Comparative analysis of energy costs on farms in the European Union: A nonparametric approach

Energy 195 (2020) 116953

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Comparative analysis of energy costs on farms in the European Union: A nonparametric approach ~o Pereira Domingues Martinho a, b, * Vítor Joa a b

Agricultural School (ESAV) and CI&DETS, Polytechnic Institute of Viseu (IPV), Portugal s-os-Montes and Alto Douro (UTAD), Portugal Centre for Transdisciplinary Development Studies (CETRAD), University of Tra

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 April 2019 Received in revised form 25 November 2019 Accepted 11 January 2020 Available online 13 January 2020

A rational and efficient use of the various sources of energy available to farms allows, not only for cost cutting, but also, a reduction in the environmental impacts bringing positive externalities and contributions toward sustainability. Of course, this efficiency depends on several factors that may influence the dynamics and performance of the farms in question which may also change spatially between countries and regions. These questions related to the great diversity of realities in the agricultural sector are especially relevant within the European Union (EU) context. In this framework, the main objective of the research presented here is to make an efficiency analysis of energy costs in farms across EU countries and regions, stressing possibilities of savings and taking into account the several realities. For this purpose, data at farm level from the Farm Accountancy Data Network (FADN) was considered for the period 2014 e2016 which were then explored through the nonparametric approach Data Envelopment Analysis (DEA). For the nonparametric analysis the Cobb-Douglas model was considered as a base. In this way, the total production (euros) was considered as output. Paid labour (hours), the total fixed assets (euros) and the energy costs (euros) were considered as inputs. In an alternative attempt to take into account the different realities across the EU countries the statistical information in monetary units was corrected by the Price Level Indices and was deflated by the Harmonised Indices of Consumer Prices. Furthermore, to alternatively consider the diversity of contexts of the farms from the EU, the several regions and countries here were clustered through cluster analysis, after factor analysis so as to avoid problems of collinearity. As main insights, it is worthy of stressing the possibilities of significantly reducing the costs of energy consumption in farms from many EU regions. For example, it is possible to reduce the costs of energy use by about 55% in Pohjanmaa (Finland), 53% in Cyprus, 56% in Makedonia-Thraki (Greece), 52% in Thessalia (Greece), 56% in Puglia (Italy) and 53% in Basilicata (Italy). In these contexts the Common Agricultural Policy (CAP) should play a determinant role. © 2020 Elsevier Ltd. All rights reserved.

JEL classification: C14 D61 Keywords: Efficiency Data envelopment analysis Farm accountancy data network Common agricultural policy

1. Introduction The questions related with the sustainability of energy use are a great concern for the several stakeholders around the world, as well as, across the different social and economic sectors. These aspects take special relevance in the agricultural sector, considering its particularities. The efficiency analysis may provide an interesting contribution here and within the efficiency approaches, the DEA holds particular importance, considering the kind of outcomes that it can afford [1].

* Agricultural School (ESAV) and CI&DETS, Polytechnic Institute of Viseu (IPV), Portugal. E-mail address: [email protected]. https://doi.org/10.1016/j.energy.2020.116953 0360-5442/© 2020 Elsevier Ltd. All rights reserved.

The Data Envelopment Analysis is a useful nonparametric approach considered usually to analyse the efficiency and the productivity between Decision Making Units (DMUs). However, this methodology benchmarks the several DMUs and presupposes homogeneity between them. When these assumptions may not be accepted, it is crucial to create a cluster of homogeneous DMUs [2]. The diversity of realities and the lack of homogeneity is particularly significant in the European Union agricultural sector. Many aspects contribute towards this diversity, such as natural factors (soil, climate, and natural landscape), social conditions, political and legal frameworks and economic indicators. It is not easy to consider all these factors and often it is almost impossible to obtain information in order to quantify them [3]. However, as motivation background and theoretical analysis/

