ABSTRACT
The purpose of the paper is to assess the agri-environmental situation in the European Union at the national level. To realize that goal, a multi-criteria analysis of indicators from the official European database was used. The results of the ranking show that Portugal, Estonia, and Ireland are at the top according to agri-environmental performance, while the worst ranked countries are Malta, the Netherlands, Slovenia, and Cyprus. The common agricultural policy of the European Union must be designed to improve the position of certain countries, based on the experience and sustainable agricultural practices of the leading countries in this area, considering the obtained research results. This study can contribute to the creators of agri-environmental policies in the preparation of the future strategy of the agricultural development of the European Union countries.
Keywords:
agriculture, environment, sustainability, multi-criteria analysis, European Union
JEL: O56, C44.
Introduction
As part of the assessment of the sustainable development of society, environmental and agricultural sustainability have a special place. The sustainability of agriculture relies heavily on environmental sustainability (Mukherjee, 2022). In fact, agriculture is an activity, which, unlike others, depends significantly on natural and climatic factors. But, at the same time, it exerts a significant impact on the environment (negative externalities), bearing in mind the reliance on land as a basic natural resource in agricultural production. According to the latest data from the World Bank (2021), agricultural land makes up 40.76% of the total land of the European Union. Lal (2009) argues that ecosystem degradation due to inadequate agricultural practices can be devastating to all of humanity. The following trends are characteristic of agriculture: (1) increasing use of pesticides that pollutes the soil, (ii) accelerated conversion of forest land into agricultural land, which affects soil erosion, (iii) large emissions of ammonia, and (iv) agricultural intensification. All this calls into question the possibility of agricultural development in the future and disrupts the entire ecosystem to a certain extent. That is why Volkov et al. (2020) in the study emphasize that agricultural performance must always be viewed together with environmental indicators. Their study showed that the newer member states of the European Union achieve better agrienvironmental performance compared to the members that joined earlier.
Although it does not have a significant contribution to the gross domestic product, agriculture is important because it ensures food security and poverty reduction; affects the satisfaction of basic human needs, as well as human health (considering food quality and the impact of agricultural activities and practices on natural resources and the environment) (Renner et al., 2020; Streimikiene, & Mikalauskiene, 2023). In addition, it is expected that this sector will gain importance, bearing in mind the forecast of further increase in food prices. Agriculture must provide enough food for the growing population, but without harming the quality of the environment (water, soil, air, etc.), which is the idea of sustainable development of this economic activity (Skaf et al., 2019). The increasing number of studies on this topic testifies to the significant interest of the scientific community in the problem of sustainability, quantifying the sustainable development of agriculture, as well as the impact of the agricultural sector on the environment (Talukder, Blay-Palmer, & Hipel, 2020; Streimikiene, & Mikalauskiene, 2023). Therefore, our determination is to investigate the achieved level of development of agri-environmental performance in the countries of the European Union. This economic integration directs significant resources to agriculture, its protection from foreign competition, as well as for strengthening the position of farmers. Also, the Common Agricultural Policy of the European Union influences the greening of the agricultural sector and the reduction of negative effects on the environment (Rudnicki et а!., 2023), especially due to high energy consumption and high greenhouse gas emissions (Cheba et al., 2022). All reforms of the European Union's Common Agricultural Policy had measures to prevent negative effects of agricultural production on the environment (Salvan et al., 2022). Considering the amount of energy consumed by agriculture, it is necessary to reduce consumption due to at least two reasons: (i) high energy dependence of European countries, and (ii) negative consequences on environmental pollution.
