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Article

Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies

by
Jinxing Wang
1,
Wanming Li
1,*,
Shamsheer ul Haq
2 and
Pomi Shahbaz
2
1
School of Economics and Management, Shihezi University, Shihezi 832000, China
2
Department of Economics, Division of Management and Administrative Science, University of Education, Lahore 54770, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(7), 5611; https://doi.org/10.3390/su15075611
Submission received: 18 February 2023 / Revised: 18 March 2023 / Accepted: 20 March 2023 / Published: 23 March 2023
(This article belongs to the Special Issue Adoption of Management Strategies and Technologies for Sustainable Production: A Solution to Reduce Environmental Emissions Volume II)
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Abstract

:
Traditional energy consumption raises greenhouse gas emissions, which is a major concern in China. Agricultural energy consumption accounts for one third of China’s greenhouse gas emissions. Thus, current patterns of energy consumption on farms are both unsustainable and inadequate since rural communities in emerging nations have limited access to energy sources. This study explores the factors affecting the adoption of renewable energy on farms and examines the effect of such adoption on technical efficiency. The data were collected from 801 farmers selected through a multistage random and purposive sampling method in a face-to-face survey in China. Logistic regression, data envelopment analysis and propensity score matching were used to analyze the data. The descriptive statistics depicted that renewable energy technology was adopted by more than 25% of the total farmers. The logistic regression results depicted that education, farm size, government financial support, perceptions of renewable energy (usefulness, cost effectiveness, environmental friendliness and information availability) and farmer entrepreneurial orientation dimensions (risk-taking, innovativeness and pro-activeness) all significantly affected the adoption of renewable energy technology on farms. Farmers who perceived renewable energy as more useful and cost-effective than conventional energy technology were 1.89 and 2.13 times more likely to adopt it on their farms, respectively. Farmers who perceived renewable energy as more environmentally friendly than traditional energy technology were 1.54 times more likely to use it on their farms. The findings also showed that innovative farmers were 2.24 times more likely to adopt renewable energy technology on their farms. The propensity score matching results showed that the technical efficiency of adopters of renewable energy was 10% higher than that of nonadopters. The study suggests that agriculture must be transformed to fulfill the existing and forthcoming demand for food and energy in an impartial and ecologically sustainable manner.

1. Introduction

Energy and agricultural food systems are deeply entwined and almost one third of the world’s total energy is utilized in agricultural food systems. The energy used by agricultural food systems is also accountable for one third of their GHGs [1]. Agriculture and energy systems are also important for achieving Sustainable Development Goals (SDGs) and they also have a big effect on people and the environment. About 2.5 billion people around the world depend on agriculture as their main source of income. This makes agriculture a key factor in the growth of the world economy. Making sure that reliable, economical and ecologically friendly energy is available for primary production and post-harvest handling is a key part of improving yields, earnings, losses and climate resilience. Climate change threatens agriculture and it has become the foremost challenge for sustainable agriculture development [2].
Energy is an important resource in agriculture and it is used in different forms for enhancing the productivity of the farm, food security and economic development [3]. From farm to fork, agriculture consumes a lot of energy in various forms. It includes the mechanical, animal and human energy for crop production in agriculture. In agriculture, two types of energy are used: direct energy and indirect energy. Direct energy is the energy used in various farming activities such as land preparation, irrigation, harvesting, transportation, etc. [4]. The energy used in the manufacturing, packaging and distribution of pesticides, farm machinery and fertilizer is referred to as indirect energy [5,6,7]. The major items of indirect energy, such as fertilizer and chemical pesticides, are used in the production of crops on the farm. Fossil fuel is the major source of energy that is highly consumed in transportation, electricity generation and the production of crops and livestock products [8]. Energy is an absolute necessity for performing various farming operations such as tillage, crop protection, irrigation, harvesting and using farm machinery [9]. Similarly, agriculture is a major consumer of fossil fuels in order to generate energy for the extraction of water from the ground for irrigation, which in turn generates 14 to 30 percent of total GHGs [10]. Agriculture consumes electricity and fuel for the operation of farm machinery and other tools, for lighting farms, for cooling and heating farm buildings and for the application of fertilizer and pesticides [5,11].
Agriculture’s energy consumption is increasing on a daily basis. The growing population, shortage of arable land and desire for high living standards are possible reasons for the consumption of more energy in agriculture. Furthermore, conventional farming is heavily reliant on energy, which may be a major source of pollution and contribute to global warming [12]. Bailey et al. [13] found that conventional farming systems need more energy per hectare (14,667 MJ/ha) than integrated farming systems (13,428 MJ/ha). Therefore, agriculture is the biggest emitter of GHGs in the form of nitrous oxide and methane from soil management practices and livestock production [14].
The continuously changing climate attracts the attention of researchers and policymakers toward developing renewable energy (RE) and transforming agricultural practices. RE may play a crucial role in addressing power, heating, cooling and food transportation demands in both developed and developing nations. RE provides environmental protection benefits over nonrenewable fossil energy sources like coal and oil [15,16]. De Groote et al. [17] consider RE as a response to climate change and a way to reduce pollution in agriculture. RE has become a true choice for developing and developed countries to improve their energy structures and achieve green development [18,19].
Indeed, irrigation practices can be switched from nonrenewable to renewable energy technology (RET) to reduce carbon emissions [8] and maximize energy efficiency. Powell et al. [20] reported that the adoption of RE on farms can significantly reduce energy costs and ecological impacts. Rana et al. [21] also found that RE sources are more financially viable than nonrenewable energy sources in agriculture. The efficient consumption of energy in agriculture will lower the associated environmental hazards, control the destruction of natural resources and stimulate sustainable agriculture [22]. Sustainable agriculture is a system of taking maximum benefits from the existing soil nutrients and water cycle, advantageous soil organisms and energy flows [12]. A considerable number of studies have been accomplished regarding the use of energy in agriculture [23,24,25,26,27]. Additionally, Rana et al. [21] compared the financial viability and environmental implications of renewable and nonrenewable energy on farms. Luthra et al. [28] conducted a study to identify the barriers to the adoption of RET on farms in India. Kardooni et al. [29] also investigated the variables that influence the use of RE on Malaysian farms.
China is primarily focused on using environmentally friendly RET. China is very rich in renewable (solar) energy resources and this solar energy is commonly used for the purpose of heating water [30]. Although China has the highest total capacity in the world, the adoption of this RET is still very low. The adopted capacity of the country was 193.3 KWH per 1000 households in 2013, which was very low compared to that of Australia, Israel and Greece [31]. The factors affecting RE adoption in rural areas have been extensively explored worldwide [32,33,34,35,36,37,38,39]. Most of these studies have focused on socio-economic factors affecting the adoption of different forms of RE technologies in developing and developed countries. None of the studies considered the entrepreneurial characteristics of farmers in the adoption of RET on farms. Therefore, this study was designed to fill this gap in the literature. Thus, the first objective of this study was to determine the role of farmers’ entrepreneurial orientation, perceptions of RE and government financial support in the RET adoption on farms. The hypotheses were that farmers’ entrepreneurial orientation, perception of RE and government financial support significantly affect RE adoption on farms. The second objective was to compare the technical efficiency of adopters and nonadopters of RET. Here, the hypothesis was that RE adopters have higher farm technical efficiency compared to nonadopters. The third objective was to explore the key sources of information about RET.
The remaining sections of this article are structured as follows: The following section discusses the literature on innovation and technology adoption. Section 3 provides information about the materials and methods of the study. Section 4 presents the results of the study and discusses them in light of the prior relevant literature. The conclusion and policy implications are provided in the last section.