2

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

modelling, the literature review (complemented with bibliometric analysis) presented below shows that on the subject of efficient energy consumption in the agricultural sector within the EU, there are few scientific studies, less so at a regional level and almost none with microeconomics data at farm level from all regions and countries. It would appear that there seems to be a knowledge gap here that needs to be further explored. The data at farm level allows capturing for other kinds of dynamics (namely farm scale economies having implications on the numerous farm variables) which justifies being addressed specifically [1]. In this context, it seems pertinent to identify the most efficient EU countries and regions, in terms of farming energy consumption, through efficiency approaches and microeconomics data. This will be important for further planning of the European agricultural sector, specifically for agricultural policy design. In fact, the most efficient European countries and regions may be considered as benchmarks for the design of new policy instruments in the context of the Common Agricultural Policy. On the other hand, it is important to create specific strategies to improve the performance of those less efficient regions and countries, in a framework of better sustainability. For this purpose statistical information was considered at farm level from the European Union Farm Accountancy Data Network [4] in average for the period 2014e2016. An average was considered for this period to, in an alternative way, take into account the eventual annual changes in the farms’ variables derived from natural or agronomic changes and cycles. This information was explored through DEA methodologies with the DEAP [5] software. In this analysis it a model based on the Cobb and Douglas [6] theory of production was utilized, where the output is the total production (euros) and the inputs are the total labour paid (hours), the total fixed assets (euros, proxy for the capital) and the energy costs (euros). In an alternative approach so as to take into account the heterogeneity between the farms of the EU regions the variables in euros were corrected and deflated, respectively, with the Price Level Indices and the Harmonised Indices of Consumer Prices obtained from the Eurostat [7]. To correct the total agricultural output the Price Level Indices from the gross domestic product were considered, for the total fixed assets those from gross fixed capital formation were considered and for the energy costs from electricity, gas and other fuels. For the Harmonised Indices of Consumer Prices the average annual index for all items was considered. After these adjustments the several regions and countries were clustered, after factor analysis (to avoid problems of collinearity), following Stata [8] and Torres-Reyna [9] procedures. The novelty/originality of the work presented here is related with the consideration of an alternative approach/methodology, to analyse the efficient energy consumption by the EU agricultural sector, which combines data at a regional and farm level (with the monetary statistical information corrected, to spatial effects, and deflated to remove the difference of prices across years and countries/regions) explored through DEA, after factor and cluster (FC) analysis. In other words, a DEA-FC methodology was used, based on a Cobb-Douglas model and statistical information at farm level (corrected to consider the specific characteristics of each country). It is important to highlight that the great novelty here is the consideration of alternative approaches in order to deal with the particularities of the farming sector, as well as with the heterogeneity of the farms across the EU regions and countries (taken into account through the spatial correction and deflation of the monetary variables and through the factor plus cluster analysis). These alternatives also have limitations, as others often do, however the results presented here are one more contribution towards understanding the agricultural sector within the EU framework. After this introduction this study will be organized into five subsequent sections. The second section will be for the literature

review with documents obtained from all the Web of Science databases [10] and Scopus [11]. This literature survey was complemented with bibliometric analysis with the VOSviewer software [12]. The third section for data analysis, the fourth for factor and cluster approaches, the fifth for the efficiency analysis and the sixth for the main findings. 2. Bibliometric analysis and literature review Fig. 1 presents a network visualization map of terms cooccurrences (considering 4 as the minimum number of occurrence of a term) obtained through the VOSviewer software with scientific documents (about forty articles) obtained from the Web of Science (all databases) and Scopus scientific platforms for the following topics: Energy consumption; Agricultural sector; European Union; Efficiency. The terms with greater circles are those with more occurrences. In Fig. 1 it is possible to identify three clusters of terms (each with a different colour). The cluster at the top of the figure reveals that fossil fuels still hold their relevance as an energy source in land use for food production with environmental impacts. The cluster at the bottom left of the figure stresses the economic aspects from energy in agricultural systems and its importance for sustainability. The third cluster at the bottom right highlights the needs for change in agricultural policies, namely to mitigate greenhouse gas emissions. After this initial bibliometric analysis, in the next subsection this range of documents will be analysed even further. 2.1. Literature survey One of the biggest concerns with energy use in the agricultural sector is, in fact, related with the greenhouse gas emissions from the fossil fuel sources, namely because of the impacts on the environment and sustainability [13e15]. One of the main sources in Europe of greenhouse gas emissions is the overall energy sector [16,17], requiring adjusted energy management practices [18]. This is particularly relevant if it is noted that the food industry is one of the sectors with a greater energy consumption within Europe [19]. Following the growth of the sector [20], where building design, for example, may provide a relevant contribution here [21], as well as the efficiency analysis [22]. The environmental problems related with the food industry are particularly significant when it is

Fig. 1. Network visualization map for agricultural efficiency in energy consumption across the European Union countries.

