Abstract
Because of global environmental problems, low-carbon agriculture has gained increasing importance both in developed and developing countries. Hence, there is a need to find ways to develop more efficient agricultural systems. The purpose of this article is to identify the drivers of low-carbon agriculture on farms in the Wielkopolska region (in Poland). We aimed to take an original approach to investigate low-carbon agriculture with a unique set of different economic and environmental variables and contribute to the literature, which is not very extensive in terms of microeconomic research, including research on farmers in the Wielkopolska region. Therefore, we employed a multiple-factor measurement model for structural equation modeling (SEM) of data collected individually from 120 farms in 2020. As a result, we formulated the following conclusions: the increasing productivity of factors (land, labor, and capital) have a positive effect on low-carbon farming, just as increasing fertilizer and energy efficiency. Moreover, thermal insulation is also important for low-carbon agriculture, with efficiency of fertilizer use being the most important factor. We believe that the issues of farm use of fertilizers and thermal insulation of buildings should be more broadly included in energy policy, both at the national and the European Union (EU) levels. Some of these factors however are already present in the common agricultural policy (CAP) for 2021–2027.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Introduction
Climate change has an increasingly significant impact on a number of industries and economic sectors, and mainly refers to seven sectors: energy, industry, agriculture, water and sanitation, transportation, urban development, and tourism (Chen et al. 2020). In agriculture, factory farming is a massive contributor to global warming because of the high methane and nitrous oxide emissions (Rafiq et al. 2016). This leads to an increased interest in low-carbon agriculture, which may be defined as an agricultural system that enables efficient production of the raw materials, food, feed, and fibers while reducing energy inputs and greenhouse gas (GHG) emissions from agriculture and respecting the principles of sustainable development (Piwowar 2019). This means that it is possible to simultaneously achieve both economic (income, economic performance) and environmental (provision of public goods, protection of the environment, internalization of externalities, and preservation of biodiversity) gains. Therefore, a global and local shift in farming can help the transition to a low-carbon economy (Norse 2012). And as Sharma et al. (2021a) emphasized, higher income level easier allows a country to adopt low-carbon practices in the economy (including industry and households). According to Clapp et al. (2010), national plans and strategies for low-emission and climate-resilient economic growth are crucial for sustainable development. However, low-carbon farming requires high annual investments to meet the goals of the Paris Agreement (Van Veelen 2021). Therefore, more attention should be paid to these issues in energy policy. Today, some Polish researchers are investigating measures for reducing the carbon footprint of farming practices. For example, Wiśniewski and Kistowski (2018) analyzed GHG emissions and carbon footprint in Polish agriculture at the local level. In turn, Kistowski and Wiśniewski’s (2020) study puts a particular focus on emissions in the municipalities.
The motivation for this paper is the research gap which exists especially in Central European countries with a medium level of agricultural development in identifying the economic determinants of a low-carbon economy. These problems do not have a clear-cut solution. Researchers have pointed out many factors affecting CO2 emissions from agriculture (Zhao et al. 2018). Therefore, the objective of this study was to identify the factors that contribute to the transformation towards low-carbon agriculture in Wielkopolska farms (in Poland). It is worth noting that Poland is one of the largest consumers of mineral fertilizers in Europe (Piwowar 2021). Hence, research in this field seems to be needed and urgent, as Polish agriculture faces several important challenges. The following two hypotheses were adopted: H1: productivity of factors such as land, labor, capital (production factors) positively affects low-carbon farming; H2: increasing fertilizer efficiency and energy efficiency is conducive to low-carbon farming. Our original contribution is that we have taken a unique approach to study low-carbon agriculture (with a set of different economic variables) and employed a well-fitted estimation model for microeconomic data collected from 120 farms in the Wielkopolska region, using structural equation modeling. Structural equation modeling, an extension of the general linear model (StataCorp 2017), provides a more general framework for performing factor analysis. The strength of SEM is that it has all-encompassing possibilities, because it includes path analysis and multivariate regression as special cases. SEM is even more useful when dealing with composite indicators, which is a significant advantage because most simple variables do not sufficiently describe complex theoretical phenomena and concepts. Usually, SEM combines multiple indicators for each latent variable and supports complex paths connecting latent variables (Garson 2015). Moreover, the goodness of the model fit may be verified using many different statistics. The theoretical frameworks for our research are environmental economics and sustainable development (Rogall 2004; Goldin and Winters 2010). Our results may be a basis for formulating recommendations in these areas.
The remainder of the paper is organized as follows: “Literature review” offers a critical review of the literature on carbon emissions, and “Materials and methods” explains the data and methodology. “Results and discussion” describes the results and contains discussion. “Managerial implications” outlines some qualitative managerial implications, while “Conclusion and policy implications” contains conclusions and policy implications.
Literature review
The scale of global emissions from agriculture has been investigated by numerous studies in recent years (Eyuboglu and Ezar 2020; Groeneveld et al. 2003; Haider et al. 2020; Moran and Knook 2017; Qiao et al. 2019). As emissions concern externalities and international public goods (like the atmosphere), they are of global importance, even though they occur at a local level (Muhammad and Khan 2021). There are also many country-level studies that identify factors affecting CO2 emissions in agriculture (Xu and Lin 2017) or drawing attention to the problem of ecological footprint (Alvarado et al. 2021; Khan et al. 2021a; Zia et al. 2021). Interesting studies linked to the relationship between growth processes and carbon emissions are examples of the use of environmental Kuznets curve (Dogan and Seker 2016b; Khan et al. 2021a; Koondhar et al. 2021a; Shahbaz et al. 2020; Sinha and Shahbaz 2018). Other papers highlighted that energy efficiency is linked with the per capita income from the country’s overall economic growth (Zakari et al. 2021). There are also numerous studies that pointed to the impact of financial development (including foreign direct investment) on carbon dioxide (CO2) emissions (Ahmad et al. 2019; Muhammad and Khan 2021; Muhammad et al. 2021; Shahbaz et al. 2020, 2021), carbon intensity (Sharma et al. 2021b), and study drawing attention to the fact that increasing financial risk directly reduces CO2 (Zhao et al. 2021). In turn, Piwowar (2019) presented theoretical and practical challenges for low-carbon agriculture in Poland. As he pointed out, reducing gas emissions in the agricultural sector requires the introduction of innovative techniques and tools to increase the efficiency of production. Syp and Osuch (2018) adopted an interesting approach to assessing GHG emissions from conventional farms. They argued that there are significant differences in the intensity of the GHG emissions among dairy farms in Poland: the highest emissions were noted in medium-small and medium-large farms.