2. Literature Review on Innovation Adoption

Any technology’s relative superiority must be known and accepted by farmers before it can be adopted on farms [40]. The technology acceptance model proposed by Davis [41] described the importance of people’s perceptions regarding the benefits associated with technology adoption. This model regarded the perceived usefulness of technology as an important factor in determining technology adoption. Adoption stages often include recognizing a problem, looking for possible solutions, deciding to try adopting a solution and finally deciding to try putting the solution into action [42,43,44]. Greenhalgh et al. [45] reported three stages of adoption: pre-adoption (knowledge of the innovation), peri-adoption (information about the innovation is always available) and established adoption (commitment to the adoption). Frambach and Schillewaert [46] provided an alternative viewpoint by identifying two phases of adoption and innovation: the organization’s decision to pursue adoption and the staff’s acceptance and initiation of their own processes of accepting the innovation.
Adopting an innovation at the organizational or system level is a difficult task. It is more difficult to induce changes in normal practice when organizational decision-makers do not regard changes as necessary [47]. Despite the similarities to individual adoption, Aarons et al. [48] contend that individuals in organizations may have trouble understanding, evaluating or selecting suitable innovations to solve specific problems, or that the choice of innovation to adopt is frequently complicated by organizational factors (such as hierarchy, culture and values) that are not always encountered when dealing with problems at the individual level. Talukder and Quazi [49] claimed that the opinions of people’s social networks about innovations have a big impact on how individuals adopt new ideas. It has also been found that a person’s social network has a big effect on how quickly they accept a new idea. Fries et al. [50] found that the six most important things that keep SMEs from adopting innovations are a perceived imbalance between opportunities and risks, incompatibility with daily activities, lack of fit in independent business systems, uncertainty about infrastructure development, lack of information technology knowledge, and costs. Pérez-Luo et al. [51] found that an entrepreneur’s way of thinking has a big effect on the creation and spread of innovation.
They also found that the relationship between creating and adopting the innovation changed depending on how dynamic the environment was. Jansson [52] talked about the aspects that influence consumer acceptance of high-involvement eco-innovation for cars. According to what he found, early adopters and late adopters have very different social norms, attitudes, levels of curiosity and opinions about what makes something creative.
As a result of climate change, economies, especially those in developing and vulnerable countries, have had to adapt by establishing new innovations to make agriculture more sustainable. Recent research shows that agricultural innovations have a good chance of reducing the effects of climate change, improving food security and bringing in more money for farmers [53,54]. Climate change has a significant negative effect on crop yields. In Zimbabwe, the use of sustainable agricultural advances like conservation agriculture and drought-resistant maize has led to higher crop yields and better food security for households [55,56]. Van Vuuren et al. [57] found that a strong transition to resource efficiency, sustainable production practices and human development investment could lead to the use of RE, less land use and lower greenhouse gas emissions caused by agriculture. Similarly, Prados et al. [58] assessed the contribution of local economic activities to rural development, whether they are created by or influenced by the vicinity of RE installations. The findings revealed that some local economic activities, due to their interconnection with large RE plants, can generate a significant number of employment opportunities.
Today, the adoption of RET depends on economic reasoning [59,60]. Moreover, farmers also face serious health problems due to dirty energy sources like fuel wood and coal [61]. The concept of post-materialist values describes that as the socioeconomic status of farmers increases, they seek to improve their well-being and desire to have a clean and healthy environment [62]. With the passage of time, farmers require an environmentally friendly and clean source of energy [63,64]. RE technologies are distinct sources of clean energy that significantly improve people’s quality of life [65,66].