In recent years, global society has faced economic, energy, political and health crises. This also influenced the transformation of agricultural practices to minimize the negative impact on the environment (Cheba et al., 2022). Namiotko et al. (2022) point out that the deterioration of agri-environmental indicators is one of the important aspects of these crises, so in their work they apply TOPSIS, EDAS and SAW methods of multi-criteria analysis for European countries to find and overcome this situation. With this objective in mind, they analyse seven agri-environmental indicators: ammonia emissions from agriculture, areas of intensive agriculture, average organic carbon content in arable land, surface water quality, groundwater quality, the farmland birds index, and the favourable conservation status of agricultural habitats. Markovic et al. (2023) state that intensive irrigation, the use of chemicals and the disruption of biodiversity due to monoculture production are the key issues of concern. That is why the evaluation of environmental sustainability of agriculture is important. Multi-criteria decision making is particularly prevalent in the field of sustainable development (Bartzas, & Komnitsas, 2020; CastilloDiaz et al., 2023), bearing in mind the multidimensionality of the research problem and the complexity of data aggregation. Observing agri-environmental performance using multi-criteria decision-making methods has been the preoccupation of researchers, especially since 2016 (Gürlük, & Uzel, 2016; Gómez-Limón, Arriaza, & GuerreroBaena, 2020; Cicciú, Schramm, & Schramm, 2022). Most of this research apply criteria such as enhancing or protecting biodiversity, improving habitat diversity, minimizing soil erosion, promoting soil fertility, improving soil and water quality, reducing water extraction, optimizing energy balance, maximizing the economic value of agricultural production, increasing the efficiency of fertilizer and pesticide use, and/or reducing total agricultural emissions. Recent research used the following techniques: Principal Component Analysis, Data Development Analysis, and the DEXiPM (Cicciù, Schramm, & Schramm, 2022). In this paper, the authors opted for the MOORA (Multi-Objective Optimization by Ratio Analysis) method, which until now (according to the literature review) has not been used in the ranking of European Union countries according to agri-environmental status, and it is ideal for conflicting criteria that exist in this case. In addition to the highlighted originality of the study, the justification for the research lies in the fact that there is still no unified view of the coverage of agri-environmental indicators that would constitute a single, composite index. The basic research question of this paper is: Which countries of the European Union represent leaders in terms of agri-environmental performance, and which, on the other hand, should significantly improve their prospects for the realization of ecologically acceptable agriculture?
The study consists of several standard parts. After the introduction, the analysis material (indicators, data sources, definitions) is presented in detail, the weighting method is described, as well as the data aggregation tool (section Materials and methods). Then, the research results are presented in tabular and graphical form. In this unified section (Results and Discussion), an effort will be made to review and evaluate the current situation in the countries of the European Union based on the obtained composite indicators of agri-environmental performance. In the last section (Conclusions), final considerations and limitations of the research will be stated, and recommendations to other authors for future research on this topic will be highlighted.
Materials and methods
Multi-criteria decision-making implies several stages. The first step in creating a composite index is the choice of indicators. Carefully selected indicators are essential for the later decision-making by sustainable development policy makers (Krstic, Milenovic, & Radenovic, 2021). The authors selected seven indicators from the database of the European Commission (Eurostat), from the segment related to agrienvironmental indicators. These are the attributes that will be used in the multi-criteria model. The choice was conditioned by the level of observation (national level), the availability of data, as well as their relevance (significance) based on a thorough review of the literature. Thus, the following indicators of agri-environmental performance were reached (European Commission, 2024):
1. Area under organic farming (percentage of the total used agricultural land),
2. Ета! energy consumption by agriculture/forestry (per hectare of utilised agricultural area),
3. Permanent grassland (percentage of the total used agricultural land),
4. Energy productivity (EUR per kilogram of oil equivalent),
5. Ammonia emissions from agriculture (kilograms per hectare),
6. Greenhouse gas emissions from agriculture (in percentage), and
7. Estimated soil loss by water erosion (tonnes per hectare).
Table 1 provides a description of the indicators, the unit of measure for each of them, as well as information on the year to which the data refer (the most recent data according to the Eurostat database).
Three indicators are revenue-type criteria (Area under organic farming, Permanent grassland, and Energy productivity), while the remaining four indicators are costrelated criteria (Final energy consumption by agriculture/forestry, Ammonia emissions from agriculture, Greenhouse gas emissions from agriculture, and Estimated soil loss by water erosion).
Before building the composite index, it is necessary to define the method of determining the weighting coefficients. As a method of weighting, the method of equal weighting coefficients was applied in the paper. Based on the existing shortcomings of subjective methods, the paper uses the method of equal weighting coefficients, which gives equal relative importance to each indicator (when creating a composite index). In this way, the subjectivity of decision-makers and the possible favouring of some indicators were avoided, and on the other hand, the task was significantly simplified, bearing in mind the different preferences of stakeholders (interested parties) at the macro or micro level (Hagerty, & Land, 2007).
In aggregating data, the authors chose one of the newer multi-criteria methods that has not been applied in the assessment of agri-environmental sustainability - the MOORA (Multi-Objective Optimization based on Ratio Analysis) method. The MOORA method is selected due to its ability to normalize and compare criteria that may have different units of measurement, making it particularly suitable for complex decision-making scenarios (Brauers, & Zavadskas, 2006). Additionally, the MOORA method does not require complex mathematical models, allowing decision-makers to easily apply it without extensive computational resources (Stanujkic et al., 2012). In the process of obtaining the value of the composite index, the authors followed the steps described below and manually arrived at the final results. Scientists use this tool when it is necessary to reduce various conflicting indicators to a single measure and to rank alternatives (Filipe, & Caleiro, 2020). By simultaneous optimization of several criteria, an aggregate indicator is obtained, in this case, the index of agri-environmental performance of the countries of the European Union. The MOORA method usually involves the following procedures for calculating the composite index and ranking the alternatives (Brauers, & Zavadskas, 2006; Gadakh, Shinde, & Khemnar., 2013; Madié, Radovanovic, & Petkovic, 2015; Marjanovié, Radenovic, & Markovic, 2019):
...