The higher the degree of agricultural mechanization is, the higher the energy consumption (Eyuboglu and Ezar 2020). Therefore, there are many actions that farmers can take to reduce emissions. These include increasing carbon sequestration in the soil (West and Marland 2002; Norse 2012; Reay 2020; Xiao et al. 2021); reducing the use of mineral fertilizers and replacing them with recycled fertilizers (Kytta et al. 2021), as well as using fertilizer appropriately and decreasing energy intensity (Solinas et al. 2021), and decreasing the use of mineral pesticides while introducing integrated pest management (Piwowar 2021); lowering the overuse and misuse of nitrogen (Norse 2012); reducing energy consumption and increasing energy efficiency (Piwowar 2019); replacing fossil fuels by renewable energy sources (Dogan and Seker 2016a; Eyuboglu and Ezar 2020, Paramati et al. 2018); and improving animal feeding techniques and animal husbandry systems as well as designing and using energy-efficient livestock buildings (Kistowski and Wiśniewski 2020). It is also important to optimize animal nutrition and efficient waste management, which have a positive impact on reducing emissions. A wide range of mitigation technologies and measures was reported and discussed by Eyuboglu and Ezar (2020) and Xiong et al. (2021). The literature also highlights the potential to improve overall productivity by reducing emissions per unit of product in some systems (Johnson et al. 2007). One of the most important measures for reducing CO2 emissions in farms is thermal retrofitting of agricultural buildings, which can also lead to reduced energy consumption (Hörndahl 2008). Some studies have also considered the issue of crop fertilization in the context of CO2 emissions. They mainly focused on emissions resulting from the production of fertilizers and plant protection products. These problems were investigated, for example, by Bouwman et al. (2002). In turn, Shahbaz et al. (2016), using Portuguese annual data, highlighted that environmental degradation in terms of CO2 emissions can be controlled by using energy-efficient technologies.
Within the European Union, the current course of action, especially since the beginning of the twenty-first century, further reinforces the move towards a zero-emission economy in member countries. Numerous studies (Jensen et al. 2019; Xiong et al. 2016; Wang et al. 2020) also highlight the desirable direction of such changes. According to Moran and Knook (2017), some of the CO2 reduction measures in agriculture are relatively cheap in comparison to the cost of reducing emissions in other sectors, such as energy or heavy industry. In turn, Akbar et al. (2021) emphasized in their research, based on experiences in China, that changes in agricultural planting structure, agricultural value-added, and agricultural scale management are also important factors affecting agro-ecological growth; thus, they reduce CO2 emissions. Piwowar (2020) emphasized the essential role of agricultural biogas in low-carbon and circular economy. What distinguishes our research approach from the previous studies is the use of microeconometric quantitative as well as qualitative data collected from individual farms and employing structural equation modeling.
Materials and methods
We employed structural equation modeling, which is considered to be one of the best techniques for analyzing interdisciplinary issues, also within environmental economics (Brown 2015; Hooper et al. 2008). Therefore, it may be applied in our research, which examines economic and environmental interdependencies. SEM combines the advantage of analysis of variance, regression, and factor analysis, extending them with the possibility of modeling cause-and-effect relationships using latent variables (Garson 2015; Brown and Moore 2012; OECD 2008). Any researcher using SEM may identify indirect, direct, and total independencies between variables, both latent and observed variables, and between all specified variables (Garson 2015; Anghel et al. 2019). Hoyle (2012), Kline (2011), and StataCorp (2017) provide very broad and valuable descriptions of SEM and different SEM models, while Garson (2015) discusses the many advantages of SEM. We used a multiple-factor measurement model as a type of SEM, where we have a firm idea and clear expectations about the latent factors and variables that most likely load onto each construct. This model is usually used to test hypotheses or theories about the factors or latent variables that are expected to be found (Brown 2015; Hadrich and Olson 2011). In such a model, every hypothesized connection should be logically justified, and there must be a coherent story behind the entire model (Iacobucci 2010).
According to StataCorp (2017), in any given analysis, there are typically several variables of each type. Vectors of the four main variable types are denoted y, x, η, and ξ.
The vector of all endogenous variables is:
The vector of all exogenous variables is:
The vector of all error variables is:
Structural equation modeling can fit models of the form (StataCorp 2017):
where Yi is a vector of latent endogenous variables for unit i; B = [βij] is the matrix of coefficients giving the expected effects of the latent endogenous variables (Y) on each other; Xi is the vector of exogenous variables; Γ = [γij] is the matrix of coefficients on exogenous variables; α = [αY] is the vector of intercepts for the endogenous variables; \({\varsigma }\) is the vector of errors and is assumed to have mean 0 and Cov (X, \({\varsigma }\)) = 0. Let:
where κ is the vector of latent exogenous means; φ is the matrix of latent exogenous variances and covariances; ψ is the matrix of latent endogenous error variances and covariances.
Then, the mean vector of the endogenous variables is:
The variance matrix of the endogenous variables is:
And the covariance matrix between the endogenous variables and the exogenous variables is:
Let Z be the vector of all variables:
Then, its mean vector is:
And its variance matrix is:
In our model, the maximum likelihood (ML) method is applied. We decided to use this technique because it is flexible, helps to provide reliable results, and allows to analyze various kinds of model in an easy, straightforward, and efficient way. Moreover, maximum likelihood is the method which is recommended and used by default in SEM. The estimation method of the ordinary least square (OLS) is identical to maximum likelihood; however, the first one may be used only in the case of normal distribution of residuals. Therefore, ML is more general and flexible. For the BHHH optimization technique (Berndt-Hall-Hall-Hausman algorithm) and when computing observation-level scores, the log-likelihood (logL) for θ is computed as:
where θ is a vector of unique model parameters; wt is corresponding weight value, where t = 1, …, N; k is the number of observed variables; Ʃo is the submatrix of Ʃ corresponding to the observed variables; zt is the vector of all observed variables for the tth observation; μo is the subvector of μ corresponding to the observed variables.