3. Materials and Methods

3.1. Study Area and Sampling Techniques

The Sichuan province was selected for the collection of data due to its diversified climatic conditions and economic contribution to the Chinese economy. It is also a very important province of the country based on its clean energy bases. It has the highest hydropower reserve among all provinces of the country. Similarly, it has the largest conventional gas reservoir. This province is also one of the major energy consumers of China. The Sichuan province has many advantages in terms of clean energy resources and it has a much better energy consumption mix compared to eastern and northern parts of China [67]. The Sichuan province has a very heterogeneous distribution of solar energy resources across its counties. The spatial distribution tends to rise from east to west. It has three types of landscapes including plains, mountains and hills. It has four different classes of solar energy resources area known as class I, class II, class III and class IV. It comprises a total sunshine duration equal to 750–2680 h, with a range of 3200 MJ/m2 to 6390 MJ/m2 annual total radiation [68,69]. Figure 1 presents the study area. The Sichuan province is bestowed with silicon-rich resources, and it is a key element for the crystalline silicon PV industry. This province counted overall 13% of the country’s polysilicon production. Consequently, China contributed 70% to the global production of polysilicon in 2012. The provincial government actively carried out the development projects of pilot rooftop-distributed PV and PV poverty alleviation. By the end of September 2022, the province had reached 2.044 million KW capacity of cumulated grid-connected PV [70].
Multistage random and purposeful sampling techniques were used to approach the representative sample. In view of the importance of the Sichuan province to the Chinese economy, it was selected as the study area for this research in the first stage of sampling. The province’s counties were first classified based on their topography, which included plains, mountains and hills. In the second step, two counties were randomly selected from each county group. In the third stage of sampling, two towns were randomly selected from each county. In the next step, four villages were randomly chosen from each town. Finally, 25–26 farmers from each village were chosen randomly. A total of 801 farmers were approached to conduct face-to-face interviews; out of them, only 205 were adopters and 596 were nonadopters. The face-to-face interviews were carried out to collect error-free data through a well-designed and pretested questionnaire.

3.2. Questionnaire Design

The questionnaire was divided into different sections. The first section contained the informed consent that was verbally explained to the respondents. After receiving consent from the respondents, the questionnaire was properly executed. The experts explained the question and recorded the answer on the questionnaire. The next section contained questions regarding the human capital at the farm. These included the age of the respondents, education, farming experience, agriculture labor force and organizational membership. The second section contained questions related to the farm characteristics, such as farm size, distance to the nearest RE enterprise and farm income share of total income. The third section comprised questions regarding the farmers’ perceptions of RE resources. The fourth part of the questionnaire was about government financial support and Internet use for agricultural purposes. The fifth part contained questions regarding entrepreneurial orientation. In the sixth section of the study, farm production and cost data were gathered to estimate the farms’ technical efficiency. The questionnaire ended with a question asking whether farmers adopted RET at the farm or not. The questionnaire included both open-ended and closed-ended questions. The survey was conducted by a team of enumerators consisting of both males and females through a face-to-face interview.

3.3. Selection of Independent Variables

The prior studies on technology adoption on farms were thoroughly explored to select the appropriate explanatory variables for the present study. These variables were divided into five broader categories. The first three broader categories consist of generic variables like gender, age, education, farm size, Internet use and government financial support as they have been utilized in the literature [71,72,73,74,75]. This study also included variables about farmers’ entrepreneurship to find out about the farmers’ risk behavior, innovativeness and proactivity, as well as their perceptions of renewable agriculture [76]. All variables considered in this study are described in Table 1.

3.3.1. Human Capital

Previous studies have shown that farmer characteristics such as gender, age, education and farming experience affect the adoption of new technology on farms [17,77,78]. Liu et al. [79] also described that the agricultural labor force has an impact on the adoption of new technologies at the farm level. Except for gender, which was measured as a dummy variable, all variables in this category were measured as continuous variables.

3.3.2. Farm Characteristics

Farm characteristics have also been indicated as important determinants of new farming technologies [80]. Farmers of large farms have more resources to invest in new technologies on their farms compared to farmers of small farms [81]. A large family size also assists farmers in achieving economies of scale that are impossible for farmers of small farms without increasing their farm area. Shahbaz et al. [75] found that landholdings have an impact on the adoption of new technology on farms. Farmers of small farms are expected to have a more risk-averse attitude compared to farmers of large farms. A large share of farm income of the total family income shows farmers’ dependence on agriculture. Farmers with a larger share of dependence on farm income have also been found to be risk-averse [82]. Farmers who are unwilling to experiment with new technologies are less likely to adopt new farm practices [83]. All variables included in this category were measured as continuous variables.

3.3.3. Institutional Characteristics

Institutional characteristics play an essential role in the diffusion of new technologies on farms because the government can change farmers’ attitudes and behavior towards new technologies through new policies. Chowdhury et al. [84] studied the impact of government policies on the diffusion and adoption of renewable technology. The Internet has also been reported as an influential factor in the adoption of new technologies on farms. The government financial support variable was measured on a five-point Likert scale and Internet use was measured as a dummy variable.

3.3.4. Perception of RET

The prior literature describes that the perceived usefulness and perceived cost-effectiveness of a technology relative to other current technologies had an influence on attitudes toward using RET [85,86]. Similarly, farmers’ access to knowledge and information also had an impact on the adoption of new farming technologies. As a result, increasing their understanding of agricultural technology can increase their likelihood of adopting new technologies [15]. Furthermore, household knowledge of environmental benefits influenced the adoption of RET [87,88]. All indicators of farmers’ entrepreneurial orientation were measured on a five-point Likert scale.