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

Paid labour Input

120

25000

100

20000

80

Hours

60 40

15000 10000

5000

20

Austria

Ireland

Slovenia

Greece

Portugal

Romania

Malta

Romania

Poland

Croa a

Greece

Finland

Italy

Cyprus

Lithuania

Portugal

Sweden

European Union

Luxembourg

Croa a

Slovenia

Ireland

Spain

Cyprus

Poland

Italy

Austria

Malta

European Union

Bulgaria

Lithuania

Latvia

France

Denmark

Belgium

Hungary

Luxembourg

United Kingdom

Germany

Finland

Estonia

Bulgaria

Romania

Portugal

Lithuania

Latvia

Greece

Cyprus

Hungary

Malta

Poland

Croa a

France

Estonia

Slovenia

European Union

Spain

Finland

Italy

Austria

Czech Republic

Sweden

Belgium

Germany

Ireland

Slovakia

Denmark

Luxembourg

Ne t h e rla n d s

0

Sweden

50

Netherlands

100

Slovakia

150

9 8 7 6 5 4 3 2 1 0

Czech Republic

1 0 0 00 e u r o s

200

United Kingdom

Spain

Energy

Total fixed assets 250

1 0 0 00 e u r o s

Belgium

Latvia

France

Denmark

Germany

United Kingdom

Estonia

Bulgaria

Hungary

Slovakia

Greece

Romania

Cyprus

Croa a

Slovenia

Portugal

Malta

Poland

Ireland

Austria

Lithuania

Italy

European Union

Spain

Latvia

Finland

Bulgaria

Estonia

Hungary

Luxembourg

France

Sweden

Belgium

Germany

United Kingdom

Denmark

Netherlands

Slovakia

Czech Republic

Netherlands

0

0

Czech Republic

1 0 0 00 e u r o s

Total outp ut

3

Fig. 2. Evolution of the main variables considered in this study (with values at farm level obtained through the FADN methodologies), average over the period 2014e2016, across European Union countries (the monetary values were corrected with Price level indices (EU28 ¼ 100) and deflated by the HICP (2015 ¼ 100)).

concentrated into agri-food clusters [23]. The agricultural systems and farm management, also, influence energy consumption and the consequent impact on the ecological footprint [24]. The same happens with the farmers’ decisions and options for the several agronomic practices on-farm [25] and with the strategies defined by the several stakeholders related with the agricultural sector upstream [26] as well as downstream [27]. Irrigation practices and options appear frequently associated in studies concerning aspects related to energy savings [28]. In these frameworks, the renewable sources of energy may, also, bring about interesting outcomes, namely, in terms of farming profitability and carbon dioxide emission mitigation [29]. The several policy instruments need to be adjusted to these new realities, creating favourable conditions in order to increase the use of renewable energies [30]. Of course, these approaches need to be thought about from a global perspective, because changes in the EU strategies for the agricultural sector and energy use, specifically, have implications on other countries around the world plus on the domestic and international markets [31], with indirect changes in land use [32]. Nonetheless, the design of adjusted policies for European farms is not an easy process, considering the heterogeneity of realities [33], namely in the Common Agricultural Policy context [34]. For the trade-offs between food production and renewable energy provisions in the agricultural sector, the efficiency concept may bring forth crucial contributions [35]. The efficiency conversion improvement is determinant for agricultural sustainability [36], but some studies reveal that there has been, in some cases, a worsening of the farming efficiency in energy consumption [37]. Efficiency and renewable sources seem to be two concepts which have a frequent presence in literature related to energy use [38].

Table 1 Results obtained through factor analysis/correlation (method: principal factors; rotation: orthogonal varimax - Kaiser off). Factor

Variance

Difference

Proportion

Cumulative

Factor1 Factor2

2.625 0.725

1.899 .

0.802 0.222

0.802 1.024

These same studies stress concepts such as eco-efficiency for a sustainable agricultural development [39], improving the dependence of natural resources [40]. In any case, it is important to simulate, for each particular case, the overall benefits from the substitution of fossil fuel energies with biofuels, for example [41]. The economic aspects as well as the relationships between incomes and costs indeed have their relevance in farmers’ decisions about the production factors to be used in each case, namely in a sector where the profit margins are, in some cases, quite low [42]. This often creates additional trade-offs when the farmers need to comply with EU laws and roles [43]. The questions related with costs have, in fact, their influence upon the decisions related with sustainable development, however with adjusted agricultural management it is possible to mitigate environmental impacts with low costs or even profit [44], sometimes by utilizing easy approaches [45]. 3. Data analysis In Fig. 2 when focussing on the evolution of the main variables considered in this study it shows that Slovakia, Czech Republic and the Netherlands are the three EU countries having a greater total output, paid labour and energy costs. On the other hand, the Netherlands, the United Kingdom and Denmark are those having more total fixed assets. Amongst the countries which possess lower values, at farm level, for these variables we can see, for example, Poland, Portugal, Cyprus, Croatia, Slovenia, Greece and Romania for the total output. For paid labour, it is worth highlighting, for instance, the lower