Step 3. Optimization of the multi-criteria problem, where the normalized values of the revenue criteria (multiplied by the weighting coefficients) are added, while the normalized values of the cost criteria (multiplied by the weighting coefficients) are subtracted:
...
where:
g (number of revenue criteria), n-g (number of cost criteria), and W, - the weight coefficients. The values of the normalized decision matrix are multiplied by the weighted coefficients to form a preference-normalized decision matrix. In this paper equal weighting approach has been applied as one of the objective approaches. This approach is commonly applied in situations where input from the decision-maker is unavailable or when insufficient information exists to determine the relative importance of criteria (Jahan et al., 2012). Equal weighting assumes that all criteria hold equal importance, eliminating the need for subjective judgments or complex weighting schemes, which can sometimes introduce bias. The equal weights can be calculated using the following equation:
...
where n is the number of criteria. Therefore, in the following analysis each indicator will have a weight coefficient of 0.142857. In other words, the sum of the weighted values is equal to the one.
Step 4. Ranking of the alternatives (in descending order of value), with the best being the one with the highest value Vi The value of the composite index can be both positive and negative, depending on whether revenue or cost criteria dominate. Unlike methods that generate results on a specific scale (such as from 0 to 1), the MOORA method produces results that depend on the specific data and context of the decision problem. The range of results is influenced by the number of criteria, the distribution of the data, and the weighting factors. While the results are typically within the [-1, 1] interval, the MOORA method can produce scores that vary beyond this range (Stanujkic et al., 2012). Therefore, the scores within the specific context of the decision-making problem should be analysed.
Results and Discussions
First, Table 2 shows the descriptive statistics of the selected indicators. One of the indispensable indicators in the evaluation of the environmental performance of agriculture is organic production. The percentage of areas under organic production is the highest in Austria, while the lowest is in Malta. The data argue that the highest energy consumption per hectare was recorded in the Netherlands' agriculture, while the lowest consumption was in Bulgaria. The latest available data shows that the percentage of permanent grassland is highest in Ireland, while it is almost nonexistent in Malta. They are particularly important from the standpoint of biodiversity conservation. Ireland achieves the highest energy productivity, while Bulgaria achieves the lowest. When looking at ammonia emissions from agriculture, the worst situation is in Malta, while farmers in Latvia realise the lowest ammonia emissions. At the level of the European Union, according to data for 2021, over 90% of ammonia emissions on average originate from agriculture (European Commission, 2024), and this percentage is the highest in Ireland (99.2%), while the lowest is in Germany (82%), as the most industrialized country in the European Union. One of the leading causes of climate change, i.e. of global warming is ammonia emissions, so this indicator is almost always used in assessing the impact of agriculture on the environment (Shakoor et al., 2021). These emissions are caused by the production of methane and nitrogen oxides, and uncontrolled application of fertilizers, which may affect the sustainability of agricultural production in the future (Markovic et al, 2023). Greenhouse gas emissions from agriculture are the highest in Ireland, while they are the lowest in Malta. Finally, inadequate water management practices in agriculture cause a significant reduction in soil quality and soil erosion. It is one of the most common types of soil degradation in the European Union, so it is a common element when looking at agri-environmental performance (Panagos et al., 2020; European Commission, 2024). Estimated soil loss by water erosion is most present in Slovenia, while the Netherlands shows the most favourable value.
The results of descriptive statistics indicate that the highest average deviations from the mean value are for the indicator Final energy consumption by agriculture/forestry per hectare of utilized agricultural area, so at the same time there are also the biggest differences between the countries of the European Union when it comes to the same indicator.
Table 3 shows the ranking of the countries of the European Union and the values of the composite indices calculated using the MOORA method. Portugal, Estonia, and Ireland stand out at the top of the list, as countries that, according to the results of the research, achieve the best agri-environmental results. On the other hand, Malta has the weakest agri-environmental performance. Along with Malta, the Netherlands, Slovenia, and Cyprus achieve rather poor results in this regard. Fourteen countries of the European Union have positive values of the aggregate indices (left side of Table 3), while in the remaining thirteen countries, cost criteria dominate over revenue ones (right side of Table 3), which results in negative values of the obtained indices.