3.3.5. Farmer Entrepreneurial Orientation

In a broad sense, farmer entrepreneurship is the process of recombining resources in new ways to take advantage of opportunities and reach economic and social goals [89]. Thus, it is a cognitive characteristic that is associated with an individual’s decision-making style [90]. Therefore, agricultural entrepreneurs are often the first to try new ways of farming and doing business. They do this by taking calculated risks and working innovatively [91]. A farmer’s entrepreneurial orientation has three dimensions: risk-taking, innovativeness and pro-activeness [92].
The farmers’ entrepreneurial orientation indicators that had been utilized by Buli [93], Lumpkin and Dess [94] were modified to fit our situation in order to test the effect of the farmers’ entrepreneurial dimensions on the adoption of RE on farms. On a five-point Likert scale, all indicators of farmers’ entrepreneurial orientation were evaluated.

3.4. Principal Component Analysis (PCA)

PCA was used to reduce the number of farmers’ entrepreneurial orientation statements to a manageable number. PCA reduces the number of variables in a given set of variables by using a linear transformation. In the literature, PCA has been used to understand the farmers’ entrepreneurial orientation [75], farm innovativeness [76], household competency levels and farmer capital [95].
PCA was used in this study to separate the nine factors of the farmers’ entrepreneurial orientation into three distinct components. The Varimax rotation method was applied to decide the category of the items. The component retained had eigenvalues greater than one. The extracted variables were then used to explain the adoption of RET on farms in the analytical model [96]. Component 1 and component 2 included the items that were relevant to risk-taking and the innovativeness of the farmers, respectively. Component 3 consisted of the pro-activeness of items.
Cronbach’s alpha was used to determine the internal consistency of the items in each component. The Cronbach’s alpha value ranged from 0.71 to 0.88, higher than the minimum value of 0.60 required for internal consistency (Table 2). The Bartlett test for sphericity (p < 0.00) indicated that there exists a correlation among farmer-entrepreneurial items. It is also an indication that the correlation matrix of the items varies significantly from the identity matrix. The three extracted components (risk-taking, innovativeness and pro-activeness) were used as explanatory variables for further analysis.

3.5. Empirical Analyses

3.5.1. Exploring the Factors Affecting RET Adoption

To investigate the influence of the independent variables on the dependent categorical variables, logistic regression was used. The binary dependent variable was in the form of 1 (for adopters) and 0 (for nonadopters). As a result, binary logistic regression was used to examine the relationship between the explanatory variables and the likelihood of adopting renewable (solar) energy technology. The following formula presents the functional form of logistic regression.
f Y = e y 1 + e y = e y 1 + e − y
where Y (= β i x i ) describes the controlled variables and f(Y) depicts their outcomes. The advantage of using this function is that the response variables can take negative and positive values, while the outcome is in the range of 0 to 1. More specifically, Y may be defined as the combined effect of a set of response variables and the f(Y) can describe the probability of adopting RE. Furthermore, the Y variable represents the overall impact of all independent variables in the logistic regression and is defined in linear form as shown below.
y = β o + β 1 x 1 + β 2 x 2 + … + β n x n
β o   describes the intercept and β i expresses the coefficients of the explanatory variables to be estimated. Therefore, a negative sign of a coefficient describes that the likelihood of accepting the RET is declining as the independent variable increases. Conversely, a positive coefficient for an independent variable increases the chance of adoption of the RE on farms. Moreover, the high value of a coefficient expresses a large effect of that variable on the probability of recognizing an event. Similarly, the low value of a coefficient would also describe the small effect of an independent variable on the probability of an event occurring [97]. The odd ratio (=exp ( β i )) is defined as the likelihood of the adoption of the RE source divided by the chance of nonadoption of the RE source. As a result, if the value of an interval-independent variable increases by one unit, the odds increase by the exponential of the variable’s coefficient value. It describes the relative amount by which the probability of adopting the energy technique increases or decreases. Moreover, in the case of a categorical independent variable, the odd ratio describes the adoption comparison among different levels.

3.5.2. Measuring Technical Efficiency

Considering the efficiency concept of Farrell [98], we have estimated the technical efficiency (TE) of Chinese farms. It describes the ability of the farmers to use the minimum level of farm inputs to obtain the given level of farm outputs. The resulting efficiency scores ranged between 0 and 1. Efficiency of near 1 describes the high efficiency of a farmer and efficiency of near 0 means the low efficiency of a farmer. In this study, we constructed the DEA model for estimating the farm efficiency of adopters and nonadopters based on the suggestions of Charnes et al. [99] and Banker et al. [100]. Therefore, the TE was estimated using the following equation for the ith Chinese farm via linear programming:
M i n i m i z e ϑ ,   π   ϑ
S u b j e c t   t o − y i + Y π   ≥ 0
ϑ x i − X π   ≥ 0
π   ≥ 0
The farm output value (CNY/ha) was considered as the output ( y i ) in the model. The farm labor (hours/ha) and working capital (CNY/ha) were the inputs ( x i )   of the model. K × N describes the input (labor and working capital) matrix and M × N shows the output matrix for all farmers. The linear programming resulted in the efficiency score ( ϑ ) .   The N × 1 vector of the weights was used to define the linear combination of the peers of the ith Chinese farms.