We used the results of a survey carried out in 2020 on a group of 120 agricultural holdings from the Wielkopolska region in Poland. The holdings were selected based on the economic size of the farms (ES)Footnote 1 and the type of farming (TF).Footnote 2 The research tool was an interview questionnaire. Because of the relatively low number of holdings from groups ES1 and ES6 which keep agricultural accounts in Wielkopolska (31 and 9, respectively), farms from groups ES2 to ES5 were selected for the research. The production types included were TF1 and TF5–TF8. A quota selection of the farms was applied. The planned number of the surveyed farms (120) was divided proportionally according to both the economic size (ES2–ES5) and production type (TF1, TF5–TF8), in the group of Wielkopolska farms that keep agricultural accounts according to Farm Accountancy Data Network (FADN). Then, specific numbers of farms were selected for the study using the records of the Institute of Agricultural and Food Economics-National Research Institute in Warsaw. Next, interviewers were assigned to the selected farms.
Wielkopolska is one of 16 voivodships (regions) in Poland. The agricultural area accounts for 11.3% of all of Poland’s area, and the value of agricultural gross output is 17.4% of the total value for Poland (Grzelak et al. 2020). In our study, we focused on drivers of low-carbon agriculture in the Wielkopolska region. We defined low-carbon agriculture using a set of factors, and then we analyzed the relationships between them and different economic variables, including the factors of productivity. The variables were:
-
fert_u_eff: fertilizer use efficiency (the ratio of agricultural output in thousands of PLN (Polish złoty)/1000 kg fertilizer used)
-
fert_eff: fertilizer efficiency (the ratio of agricultural output in thousands of PLN/expenditure on fertilizers in thousands of PLN)
-
nrg_eff: energy efficiency (the ratio of agricultural output in thousands of PLN/expenditure on energy in thousands of PLN)
-
land_prod: land productivity (the ratio of agricultural output in thousands of PLN/agricultural area in hectares)
-
lab_prod: labor productivity (the ratio of agricultural output in PLN/number of person-hours worked on a farm)
-
cap_prod: capital productivity (the ratio of agricultural output in PLN/the value of total assets in PLN),
-
inc_share: share of agricultural income in the total income of a household (in %)
-
land_val: land value in thousands of PLN (standardized variable)
-
income: agricultural income in thousands of PLN (standardized variable)
-
therm_ins: thermal insulation of livestock buildings (dummy variable: 1 = yes; 0 = no)
-
low_carbon (latent variable): low carbon agriculture as a series of actions to achieve economic (agricultural) goals while respecting the natural environment
-
productivity (latent variable): productivity of factors (land, labor, and capital).
It may seem surprising that the group of variables that make up the low-carbon latent variable does not include a measure of livestock production intensity (e.g., the number of large units per hectare), which has a direct impact on GHG emissions. However, it was not possible to generate a well-fitted model when including this variable. The reason could be that livestock density exceeded the level considered harmful for the environment, 2 large units/ha, in only 18% of the farms (Kopiński and Madej 2006). This figure is due to the official maximum annual limit of 170 kg of nitrogen from livestock manure per 1 ha. On the other hand, the median for livestock intensity was 1.1 and in 17% of the surveyed farms, there was no livestock production. Agriculture in the Wielkopolska region is not intensive compared to other EU regions, which is typical for countries with a medium level of development. Therefore, this variable plays a smaller role in this case.
As we employed structural equation modeling with a multiple-factor measurement model, for every construct, one crucial and logically justified observed variable must be a reference variable. In our case, it is fertilizer use efficiency (fert_u_eff) for the low_carbon agriculture (low_carbon) construct and labor productivity (lab_prod) for productivity construct. The coefficients of other variables are interpreted with reference to the selected variable. The descriptive statistics are given in Table 1.
The econometric strategy was as follows:
-
We formulated research goals and hypotheses on the drivers of low-carbon agriculture in Wielkopolska region, namely, H1: factor productivity (land, labor, and capital) positively affects low-carbon farming; H2: increasing fertilizer efficiency and energy efficiency is conducive to low-carbon farming.
-
Taking into account that different economic factors may be interconnected, we hypothesized and tested additional covariances and interactions.
-
We used structural equation modeling with multiple-factor measurement to determine if the hypothesized relationships hold true. As one of the variables is a dummy variable (thermal insulation), we used a polychoric correlation matrix (instead of a raw database). All estimation was done using STATA 15 software.
-
After estimating the model, we checked the model fit Table 3).
-
The final model is presented as a graph (Fig. 1), and full results are shown in Tables 2 and 3.
Results and discussion
Our estimated model (Fig. 1) meets all criteria for acceptable model fit, including RMSEA: 0.009, CFI: 1.0, TLI: 0.999, SRMR: 0.056, and CD: 0.845. Detailed data is presented in Table 3.
