et al. / Renewable Energy 138 (2019) 573e584

give full play to these three kinds of conformity in the promotion of energy utilization of crop straw in rural areas. Secondly, given the significant and negative role of conformity to rich villagers in enhancing households’ willingness to adopt energy utilization of crop straw exampled by biogas, policy makers and extension services should be alerted that cautions need to be taken while designing and implementing relevant policies that favor the role of conformity tendencies in popularizing biogas made from crop straw. Important recommended strategies are to discourage households to conform to rich villagers in biogas adoption and to carefully filter out rich villagers’ biogas adoption information. By this way, the effective role of conformity tendencies in dissemination and diffusion of energy utilization of crop straw exampled by biogas in rural areas can be achieved. Acknowledgments The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (71703051/ 71333006/71503074), the Key Program of Philosophy and Social Sciences Research, Ministry of Education of China (15JZD014) and the Soft Science Research Project of Technology Innovation in Hubei Province (2018ADC036). The authors thank Mr. Linlin Chen for his help. The authors would like to express their sincere gratitude to the editor and the anonymous reviewers of Renewable Energy for their valuable comments. In addition, Prof. Ke He would like to give a special thanks to Elva Hsiao, whose musics have accompanied him through many tough times, and wish her good health. References [1] M.H. Ashourian, S.M. Cherati, A.A. Mohd Zin, N. Niknam, A.S. Mokhtar, M. Anwari, Optimal green energy management for island resorts in Malaysia, Renew. Energy 51 (2013) 36e45. [2] A. Yasar, S. Nazir, A.B. Tabinda, M. Nazar, R. Rasheed, M. Afzaal, Socio-economic, health and agriculture benefits of rural household biogas plants in energy scarce developing countries: a case study from Pakistan, Renew. Energy 108 (2017) 19e25. [3] K. He, J.B. Zhang, Y.M. Zeng, L. Zhang, Households’ willingness to accept compensation for agricultural waste recycling: taking biogas production from livestock manure waste in Hubei, P. R. China as an example, J. Clean. Prod. 131 (2016) 410e420. [4] N. Scarlat, J.F. Dallemand, F. Fahl, Biogas: developments and perspectives in Europe, Renew. Energy 129 (2018) 457e472. [5] N. Abadi, K. Gebrehiwot, A. Techane, H. Nerea, Links between biogas technology adoption and health status of households in rural Tigray, Northern Ethiopia, Energy Policy 101 (2017) 284e292. [6] A.D. Tigabu, F. Berkhout, P. van Beukering, Technology innovation systems and technology diffusion: adoption of bio-digestion in an emerging innovation system in Rwanda, Technol. Forecast. Soc. Change 90 (Part A) (2015) 318e330. [7] H. Kabir, R.N. Yegbemey, S. Bauer, Factors determinant of biogas adoption in Bangladesh, Renew. Sustain. Energy Rev. 28 (2013) 881e889. [8] H.E. Kelebe, K.M. Ayimut, G.H. Berhe, K. Hintsa, Determinants for adoption decision of small scale biogas technology by rural households in Tigray, Ethiopia, Energy Econ. 66 (2017) 272e278. [9] M.G. Mengistu, B. Simane, G. Eshete, T.S. Workneh, Factors affecting households’ decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia, Renew. Energy 93 (2016) 215e227. [10] J.W. Mwirigi, P.M. Makenzi, W.O. Ochola, Socio-economic constraints to adoption and sustainability of biogas technology by farmers in Nakuru Districts, Kenya, Energy Sustain. Dev. 13 (2) (2009) 106e115. [11] H. Katuwal, A.K. Bohara, Biogas: a promising renewable technology and its impact on rural households in Nepal, Energy Policy 9 (2009) 2668e2674. [12] MdK. Hassan, P. Pelkonen, A. Pappinen, Rural households’ knowledge and perceptions of renewables with special attention on bioenergy resources development-results from a field study in Bangladesh, Appl. Energy 136 (2014) 454e464. [13] T. Shi, R. Gill, Developing effective policies for the sustainable development of ecological agriculture in China: the case study of Jinshan County with a systems dynamics model, Ecol. Econ. 53 (2005) 223e246. [14] M. Berhe, D. Hoag, G. Tesfay, C. Keske, Factors influencing the adoption of biogas digesters in rural Ethiopia, Energy Sustain. Soc. 7 (2017) 1e11. [15] J. Mwirigi, B.B. Balana, J. Mugisha, P. Walekhwa, R. Melamu, S. Nakami, P. Makenzi, Socio-economic hurdles to widespread adoption of small-scale biogas digesters in Sub-Saharan Africa: a review, Biomass Bioenergy 70

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