3.5.3. Comparing Farm Technical Efficiency

The average adoption effect of RET on farm efficiency was estimated by applying the propensity score matching (PSM) technique. This PSM technique considers the two groups “control” and “treated”. The farmers who have not adopted RET were incorporated into the control group and those who have adopted the technology were considered in the treated group of the current study based on their observable characteristics [101]. The common support assumption of the efficiency score was confirmed before applying the kernel matching approach. The considerable overlapping among the propensity scores of the control and treated groups’ outcomes confirmed the common support assumption. In the matching approach, the researchers considered two effects: the average treatment effect of adoption (ATT) and the average treatment effect of nonadoption (ATU). The ATT describes how farmers who used RET improved their average outcomes. Similarly, the ATT is used in this study to compare the expected efficiency outcomes in the situation of adopting RET on the farm with the counterfactual efficiency outcomes of not adopting the RE technique. The actual efficiency outcome for adopters of RET is given below.
E Y i k | I i = K = γ k X i k
The counterfactual efficiency outcomes for the nonadopters are given below.
E Y i j | I i = K = γ j X i k
The average adoption effect (ATT) on farm efficiency based on the adoption of RET is
ATT = Y i k | I i = K − E Y i j | I i = K
ATT = X i k   ( γ k − γ j )

4. Results and Discussion

4.1. Descriptive Summary

The farm and farming characteristics of the entrepreneurs provide important information about their ability to use agricultural resources efficiently. These characteristics of farm entrepreneurs were divided into different broader categories: human capital, farm characteristics, institutional factors, perceptions about RE and farm entrepreneurial orientation.
Agriculture is a popular occupation among males and females in China. Therefore, the total sample size of this study was almost equally distributed among male and female farmers. Despite the government’s efforts, the education level in rural China remains low [102]. The average age and education of the farmers were over 45 and 8 years, respectively. Farmers were rich in farming experience, having more than 18 years of working experience in the agricultural fields. On the sampled farms, the average agricultural labor force was more than two workers.
A large majority of farmers in China possess small landholdings and farming in China has been smallholder-based [103]. Farmers with less than 2–3 hectares of land account for a sizable proportion of the rural population of approximately 230–250 million inhabitants [104]. This may be the reason that the estimated average farm size was only 2.29 hectares. The average distance between two agricultural enterprises adopting RET was more than 18 km (Table 3). The farmers were majorly dependent on agriculture for their livelihood, as evidenced by their more than 58% share of farm income of their total annual income. More than half of the farmers were utilizing the Internet for agricultural information.
A large majority of the farmers considered RE more useful and cost-effective for agriculture compared to other energy sources. Similarly, a large majority of the farmers pointed out that RE is more environmentally friendly than other energy sources currently being utilized on farms. Farming remains the most important source of income in the majority of developing countries. Therefore, governments around the globe keep investing in agriculture to make it more sustainable. A large minority of the farmers stated that information and government financial supports for adopting RET on farms were not easily available.
Farmers were risk-takers and innovators, according to the farmers’ entrepreneurial orientation indicators. A large majority of the farmers showed their willingness to take risks by deviating from current energy sources and investing more in RET. A large majority of the farmers were also interested in moving toward more cost-minimizing energy technology and obtaining up-to-date information about RE technologies. Moreover, a large minority of the farmers described that they were comparatively slower in changing farming methods than their farming fellows.

4.2. Sources of Farmer Information about RET

The major sources of information used by the farmers to learn about renewable sources of energy were electronic media (TV, radio, etc.), social media, the Internet, organizations (cooperatives, private extension services, farmer organizations, etc.), fellow farmers, the government, friends and relatives (Figure 2). Electronic media was the largest source of information, which was utilized by more than half of the farmers for information on RET, followed by fellow farmers. More than one fourth of the farmers cited government sources as their main source of information for learning about RET in agriculture. Social media was also extensively used as a source of information for getting the latest information about RET, being utilized by more than one fifth of the farmers. Different farming-related organizations also played a major role in spreading information related to the RET in the country. More than 18% of the total farmers utilized farmer organizations to get information about RET. Friends and relatives were also the source of information for more than 9% of the farmers.

4.3. Adoption Status of RET

China is the leader in producing RE. Adopting RET has been identified as a key way to cut carbon emissions in rural China and give rural Chinese families more energy [105]. Figure 3 shows the adoption status of RET on the sampled farms. Even though conventional energy sources are still dominant in providing the necessary energy for farming operations, RET are also gaining popularity among the farming community worldwide. The most common types of RE used around the world are wind energy, geothermal energy, hydropower and bioenergy. Similarly, a large majority of the farmers (74.35%) used conventional energy sources compared to those who were using RET on their farms. Han et al. [87] also reported that a large majority of Chinese rural households still use traditional energy sources.

4.4. Factors Affecting Adoption of RET at Farm Level

A logistic regression was employed to analyze the factors affecting the adoption of renewable sources at the farm level. The current model in this study outperformed an empty model by a log-likelihood chi-square of 97.76 and a prob-chi square of p > 0.01. Out of 16 explanatory variables, 10 variables were significantly affecting the adoption of RET at the farm level (Table 4). These variables were education, farm size, government financial support, usefulness, cost-effectiveness, environmental friendliness, information availability, risk-taking, innovativeness and pro-activeness.

4.4.1. Human Capital

Human capital plays an important role in sustainable agriculture and the adoption of new technologies at the farm level. The literature has shown that human capital influences farming and the adoption of new technology by farmers. Farmers’ education was positively associated with the use of RE on farms. More educated farmers are more likely to adopt RET for farm operations. A one-year increase in the education level of farmers increases the likelihood of RE source adoption by 1.44 times. The reason may be that more educated farmers are expected to be more aware of the environmental, economic and social benefits of RET. This result is contradictory to that reported by Uematsu and Mishra [106], who reported a negative association between formal education and technology adoption. However, the results are in line with previous studies conducted by Mwangi and Kariuk [107] and Anang et al. [108], who also reported that education increases the likelihood of adoption of new technologies at the farm level.