The low_carbon latent variable is most significantly determined by the efficiency of fertilizer use. Much lower significance occurred for the thermal insulation of livestock buildings variable. This resulted from the relatively low prevalence of thermal insulation of buildings in the Polish countryside (23% of farms). However, this is going to change due to the growing popularity of solutions that increase energy efficiency, which in turn contributes to development of low-carbon farming. The lower impact of energy efficiency on the low_carbon construct may be caused by the relatively low level of agricultural development in Poland, which means that its energy intensity is not high. In turn, Rafiq et al. (2016) investigated the impact of sectoral production allocation, energy use patterns, and trade openness on pollutant emissions in a group of 53 countries, including European countries like Bulgaria, Italy, and Portugal, whose agricultural sectors share some characteristics with Poland. They found that energy intensity is a driver of pollution emissions, hence, increasing energy efficiency promotes low-carbon agriculture. Khan et al. (2021b) also pointed out the role of decreasing energy intensity, as well as adopting environmental-related technologies and promoting renewable energy sources in reducing environmental degradation, in Canada. When investigating the efficiency of energy use in some OECD countries (including Poland), Iram et al. (2020) showed that energy efficiency is crucial for environmental performance and carbon emission reduction. Moreover, as these authors pointed out, achieving an optimal level of energy efficiency should be prioritized over economic performance to improve environmental efficiency in these countries. In turn, Piwowar (2019) stated that, from the perspective of Polish agriculture, improving energy efficiency means, first of all, lowering fuel consumption. As we mentioned, production and use of fertilizers is strongly related to energy consumption. Therefore improving the efficiency of fertilizers (understood in our research as the amount and cost of fertilizers used) reduces energy use and promotes energy efficiency. According to Piwowar (2019), the use of fertilizers in Polish agriculture contributes to large emissions of nitrogen oxides. To increase fertilizer use efficiency, it is necessary to implement efficient, low-input agrotechnical practices. Hence, improvement of fertilizer efficiency use is an important task for achieving low-emission agriculture in Poland. Similar conclusions have been drawn in studies conducted in other countries. Xiong et al. (2021) proved that decreasing the total amount of fertilizers and increasing their efficiency are among the most important low-carbon technologies and management measures of high agricultural carbon productivity in Taihu Lake Basin, China. Koondhar et al. (2021b) proved that farmers in Pakistan should reduce chemical fertilizer application and increase the use of energy from renewable sources in order to increase grain food production, while reducing carbon emission. All these findings support the results obtained in our model.
In turn, the latent variable productivity is stimulated by the productivity of individual production factors (land, labor, and capital separately), as well as income and the share of agricultural income in the total income of a farmer’s household.
We should draw attention to the fact that labor productivity is determined by the value of land. Farmers who use agricultural land with higher market value may be more motivated to use labor more efficiently. This motivation can result from opportunity costs, which are very important at the microeconomic level, i.e., when a farmer is taking decisions about production processes. While we found that the amount of resources used in agricultural processes is not as crucial as we expected, we found that resources productivity plays a crucial role. The study shows that there is a strong association between our latent variables, low-carbon agriculture and resources productivity, and other variables, including income. Therefore, it is plausible to achieve both economic and environmental gains on a farm. Transforming production techniques as well as modernizing farming practices and introducing innovations are important in reducing the environmental footprint of agriculture. Such findings are supported by other researchers, who highlighted that promotion of best farming techniques, eco-innovation, and services that require capital are associated with improving environmental performance (Picazo-Tadeo et al. 2011; Van Grinsven et al. 2019).
There is a need to design and implement suitable institutional arrangements at the local, national, and EU levels. As Norse (2012) said, these are some key policy elements that should be considered for the global and domestic transition to low-carbon agriculture. They include (1) focusing on the largest sources of GHG with the lowest mitigation costs, (2) promoting multifunctional agriculture that achieves economic, environmental, and social objectives, and (3) prioritizing those measures that improve food security and are dedicated to poor groups, especially. Another important policy framework involves the substitution of conventional fossil energy with renewable energy, also in agriculture, including the European Union countries (Paramati et al. 2018). In turn, the great potential of renewable energy sources in reducing environmental pressure is described by Tillaguango et al. (2021) for Latin American countries and Oryani et al. (2021) in Iran. At the same time, the role of policy is also significant. Hamilton and Kelly (2017) opt for a multi-sector reform, which is important for the whole climate change policy, while Sharma et al. (2021b) suggested shaping a multipronged policy framework that allows to consider the need for complementarity between economic growth and environmental goals. Policymakers should promote cooperation with other countries and multinational corporations when introducing pro-environmental actions (Islam et al. 2021) and global economies are recommended to strive for integrating policies for emission reduction with the ones related to economic growth (Rehman et al. 2021). As summarized by Wiśniewski and Kistowski (2018), it is necessary to look at agriculture and rural areas more broadly when introducing low-carbon economy plans and strategies because agriculture (including forestry) has the potential to sequester carbon and reduce GHG emissions. According to Kampas et al. (2021), Poland may be seen as a country which is in line with the ecological modernization paradigm as Poland’s economic growth is not harmful to the environment or natural resources. In the context of patterns of CO2 emissions, Poland was compared to the Czech Republic, Germany, the Slovak Republic, and Sweden based on previous data and experiences (Narayan et al. 2016). Taking into account the abovementioned issues, low-carbon agriculture could definitely further contribute to this process.
Managerial implications
Our findings imply that both income and resources productivity favor a low-carbon economy on a farm. This may be related to the fact that low-carbon farming requires capital expenditures: investing in thermal insulation of buildings, and buying more energy-efficient machinery, more precise fertilizers, and more efficient crop protection products. Based on research on GHG emissions in Polish agriculture, Kistowski and Wiśniewski (2020) stressed the need to design and use energy-efficient livestock buildings. In turn, Wan et al. (2013) addressed the issue of optimizing technology in order to move to low-carbon agriculture. However, this may be difficult for small farms with low incomes, because of the relatively higher costs and investments (Bonfiglio et al. 2017). As indicated by Ji et al. (2021) adopting the crop efficient production technique can yield the best ecological results. Policy incentives also are important. Gao (2019) demonstrated that households’ investment decisions are determined by their beliefs about gains. Therefore, we may conclude that financial incentives for farmers to implement low-carbon agriculture techniques could be considered.
Research findings show that there is an urgent need for education in the field of low-carbon economy. Access to up-to-date knowledge in this area and increased awareness should be considered as a kind of social investment. As Hudha et al. (2021) pointed out, it is all the more important as there are not enough issues related to low-carbon economy in primary-level textbooks. This problem should be prioritized both in school teaching and in advisory services for farmers. Hence, environmental issues are becoming increasingly important in farm management.