Table 2 Rotated factor loadings. Variable

Factor1

Factor2

Uniqueness

Total output Paid labour input Total fixed assets Energy costs

0.876 0.932 0.360 0.927

0.463 0.130 0.618 0.334

0.018 0.115 0.488 0.029

4

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953 Table 3 Kaiser-Meyer-Olkin analysis for sampling adequacy. Variable

kmo

Total output Paid labour input Total fixed assets Energy costs Overall

0.708 0.850 0.704 0.724 0.745

values in countries such as Finland, Greece, Poland, Croatia, Malta, Romania, Austria, Ireland and Slovenia. For energy costs, with lower values, Cyprus, Poland, Spain, Ireland, Slovenia, Croatia, Greece, Portugal and Romania appear. Finally, for the total fixed assets, highlighting countries such as France, Croatia, Malta, Poland, Hungary, Cyprus, Greece, Latvia, Portugal, Lithuania, Bulgaria and Romania. The data show that, in fact, the Netherlands presents, over the period analysed, a greater performance in terms of farming output, supported with high paid labour, fixed assets (capital) and energy costs. Showing great social (paid labour) and economic contributions, accompanied by major environmental impacts (energy consumption). On the other extreme, with lower socioeconomic dynamics, but, also, with inferior environmental impacts, seem to appear countries such as Romania, or Poland and Greece, for example. The special characteristics of the farms (Netherlands) and the socioeconomic conjuncture seem to have an impact on the performance of the farms in each European Union country.

considering the several European Union regions and countries (165 observations), showing that Factor1 (the only one with a variance greater than 1) accounts for 80.2% of the variance. Table 2 reveals that Factor1 is mainly defined by the total output, paid labour and energy costs. The total fixed assets follow a different pattern. These findings are coherent with the data analysis performed in the previous section. On the other hand, Table 3 confirms the sample adequacy with the overall KMO being greater at around 0.75. From these results the values for Factor1 were predicted. With this new variable and the results presented in Fig. 3, through cluster analysis, the several European Union regions and countries are grouped into three clusters with the summary statistics presented in Table 4 and with the constitution shown in Table 5. From Table 5 it is possible to observe that Belgium, Bulgaria, Germany, Estonia, France, Hungary, Luxembourg, Latvia, the Netherlands, Finland, Sweden and the United Kingdom are grouped together in cluster 1. Some regions of these countries (in some cases all regions) are, also, part of this cluster. It is important to stress that the majority of the regions from France are in this group. In cluster 3 (the smallest) it is possible to find countries (and respective regions) such as the Czech Republic and Slovakia as well as some regions from Germany. The remaining countries and regions are part of cluster 2. This analysis of Table 5 shows that regions from the Czech Republic, Slovakia and some regions of Germany (those closest to the border with Poland and the Czech Republic) show a similar pattern for the variables considered and were grouped in the same cluster 3. In turn, the countries from Southern Europe (Portugal, Spain, Italy and Greece) and a great part of their regions are grouped

4. Factor and cluster analysis Considering the heterogeneity between the EU regions and countries an additional alternative approach was to create a cluster with the variables considered in this study [2]. However, to avoid problems of collinearity the variables were first analysed through factor analysis. For the factor and cluster analysis the Stata [8] and Torres-Reyna [9] procedures were considered. The results in Table 1 were obtained through factor analysis,

Table 4 Summary statistics for the three clusters found through cluster analysis. Clusters

Frequency

Percent

Cumulative

1 2 3

64 92 9

38.790 55.760 5.450

38.790 94.550 100.000

0

10

L2 dissimilarity measure 30 40 20

50

Dendrogram for _clus_1 cluster analysis

G1 n=17

G2 n=28

G3 n=19

G4 n=39

G5 n=20

G6 n=33

G7 n=2

Fig. 3. Dendrogram for cluster analysis.