4.4.2. Farm Characteristics

Farm characteristics display important information about farm resources. The larger the farm sizes, the easier it is for farmers to achieve economies of scale. Moreover, large farm sizes are also associated with a higher degree of farmers’ specialization and a greater willingness of farmers to spend more time and invest financial resources to learn and master new technologies to expand farm income [82]. The farm size of the farmers was positively related to their adoption of renewable sources on their farms. A one-hectare increase in farm size increases the probability of adopting RE resources by 1.84 times. This finding resonates with other empirical studies [109,110,111,112,113] on the adoption of farm technology, which show that farm size has a positive and significant impact on the adoption of agricultural technology.

4.4.3. Institutional Factors

Institutional factors work as catalysts in the adoption and diffusion of technologies on farms. Government financial support is particularly important for increasing the adoption of RET in developing countries due to the scarcity of financial resources. Government financial support positively influenced the adoption of RE usage on farms. Farmers with government financial support were 2.38 times more likely to adopt a RET on their farms than farmers without government financial support. Wu [114] also found that government financial support increases the adoption of modern technologies on farms. Government departments can help farmers learn about new technology by letting people know about it and promoting it. Aside from that, the fact that the government provides subsidies to farmers who use new technologies may entice them to do so [115]. This finding is in line with the study conducted by Yoon et al. [116] and Xie and Huang [117], who reported that government financial support positively affects the adoption of new farming technologies.

4.4.4. Perception of RET

The perception of farmers of agrarian technology is the key factor affecting the adoption of new technologies on farms [108,118]. Thus, the perception of RE is one of the most vital factors, having the potential to affect its adoption on farms. Farmers will adopt RET only if they perceive them as beneficial and cost-effective for their farm business. The perceived usefulness of renewable agriculture had a significant positive effect on the adoption of RET on farms. Farmers who perceived renewable agriculture as more useful and cost-effective compared to conventional energy sources were, respectively, 1.89 and 2.13 times more likely to adopt it on their farms. The results are consistent with the previous studies conducted by Challa and Tilahun [110], who also reported that perceptions of the cost of new technology significantly influence its adoption on farms. Similarly, perceptions of environmental friendliness and ease of access to information were found to be positively associated with farms’ adoption of RET. Farmers who perceived renewable agriculture as more environmentally friendly than traditional energy sources were 1.54 times more likely to use it on their farms. Li et al. [119] also found that farmers who perceive a farm technology as useful and environmentally friendly are more likely to adopt it on their farms. Bolfe et al. [120] also described that information availability about the new technology also had an influential impact on the adoption of new technologies.

4.4.5. Farmer Entrepreneurial Orientation

The agricultural environment is constantly changing and the success of a farm business depends on its ability to adapt to changing environmental and market conditions. This requires the farm operator to work entrepreneurially toward sustainable agricultural development. Adoption of new cost-efficient and environmentally friendly farming technologies is the key to a successful farm business. The entrepreneurial orientation of farmers is critical to the adoption of new farm technology. Thus, the dimensions of farmers’ entrepreneurial orientation that are of particular interest in this paper are risk-taking, innovativeness and proactivity. All three dimensions positively influenced the adoption of RET on farms. The questions concerning risk-taking concerned only the adoption and use of RE on farms. Risk-taking farmers were 2.06 times more likely to adopt RETs on their farms. Similarly, innovative farmers were 2.24 times more likely to adopt RET on their farms. These findings resonate with other prior studies [92,121] on the adoption of farm technology, which show that farmers’ entrepreneurial orientation has a positive and significant effect on the adoption of agricultural technology. Hansen [121] further stated that the pro-activeness of farmers is necessary to adopt new farm technologies according to farm needs. Verhees et al. [122] reported that farmer pro-activeness is the key farmer entrepreneurial orientation dimension for farm performance.

4.5. Adoption Effects of Renewable Sources of Energy on Farm Technical Efficiency

Since developing countries have limited financial resources to invest in new technologies to increase agricultural productivity and production, it is very important for farmers to use the presently available resources efficiently for sustainable agriculture [123]. Adopting new technologies in agriculture has had a big impact on the well-being of farmers and agricultural productivity [124]. RE is essential for increasing farm productivity and income as well as for enhancing the climate change adaptation of farming enterprises [1]. The results of the propensity score matching technique showed that there was a significant difference between the technical efficiency of RE adopters and nonadopters. The farm technical efficiency of RE adopters was 10% higher than that of nonadopters. The reason for this may be that RE technologies are much cheaper than conventional energy sources. This can also be concluded from Table 5, which shows that RE adopters would have had 10% lower farm technical efficiency if they had not adopted RET on their farms. The result can also be interpreted alternatively; namely, that RE adopters have an opportunity to increase their farm technical efficiency by 10% through the adoption of RET on farms. The results are consistent with prior studies conducted by Suárez and Quesada [125], Anang et al. [108] and Delay et al. [126], who also found that the adoption of new technology on farms positively affects the technical efficiency of the farmers.