Conclusion and policy implications
The idea of this article was to take an original approach in order to investigate low-carbon agriculture and contribute to existing research, as well as to identify the factors that promote the transition to low-carbon agriculture in the Wielkopolska region. The hypothesis that low-emission farming in the studies farms of Wielkopolska is determined by resource productivity was confirmed. The second hypothesis was proved as well. According to our research low-carbon agriculture is driven by growing energy efficiency and fertilizer efficiency. This indicates the importance of qualitative factors (i.e., production techniques and implementing innovations) in creating a low-carbon economy. Such new eco-friendly strategies involve investments in less energy-intensive machinery, thermal modernization of buildings, elimination of old types of furnaces, and the use of renewable energy sources (biogasification, photovoltaics). The latter are even more important as they are environmentally friendly and can be economically efficient.
Currently, the existing EU solutions (e.g., waste management, cross-compliance, greening, agri-environmental programs for environmental and animal welfare under the common agricultural policy, the 2007 Energy and Climate Package and the 2050 Roadmap) are, in our opinion, still insufficient to meet the global challenges of climate change and energy. Therefore, it is necessary to take complementary actions at the national and local levels to create incentives for developing a low-carbon economy. Experience in Poland with financial support for the installation of microphotovoltaic systems, thermal modernization of buildings, or replacement of central heating stoves is a good indication of this. It is also necessary to increase the awareness of farmers on these issues through education and training.
We believe that the issues of farm use of fertilizers and thermal insulation of buildings should be more broadly included in energy policy, both at the national and the European Union level. Some of these factors, however, are already present in the common agricultural policy for 2021–2027, namely, support for agricultural investments in environmental and climate protection (e.g., equipment and machinery for precise fertilizer application, investments in renewable energy sources in the production cycle, or improving energy efficiency of farm buildings used for agricultural production). In this way, the CAP is linked to energy policy.
Finally, it is also important to be aware of the limitations of this study as the research perspective is reduced. In particular, the availability of data that could be used to quantify the negative externalities associated with farm-level emissions is limited. One solution could be to gather more environmental indicators at the FADN level in the European Union countries. Further research on low-carbon economy, including low-carbon agriculture, should be even more integrated with climate change issues. In this context, such research should take into account the more detailed impact of individual countries and regions to incorporate this data into the assessment of emissions globally. It would be interesting to conduct similar research in regions with different levels of agricultural development. These studies should also include more qualitative variables in order to recognize the behavioral component of farmers’ decision-making in a low-carbon economy. In terms of research methods, applying structural equation modeling seems to be interesting and justified for such analysis.
Data availability
Data cannot be made public in the form of individual data because of obligations of secrecy. However, there is no restriction on their publication in aggregated and processed form.
Notes
Economic size was defined as the standard value of agricultural output, known as the standard output (SO—the average monetary value of the agricultural output at the farm gate value of each agricultural product, crop, or livestock in a given region). It is expressed in thousands of EUR. The analysis used six classes of economic size: very small farms ES1 (EUR 2,000–8,000 SO), small farms ES2 (EUR 8,000–25,000 SO), medium ES3 (EUR 25,000–50,000 SO), medium–large ES4 (EUR 50,000–100,000 SO), large ES5 (EUR 100,000–500,000 SO), and very large ES6 (over EUR 500,000 SO).
The system distinguishes between eight types of production of agricultural holdings within the framework of the EU FADN agricultural accounting according to the predominant production type. These are as follows: field crops (TF1), horticulture (TF2), vineyards (TF3), other permanent crops (TF4), milk production (TF5), other grazing livestock (TF6), granivores (TF7), mixed (TF8).
References
Ahmad M, Zhao ZY, Irfan M, Mukeshimana MC (2019) Empirics on influencing mechanisms among energy, finance, trade, environment, and economic growth: a heterogeneous dynamic panel data analysis of China. Environ Sci Pollut Res 26:14148–14170. https://doi.org/10.1007/s11356-019-04673-6
Akbar U, Li Q, Akmal MA, Shakib M, Iqbal W (2021) Nexus between agro-ecological efficiency and carbon emission transfer: evidence from China. Environ Sci Pollut Res 28:18995–19007. https://doi.org/10.1007/s11356-020-09614-2
Alvarado R, Tillaguango B, Dagar V, Ahmad M, Işık C, Méndez P, Toledo E (2021) Ecological footprint, economic complexity and natural resources rents in Latin America: empirical evidence using quantile regressions. J Clean Prod. 318. https://doi.org/10.1016/j.jclepro.2021.128585
Anghel I, Siminica M, Cristea M, Noja GG, Sichigea M (2019) Bioeconomy credentials and intellectual capital: a comparative modeling approach for the E.U.-13 and E.U.-15. Econ. Res. – Ekon. Istraz 32:2699–2722. https://doi.org/10.1080/1331677X.2019.1653212
Bonfiglio A, Arzeni A, Bodini A (2017) Assessing eco-efficiency of arable farms in rural areas. Agr Syst 151:114–125. https://doi.org/10.1016/j.agsy.2016.11.008
Bouwman AF, Boumans LJ, Batjes NH (2002) Emissions of N2O and NO from fertilized fields: summary of available measurement data. Global Biogeochem Cycles. 16(4). https://doi.org/10.1029/2001GB001811
Brown TA (2015) Confirmatory factor analysis for applied research, the, 2nd edn. The Guilford Press, New York
Brown TA, Moore MT (2012) Confirmatory factor analysis. In: Hoyle RH (ed) Handbook of structural equation modeling. The Guilford Press, New York-London, pp 361–379