Figure 1 shows the performance index values by country. It is concluded that there are no big differences between the countries of the European Union when looking at the calculated aggregate indicator of agri-environmental performance. This stems from the fact that there are certain countries that are very well positioned according to some indicators, while according to other indicators they have poor results at the level of the European Union. For example, Austria is the leader in terms of areas under organic production, while it is at the very bottom when it comes to the indicator related to soil erosion. Similarly, although Malta is the worst ranked country, it shows the best values for permanent grassland and greenhouse gas emissions. Furthermore, Ireland is at the top in all indicators except for area under organic production and greenhouse gas emissions.
In order to improve the placement of certain countries and improve agri-environmental sustainability at the level of the European Union, it is necessary to insist on the concept of organic agriculture and the transition to a circular model of agricultural production. Organic agriculture has been proven as the basic form of sustainable agriculture (Markovié et al., 2023). It is one of the ways to ensure high-quality, healthy food, the production of which will have minimal negative effects on the environment due to reduced use of pesticides, herbicides, fertilizers (Rouyendegh, & Savalan, 2022). In this way, soil fertility and biodiversity will be preserved, and farmers can earn solid incomes, bearing in mind the high price of organic products. Another way to build ecological agriculture can be the application of modern circular solutions (Silvestri et al., 2022), primarily in waste management from agriculture (Lombardi, & Todella, 2023). Circular models in agriculture are aimed at reducing the consumption of energy and other resources, as well as reducing waste and negative emissions, which affects many agri-environmental indicators and can lead to the fulfilment of the goals of the 2030 Agenda (Castillo-Diaz et al., 2023). Raising awareness of the strong cause-andeffect relationships between agriculture and the environment and their joint impact on the quality of life of people in every sense must be a priority (Sebek, 2020).
Finally, in Figure 2, the position of the countries of the European Union is clearly illustrated through the maps. Countries with a better state of agri-environmental performance have a darker colour, in contrast to the worse ones, which are assigned a lighter shade.
Conclusions
The study formed (developed) a model (framework) for evaluating agri-environmental performance at the national level. In this way, it is easy to follow the movement of the obtained composite index over time and compare performance indices among different countries. Accordingly, policy makers can take appropriate decisions. The results of the research represent an added value for the future definition of practices, programs, and redesign of the Common Agrarian Policy of this economic integration. Emphasis must be placed on the use of environmentally friendly technologies and the use of renewable resources to preserve natural capital and slow down climate change. Research limitations are determined by the choice of indicators, the choice of multicriteria decision-making methods, as well as the availability of data. Authors of future research could have a modified set of agri-environmental performance indicators (compared to those proposed by the authors), apply other method of analysis, as well as use updated data as soon as they are available in the database used in this study. Thus, this study can be used for comparison with results obtained in some other way. It is necessary for official databases to be supplemented with indicators of biodiversity, as well as consumption and pollution of water due to agricultural production.
Acknowledgements
This research was financially supported by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia (Agreements on the implementation and financing of scientific research in 2024 - Contracts No. 451-0366/2024-03/200371 and 451-03-66/2024-03/200100).
This paper is part of the research done within the international project "Twinning for excellence in Smart and Resilient Urban Development: Advanced Data Analytics Approach" that has received funding from the European Union's Horizon Europe Framework programme under Grant Agreement No. 101059994. Usual disclaimers apply.
Conflict of interests
The authors declare no conflict of interest.
ARTICLE INFO
Original Article
Received: 20 May 2024
Accepted: 25 June 2024
UDC 663/664:512.17(4-672EU)
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Abstract
The purpose of the paper is to assess the agri-environmental situation in the European Union at the national level. To realize that goal, a multi-criteria analysis of indicators from the official European database was used. The results of the ranking show that Portugal, Estonia, and Ireland are at the top according to agri-environmental performance, while the worst ranked countries are Malta, the Netherlands, Slovenia, and Cyprus. The common agricultural policy of the European Union must be designed to improve the position of certain countries, based on the experience and sustainable agricultural practices of the leading countries in this area, considering the obtained research results. This study can contribute to the creators of agri-environmental policies in the preparation of the future strategy of the agricultural development of the European Union countries.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Research Associate, Innovation Centre of the University of Nis, University of Nis, Univerzitetski trg 2, 18000 Nis, Serbia
2 Faculty of Economics, University of Nis, Trg kralja Aleksandra Ujedinitelja 11, 18000 Nis, Serbia
3 Department of Business Administration, University of Macedonia, Egnatia St. 156, 54124 Thessaloniki, Greece