5. Conclusions and Policy Recommendations

Agriculture and energy are strongly linked and almost one third of total worldwide energy consumption is utilized in agriculture and related food systems. This energy also accounts for one third of GHG emissions from agricultural food systems. This study suggests that agriculture must be transformed to fulfill the existing and forthcoming demand for food and energy in an impartial and ecologically sustainable manner. Sustainable Development Goals (SDGs) and the Paris Agreement on Climate Change cannot be achieved without a coordinated strategy for the energy transition and changing agriculture. Agriculture products use energy throughout the supply chain, starting at the farm and ending on the table. Nevertheless, the existing patterns of energy consumption are both unsustainable and inadequate since rural sections of emerging nations have limited access to electricity. Therefore, this study explores the factors affecting RET and also determines the impact of its adoption on farm technical efficiency. Moreover, the study also examines the sources of information on RET.
The findings showed that one fourth of the total number of farmers adopted RET on their farms. The key source of information about RE was electronic media, which was utilized by more than half of the farmers, followed by fellow farmers, which was almost adopted by one third of the farmers. The logistic regression results depicted that education, farm size, government financial support, perceptions of RE (usefulness, cost effectiveness, environmental friendliness and information availability) and farmer entrepreneurial orientation dimensions (risk-taking, innovativeness and pro-activeness) all significantly affect the adoption of RET on farms. The propensity score matching results indicated that RE adopters had higher farm technical efficiency compared to nonadopters. This advantage, along with the environmental benefits of renewable technology on farms, provides a strong argument for decision-makers across sectors to develop policies and initiatives to expedite the use of RE in farming. Thus, this study has important implications for assisting policymakers in attaining SDG-2 (zero hunger) and SDG-7 (affordable and clean energy) in developing countries.
This study has the following policy implications: Firstly, this study suggests that the government can play a vital role in improving the adoption of RE on farms. Policymakers should integrate cross-sector perspectives into national and regional policies for reforming the energy and food systems by creating a conducive environment. This environment must have (1) specific strategies and programs and (2) intersectoral coordination between the government, the business sector, civil society and end users at the national and subnational levels. Secondly, policymakers should work to improve the access of agricultural enterprises to finance in order to increase the adoption of RE on farms. Thirdly, there is a need to improve the information and perception of farmers of RET by using electronic media and other commonly used information channels in the country. Finally, the government should work to improve farmers’ entrepreneurial skills in order to increase the adoption of RET on farms throughout the country.

Author Contributions

Conceptualization, J.W. and W.L.; methodology, P.S. and S.u.H.; software, J.W., W.L. and P.S.; validation, P.S., S.u.H. and W.L.; formal analysis J.W., W.L. and P.S.; investigation, J.W., W.L. and S.u.H.; resources, J.W. and W.L.; data curation, J.W. and W.L.; writing—original draft preparation, W.L., P.S. and S.u.H.; writing—review and editing, W.L., S.u.H. and P.S.; visualization, J.W., W.L. and S.u.H.; supervision, S.u.H. and P.S.; project administration, P.S. and S.u.H.; funding acquisition, J.W. and W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the institutional review board of Shihezi University Xinjiang, China.

Informed Consent Statement

Informed consent was duly received from participants.