Chen X, Shuai C, Wu Y, Zhang Y (2020) Analysis on the carbon emission peaks of China’s industrial, building, transport, and agricultural sectors. Sci. Total Environ. 709. https://doi.org/10.1016/j.scitotenv.2019.135768
Clapp C, Briner G, Karousakis K (2010) Low-emission development strategies (LEDS): Technical, Institutional and Policy Lessons. OECD, IEA. COM/ENV/EPOC/IEA/SLT(2010)2
Dogan E, Seker F (2016a) Determinants of CO2 emissions in the European Union: the role of renewable and non-renewable energy. Renew Energy 94:429–439. https://doi.org/10.1016/j.renene.2016.03.078
Dogan E, Seker F (2016b) The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries. Renew Sustain Energy Rev 60:1074–1085. https://doi.org/10.1016/j.rser.2016.02.006
Eyuboglu K, Uzar U (2020) Examining the roles of renewable energy consumption and agriculture on CO2 emission in lucky-seven countries. Environ Sci Pollut Res 27:45031–45040. https://doi.org/10.1007/s11356-020-10374-2
Garson GD (2015) Structural equation modeling, statistical associates blue book series. North Carolina State University, Statistical Associates Publishing, School of Public and International Affairs
Gao M (2019) No pain, no gain? Household beliefs and stock market participation. Econ Lett 181:81–84. https://doi.org/10.1016/j.econlet.2019.05.001
Goldin I, Winters LA (eds) (2010) The economics of sustainable development. University Press, Cambridge. https://doi.org/10.1017/CBO9780511751905
Groeneveld RA, Kruseman G, Van Ierland EC (2003) Global warming and the economics of land-use and of land-cover change. In: Dolman AJ, Verhagen A, Rovers CA (eds) Global Environmental Change and Land Use. Dordrecht: Springer. https://link.springer.com/chapter/https://doi.org/10.1007/978-94-017-0335-2_3
Grzelak A, Staniszewski J, Borychowski M (2020) Income or assets—what determines the approach to the environment among farmers in a region in Poland? Sustain. 12. https://doi.org/10.3390/su12124917
Hadrich JC, Olson F (2011) Joint measurement of farm size and farm performance: a confirmatory factor analysis. Agr Finance Rev 71:295–309. https://doi.org/10.1108/00021461111177585
Haider A, Bashir A, Husnain M (2020) Impact of agricultural land use and economic growth on nitrous oxide emissions: evidence from developed and developing countries. Sci Total Environ. 741. https://doi.org/10.1016/j.scitotenv.2020.140421
Hamilton TGA, Kelly S (2017) Low carbon energy scenarios for sub-Saharan Africa: an input-output analysis on the effects of universal energy access and economic growth. Energy Policy 105:303–319. https://doi.org/10.1016/j.enpol.2017.02.012
Hooper D, Coughlan J, Mullen MR (2008) Structural equation modeling: guidelines for determining model fit. Electron J Bus Res Methods 6(1):53–60. https://doi.org/10.21427/D7CF7R
Hörndahl T (2008) Energy use in farm buildings – a study of 16 farms with different enterprises revised and translated second edition SLU Report, Alnarp.
Hoyle RH (ed) (2012) Handbook of structural equation modeling. The Guilford Press, New York-London
Hudha MN, Hamidah I, Permanasari A, Abdullah AG (2021) How low-carbon issues are addressed in primary school textbooks. J Pendidik IPA Indones 10(2):260–269. https://doi.org/10.15294/jpii.v10i2.26628
Iacobucci D (2010) Structural equations modeling: fit Indices, sample size, and advanced topics. J Consum Psychol 20:90–98. https://doi.org/10.1016/j.jcps.2009.09.003
Iram R, Zhang J, Erdogan S, Abbas Q, Mohsin M (2020) Economics of energy and environmental efficiency: evidence from OECD countries. Environ Sci Pollut Res 27:3858–3870. https://doi.org/10.1007/s11356-019-07020-x
Islam MM, Khan MK, Tareque M, Jehan N, Dagar V (2021) Impact of globalization, foreign direct investment, and energy consumption on CO2 emissions in Bangladesh: does institutional quality matter? Environ Sci Pollut Res 28:48851–48871. https://doi.org/10.1007/s11356-021-13441-4
Jensen H, Dominguez I, Fellmann T (2019) Economic impacts of a low carbon economy on global architecture: the bumpy road to Paris. Sustain 11:2349. https://doi.org/10.3390/su11082349
Ji TG, Raza A, Akbar U, Ahmed M, Popp J, Oláh J (2021) Marginal trade-offs for improved agro-ecological efficiency using data envelopment analysis. Agronomy 11(2):365. https://doi.org/10.3390/agronomy11020365
Johnson JM, Franzluebbers A, Weyers S, Reicosky D (2007) Agricultural opportunities to mitigate greenhouse gas emissions. Environ Pollut 150(1):107–124. https://doi.org/10.1016/j.envpol.2007.06.030
Kampas A, Rozakis S, Faber A, Mamica Ł (2021) Assessing the green growth trajectory through resource and impact decoupling indices: the case of Poland. Pol. J. Environ. Stud. 30 (3):2573–2587. https://doi.org/10.15244/pjoes/128585
Khan MK, Abbas F, Godil DI, Sharif A, Ahmed Z, Anser MK (2021a) Moving towards sustainability: how do natural resources, financial development, and economic growth interact with the ecological footprint in Malaysia? A dynamic ARDL approach. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14686-9
Khan MK, Babar SF, Oryani B, Dagar V, Rehman A, Zakari A, Khan MO (2021b) Role of financial development, environmental-related technologies, research and development, energy intensity, natural resource depletion, and temperature in sustainable environment in Canada. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-15421-0
Kistowski M, Wiśniewski P (2020) Regionalisation of needs to reduce GHG emission from agriculture in Poland. Geogr Pol 93(3):361–376. https://doi.org/10.7163/GPol.0178
Kline RB (2011) Principles and practice of structural equation modeling. The Guilford Press, New York, The third edition
Koondhar MA, Shahbaz M, Memon KA, Ozturk I, Kong R (2021a) A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on co-citation analysis theory and pathfinder network scaling algorithms. Environ Sci Pollut Res 28:16690–16706. https://doi.org/10.1007/s11356-020-12199-5
Koondhar MA, Udemba EN, Cheng Y, Khan ZA, Koondhar MA, Batool M, Kong R (2021b) Asymmetric causality among carbon emission from agriculture, energy consumption, fertilizer, and cereal food production – a nonlinear analysis for Pakistan. Sustain Energy Technol Assess 45:101099. https://doi.org/10.1016/j.seta.2021.101099
Kopiński J, Madej A (2006) Attempt determination of nitrogen coefficient into manure in dependence of the livestock load, Nawozy i nawożenie 8/4 (in Polish).