Data Availability Statement

The data can be obtained from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 05611 g001 550
Figure 1. Study area (Sichuan province).
Figure 1. Study area (Sichuan province).
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Figure 2. Sources of information that farmers used to learn about RE.
Figure 2. Sources of information that farmers used to learn about RE.
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Figure 3. Adoption status of RET.
Figure 3. Adoption status of RET.
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Table
Table 1. Variable description used in the study.
Table 1. Variable description used in the study.
VariableDescriptionUnit of MeasurementVariable Type
Human capital
Gender Gender of respondent 0 = Female, 1 = maleDummy
Age Age of the respondent YearsContinuous
Education Schooling years of the respondentYearsContinuous
Farming experience Farming experience of the respondent YearsContinuous
Agriculture labor force Agricultural employeesNumbersContinuous
Organizational membership Whether respondent is member of any agriculture-related organization0 = No, 1 = YesDummy
Farm characteristics
Farm sizeTotal cultivable land HectaresContinuous
Distance to nearest RE-using enterprise Distance to nearest RE-using enterpriseKilometersContinuous
Farm income share Farm income share of total incomePercentage Continuous
Perception of RE
Usefulness Usefulness of RET compared to other available energy sources Very useless (1) to very useful (5)Likert Scale
Cost-effectiveness Cheapness of RET compared to other available energy sources Very expensive (1) to very cheap (5)Likert Scale
Environmental friendliness Environmental friendliness of RET compared to other available energy sourcesNot at all environmentally friendly (1) environmentally friendly (5)Likert Scale
Information availabilityInformation availability about RETVery difficult (1) to very easily availableLikert Scale
Institutional characteristics
Government financial supportGovernment grants for installing renewable energy sources on farmVery scarce (1) to very abundant (5)Likert Scale
Internet Internet use for agriculture by respondent 0 = No, 1 = YesDummy
Entrepreneurial orientation
Risk-taking
I prefer to adopt new energy sources on my farm instead of continuing with the present ones.Completely disagree (1) to completely agree (5)Likert Scale
I would further invest in my farm energy sources, even with the current challenging farming environment.Completely disagree (1) to completely agree (5)Likert Scale
I am always open to adopting new environmentally friendly energy technology on my farm.Completely disagree (1) to completely agree (5)Likert Scale
Innovativeness
I like to try out new environmentally friendly energy technology on my farm.Completely disagree (1) to completely agree (5)Likert Scale
I like to know the latest about new environmentally friendly farm energy technologies.Completely disagree (1) to completely agree (5)Likert Scale
I often try adopting new cost-minimizing farming practices.Completely disagree (1) to completely agree (5)Likert Scale
Pro-activeness
I react faster to changes in my farm’s environment compared to other farmers.Completely disagree (1) to completely agree (5)Likert Scale
I am one of the first farmers in my village to use new farming methods. Completely disagree (1) to completely agree (5)Likert Scale
I always look for ways to improve the farm.Completely disagree (1) to completely agree (5)Likert Scale
Table
Table 2. Principal component analysis for the farmers’ entrepreneurial orientation.
Table 2. Principal component analysis for the farmers’ entrepreneurial orientation.
Item Component 1
(Risk Taking)		Component 2
(Innovativeness)		Component 3
Pro-Activeness		Cronbach’s AlphaPercentage Variance Explained
I prefer to adopt new energy sources on my farm instead of continuing with the present ones.0.87 0.8830.65
I would further invest in my farm energy sources, even with the current challenging farming environment.0.81
I am always open to adopting new environmentally friendly energy technology on my farm.0.79
I like to try out new environmentally friendly energy technology on my farm. 0.71 0.7525.44
I like to know the latest about new environmentally friendly farm energy technologies. 0.66
I often try adopting new cost-minimizing farming practices. 0.62
I react faster to changes in my farm’s environment compared to other farmers. 0.810.7117.56
I am one of the first farmers in my village to use new farming methods. 0.64
I always look for ways to improve the farm. 0.60
KMO = 0.721 Bartlett Test for Sphericity = p < 0.00.
Table
Table 3. Sociodemographic characteristics and entrepreneurial orientation indicators of the farmers.
Table 3. Sociodemographic characteristics and entrepreneurial orientation indicators of the farmers.
CharacteristicsMean (SD)
Human capital
Gender 0.53 (0.44)
Age 45.65 (8.76)
Education 8.43 (2.39)
Farming experience 18.65 (5.66)
Agriculture labor force 2.19 (1.13)
Organizational membership 0.33 (0.49)
Farm characteristics
Farm size2.29 (0.73)
Distance to nearest RE-using enterprise 18.54 (9.87)
Farm income share 58.47 (13.42)
Perception about RE
Usefulness4.76 (0.56)
Cost-effectiveness 4.14 (1.38)
Environmental friendliness 4.60 0.65)
Information availability3.07 (1.76)
Institutional support
Government financial support3.04 (1.89)
Internet 0.51 (1.06)
Farmers’ entrepreneurial orientation
Risk-taking 3.67 (1.45)
I prefer to adopt new energy sources on my farm instead of continuing with the present ones.3.79 (1.43)
I would further invest in my farm energy sources, even with the current challenging farming environment.3.90 (1.44)
I am always open to adopting new environmentally friendly energy technology on my farm.3.00 (2.67)
Innovativeness 4.35 (0.78)
I like to try out new environmentally friendly energy technology on my farm.4.01 (1.45)
I like to know the latest about new environmentally friendly farm energy technologies.3.23 (1.66)
I often try adopting new cost-minimizing farming practices.4.89 (0.23)
Pro-activeness 3.45 (1.67)
I react faster to changes in my farm’s environment compared to other farmers.2.93 (2.21)
I am one of the first farmers in my village to use new farming methods. 4.12 (1.23)
I always look for ways to improve the farm.3.64 (1.54)
Table
Table 4. Factors affecting adoption of RET at farm level.
Table 4. Factors affecting adoption of RET at farm level.
Variable Coef. (Std. Errs.)Odd Ratio
Constant−12.39 (3.75)
Human capital
Gender −0.76 (0.65)0.47
Education 0.37 (0.12)1.44 *
Farming experience −0.26 (0.30)0.77
Agriculture labor force 0.48 (0.36)1.61
Organizational membership 0.19 (0.27)1.20
Farm characteristics
Farm size0.61 (0.29)1.84 **
Distance to nearest RE-using enterprise −1.54 (0.98)0.21
Institutional factors
Government financial support1.63 (0.87)2.38 ***
Internet0.45 (0.41)1.56
Perception of RET
Usefulness 0.64 (0.33)1.89 ***
Cost-effectiveness0.76 (0.21)2.13 *
Environmental friendliness 0.43 (0.15)1.54 *
Information availability0.98 (0.43)2.66 **
Farmer entrepreneurial orientation
Risk-taking0.72 (0.24)2.06 *
Innovativeness 0.81 (0.20)2.24 *
Pro-activeness 0.42 (0.23)1.54 ***
Log-likelihood −97.76
Prob > chi20.00
Pseudo square0.61
*, ** and *** show significance level at 1%, 5% and 10%, respectively.
Table
Table 5. Adoption effect of renewable source of energy on farm technical efficiency (propensity score matching).
Table 5. Adoption effect of renewable source of energy on farm technical efficiency (propensity score matching).
Adoption StatusAverage Difference
AdoptersNonadopters
0.78 (0.07)0.68 (0.06)0.10 (0.04) **
** shows significance level at 5%.
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Wang, J.; Li, W.; Haq, S.u.; Shahbaz, P. Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies. Sustainability 2023, 15, 5611. https://doi.org/10.3390/su15075611

AMA Style

Wang J, Li W, Haq Su, Shahbaz P. Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies. Sustainability. 2023; 15(7):5611. https://doi.org/10.3390/su15075611

Chicago/Turabian Style

Wang, Jinxing, Wanming Li, Shamsheer ul Haq, and Pomi Shahbaz. 2023. "Adoption of Renewable Energy Technology on Farms for Sustainable and Efficient Production: Exploring the Role of Entrepreneurial Orientation, Farmer Perception and Government Policies" Sustainability 15, no. 7: 5611. https://doi.org/10.3390/su15075611

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