Kyttä V, Helenius J, Tuomisto HL (2021) Carbon footprint and energy use of recycled fertilizers in arable farming. J Clean Prod 287:125063. https://doi.org/10.1016/j.jclepro.2020.125063
Moran D, Knook J (2017) Economics of low carbon agriculture. Oxford University Press
Muhammad B, Khan MK (2021) Foreign direct investment inflow, economic growth, energy consumption, globalization, and carbon dioxide emission around the world. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14857-8
Muhammad B, Khan MK, Khan MI, Khan S (2021) Impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth on environmental degradation: evidence from BRICS, developing, developed and global countries. Environ Sci Pollut Res 28:21789–21798. https://doi.org/10.1007/s11356-020-12084-1
Narayan PK, Saboori B, Soleymani A (2016) Economic growth and carbon emissions. Econ Model 53:388–397. https://doi.org/10.1016/j.econmod.2015.10.027
Norse D (2012) Low carbon agriculture: objectives and policy pathways. Environ Dev 1(1):25–39. https://doi.org/10.1016/j.envdev.2011.12.004
OECD (2008) Handbook on constructing composite indicators: methodology and user guide. OECD Publications, Paris
Oryani B, Koo Y, Rezania S, Shafiee A, Khan MK, Mahdavian SM (2021) The role of electricity mix and transportation sector in designing a green-growth strategy in Iran. Energy 233:121178. https://doi.org/10.1016/j.energy.2021.121178
Paramati SR, Apergis N, Ummalla M (2018) Dynamics of renewable energy consumption and economic activities across the agriculture, industry, and service sectors: evidence in the perspective of sustainable development. Environ Sci Pollut Res 25:1375–1387. https://doi.org/10.1007/s11356-017-0552-7
Parry S (2021) Fit statistics commonly reported for CFA and SEM, Cornell University, Cornell Statistical Consulting Unit. https://www.cscu.cornell.edu/news/Handouts/SEM_fit.pdf (1 Mar 2021).
Picazo-Tadeo A, Gomez-Limon J, Reig-Martínez E (2011) Assessing farming eco-efficiency: a data envelopment analysis approach. J Environ Manage 92:1154–1164. https://doi.org/10.1016/j.jenvman.2010.11.025
Piwowar A (2019) Low carbon agriculture in Poland – theoretical and practical challenges. Pol. J. Environ. Stud. 28(4):2785–2792. https://doi.org/10.15244/pjoes/92211
Piwowar A (2020) Agricultural biogas—an important element in the circular and low-carbon development in Poland. Energies 13(7). https://doi.org/10.3390/en13071733
Piwowar A (2021) The use of pesticides in Polish agriculture after integrated pest management (IPM) implementation. Environ Sci Pollut Res 28:26628–26642. https://doi.org/10.1007/s11356-020-12283-w
Qiao H, Zheng F, Jiang H, Dong K (2019) The greenhouse effect of the agriculture-economic growth-renewable energy nexus: evidence from G20 countries. Sci Total Environ 671:722–731. https://doi.org/10.1016/j.scitotenv.2019.03.336
Rafiq S, Salim R, Apergis N (2016) Agriculture, trade openness and emissions: an empirical analysis and policy options. Aust J Agric Resour Econ 60:348–365. https://doi.org/10.1111/1467-8489.12131
Reay D (2020) Land use and agriculture: pitfalls and precautions on the road to net zero. Front. Clim. 04 August. https://doi.org/10.3389/fclim.2020.00004
Rehman A, Ma H, Ozturk I, Murshed M, Dagar V (2021) The dynamic impacts of CO2 emissions from different sources on Pakistan’s economic progress: a roadmap to sustainable development. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01418-9
Rogall H (2004) Ökonomie der Nachhaltigkeit. Handlungsfelder für Politik und Wirtschaft. VS– Verlag, Wiesbaden.
Shahbaz M, Jam F, Bibi S, Loganathan N (2016) Multivariate Granger causality between CO2 emissions, energy intensity and economic growth in Portugal: evidence from cointegration and causality analysis. Technol Econ Dev Econ 22(1):47–74. https://doi.org/10.3846/20294913.2014.989932
Shahbaz M, Nasir MA, Hille E, Mahalik MK (2020) UK’s net-zero carbon emissions target: Investigating the potential role of economic growth, financial development, and R&D expenditures based on historical data (1870–2017). Technol Forecast Soc Change 161:120255. https://doi.org/10.1016/j.techfore.2020.120255
Shahbaz M, Destek MA, Dong K, Jiao Z (2021) Time-varying impact of financial development on carbon emissions in G-7 countries: Evidence from the long history. Technol. Forecast. Soc. Change 171. https://doi.org/10.1016/j.techfore.2021.120966
Sharma R, Shahbaz M, Kautish P, Vo XV (2021a) Does energy consumption reinforce environmental pollution? Evidence from emerging Asian economies. J Environ Manage 297:113272. https://doi.org/10.1016/j.jenvman.2021.113272
Sharma R, Shahbaz M, Sinha A, Vo XV (2021b) Examining the temporal impact of stock market development on carbon intensity: evidence from South Asian countries. J Environ Manage 297:113248. https://doi.org/10.1016/j.jenvman.2021.113248
Sinha A, Shahbaz M (2018) Estimation of environmental Kuznets Curve for CO2 emission: role of renewable energy generation in India. Renew Energ 119:703–711. https://doi.org/10.1016/j.renene.2017.12.058
Solinas S, Tiloca MT, Deligios PA, Cossu M, Ledda L (2021) Carbon footprints and social carbon cost assessments in a perennial energy crop system: a comparison of fertilizer management practices in a Mediterranean area. Agric Syst 186:102989. https://doi.org/10.1016/j.agsy.2020.102989
StataCorp (2017) Stata: Release 15. Statistical Software. College Station, TX: StataCorp LLC.
Syp A, Osuch D (2018) Assessing greenhouse gas emissions from conventional farms based on the farm accountancy data network. Pol. J. Environ. Stud. 27(3):1261–1268. https://doi.org/10.15244/pjoes/76675
Tillaguango B, Alvarado R, Dagar V, Murshed M, Pinzón Y, Méndez P (2021) Convergence of the ecological footprint in Latin America: the role of the productive structure. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14745-1
Van Veelen B (2021) Cash cows? Assembling low-carbon agriculture through green finance. Geoforum 118:130–139. https://doi.org/10.1016/j.geoforum.2020.12.008
Van Grinsven H, Van Eerdt M, Westhoek H, Kruitwagen S (2019) Benchmarking eco-efficiency and footprints of Dutch agriculture in European context and implications for policies for climate and environment. Front Sustain Food Syst. https://doi.org/10.3389/fsufs.2019.00013
Wan N, Ji X, Qiao H, Huang K (2013) A methodological approach to assess the combined reduction of chemical pesticides and chemical fertilizers for low-carbon agriculture. Ecol Indic 24:344–352. https://doi.org/10.1016/j.ecolind.2012.07.006
Wang L, Vo XV, Shahbaz M, Ak A (2020) Globalization and carbon emissions: is there any role of agriculture value-added, financial development, and natural resource rent in the aftermath of COP21? J Environ Manage 268:110712. https://doi.org/10.1016/j.jenvman.2020.110712
West T, Marland G (2002) A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: comparing tillage practices in the United States. Agric Ecosyst Environ 91:217–232. https://doi.org/10.1016/S0167-8809(01)00233-X
Wiśniewski P, Kistowski M (2018) Assessment of greenhouse gas emissions from agricultural sources in order to plan for needs of low carbon economy at local level in Poland. Geografisk Tidsskrift-Danish Journal of Geography 118 (2). https://doi.org/10.1080/00167223.2018.1436447
Xiao M, Shahbaz M, Liang Y, Yang J, Wang S, Chadwicka DR, Jones D, Chen J, Ge T (2021) Effect of microplastics on organic matter decomposition in paddy soil amended with crop residues and labile C: a three-source-partitioning study. J Hazard Mater. 416. https://doi.org/10.1016/j.jhazmat.2021.126221
Xiong C, Wang G, Su W, Gao Q (2021) Selecting low-carbon technologies and measures for high agricultural carbon productivity in Taihu Lake Basin, China. Environ Sci Pollut Res, https://doi.org/10.1007/s11356-021-14272-z
Xiong C, Yang D, Huo J, Zhao Y (2016) The Relationship between agricultural carbon emissions and agricultural economic growth and policy recommendations of a low-carbon agriculture economy. Pol J Environ Stud 25(5):2187–2195. https://doi.org/10.15244/pjoes/63038
Xu B, Linc B (2017) Factors affecting CO2 emissions in China’s agriculture sector: evidence from geographically weighted regression model. Energy Policy 104:404–414. https://doi.org/10.1016/j.enpol.2017.02.011
Zakari A, Toplak J, Ibtissem M, Dagar V, Khan MK (2021) Impact of Nigeria’s industrial sector on level of inefficiency for energy consumption: Fisher ideal index decomposition analysis. Heliyon 7(5). https://doi.org/10.1016/j.heliyon.2021.e06952
Zhao J, Shahbaz M, Dong X, Dong K (2021) How does financial risk affect global CO2 emissions? The role of technological innovation. Technol. Forecast. Soc. Change 168. https://doi.org/10.1016/j.techfore.2021.120751
Zhao R, Liu Y, Tian M, Yao L, Ding M, Cao L, Zhang Z, Chuai X, Xiao L, Yao L (2018) Impacts of water and land resources exploitation on agricultural carbon emissions: the water-land-energy-carbon nexus. Land Use Policy 72:480–492. https://doi.org/10.1016/j.landusepol.2017.12.029
Zia S, Rahman MU, Noor MH, Khan MK, Bibi M, Godil DI, Quddoos MU, Anser MK (2021) Striving towards environmental sustainability: how natural resources, human capital, financial development, and economic growth interact with ecological footprint in China. Environ Sci Pollut Res 28:52499–52513. https://doi.org/10.1007/s11356-021-14342-2
Acknowledgements
We thank Jakub Staniszewski, Ph.D. from Poznań University of Economics and Business for help with the questionnaire.
Funding
This paper is financed by the National Science Centre in Poland (grant no. 2018/29/B/HS4/01844).
Author information
Authors and Affiliations
Contributions
• Michał Borychowski: resources, methodology, conceptualization and visualization, formal analysis and investigation, writing—original draft preparation, writing—review and editing, supervision.
• Aleksander Grzelak: resources, data curation, conceptualization, formal analysis, writing-original draft preparation, writing—review and editing, supervision, project administration, funding acquisition.
• Łukasz Popławski: resources, writing—original draft preparation.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Eyup Dogan
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Highlights
• Productivity factors are important for low-carbon farming in the Wielkopolska region.
• More efficient use of energy and fertilizers is conducive to low-carbon agriculture.
• Energy policy may promote low-carbon agriculture.
• SEM may be an efficient technique for researching low-carbon agriculture.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Borychowski, M., Grzelak, A. & Popławski, Ł. What drives low-carbon agriculture? The experience of farms from the Wielkopolska region in Poland. Environ Sci Pollut Res 29, 18641–18652 (2022). https://doi.org/10.1007/s11356-021-17022-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-17022-3
Keywords
- Low-carbon agriculture
- The Wielkopolska region
- Energy
- Fertilizers
- Productivity
- Agricultural efficiency
- Structural equation modeling (SEM)