G8 n=3

G9 n=2

G10 n=2

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

5

Table 5 Organization of the European Union regions and countries in clusters (the monetary values were corrected with Price level indices (EU28 ¼ 100) and deflated by the HICP (2015 ¼ 100)). Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Clusters

Belgium Belgium Bulgaria Bulgaria Bulgaria Bulgaria Bulgaria Germany Germany Germany Germany Germany Germany Germany Germany Germany Spain Estonia Estonia France France France France France France France France France France France France France France France France France France France France Hungary Hungary Hungary Hungary Luxembourg Luxembourg Latvia Latvia Netherlands Netherlands Romania Finland Finland Finland Finland Finland Sweden Sweden Sweden Sweden United Kingdom United Kingdom United Kingdom United Kingdom United Kingdom Belgium Bulgaria Bulgaria Cyprus Cyprus Denmark Denmark Germany

Vlaanderen Total Severozapaden Severen tsentralen Severoiztochen Yugoiztochen Total Schleswig-Holstein Hamburg Niedersachsen Nordrhein-Westfalen Hessen Rheinland-Pfalz Baden-Württemberg Saarland Total Canarias Estonia Total ^Ile-de-France Picardie Haute-Normandie Centre Basse-Normandie Bourgogne Nord-Pas-de-Calais Lorraine Alsace  Franche-Comte Pays de la Loire Bretagne Poitou-Charentes Aquitaine  ne es Midi-Pyre ^ ne-Alpes Rho ^te d’Azur Provence-Alpes-Co Corse Martinique Total  g Eszak-Magyarorsz a € ld Alfo ntúl Duna Total Luxembourg Total Latvia Total The Netherlands Total Bucuresti-Ilfov Etel€ a-Suomi €-Suomi Sisa Pohjanmaa Pohjois-Suomi Total €ttbyggdsl€ Sla an €n Skogs- och mellanbygdsla €n i norra Sverige La Total England - North Region England - East Region England - West Region Scotland Total Wallonie Yugozapaden Yuzhen tsentralen Cyprus Total Denmark Total Bayern

27.468 23.029 14.522 14.297 14.568 10.688 8.577 26.174 22.422 26.368 22.843 14.868 16.649 16.309 12.510 23.546 8.837 15.273 15.273 22.320 24.167 24.374 18.184 18.391 18.608 21.509 20.322 16.446 18.366 23.471 26.358 17.562 17.780 11.079 15.005 18.193 13.407 11.531 18.024 10.854 10.594 18.389 12.758 16.354 16.354 8.253 8.253 44.315 44.315 6.332 8.082 8.701 9.729 10.524 8.813 18.314 14.981 11.160 16.873 23.568 31.284 22.528 18.338 21.433 15.342 3.818 3.899 4.081 4.081 30.540 30.540 13.525

1258.950 951.337 3100.293 3160.653 3065.057 3219.730 2156.450 1126.180 2220.873 1046.257 1179.470 1058.250 1535.823 1201.350 368.117 1744.997 3167.883 2418.407 2418.407 1238.463 791.970 866.450 798.927 511.087 1259.083 1128.880 539.810 1322.697 477.713 1108.747 937.580 787.483 1681.140 577.783 1296.937 2747.750 2175.533 3364.410 1072.140 2393.300 1822.917 2575.903 2079.650 798.457 798.457 1402.250 1402.250 2673.040 2673.040 545.655 501.910 492.710 499.130 605.403 510.517 947.783 517.143 585.647 814.097 2048.727 4060.360 2330.700 1227.113 2055.013 417.490 1064.057 1271.087 668.663 668.663 1701.843 1701.843 395.633

72.526 65.838 16.344 13.469 13.516 10.184 8.700 84.735 21.737 81.321 64.481 48.868 47.781 52.169 47.980 69.820 22.336 26.237 26.237 29.805 32.689 31.063 21.377 27.998 24.563 30.614 30.032 17.744 28.629 25.269 25.583 20.349 18.873 17.626 18.725 15.914 16.980 6.409 22.743 16.980 15.057 23.409 17.519 100.471 100.471 13.769 13.769 193.300 193.300 9.967 33.605 22.974 34.891 29.283 31.402 75.088 47.144 28.992 64.308 149.594 258.626 203.740 166.569 178.053 54.351 4.708 4.094 15.782 15.782 168.527 168.527 61.042

1.655 1.313 1.260 1.263 1.263 0.996 0.766 1.691 1.472 1.476 1.388 1.146 0.924 1.012 1.126 1.535 0.300 1.658 1.658 1.416 1.378 1.358 1.335 1.164 1.066 1.289 1.559 0.972 1.152 1.319 1.638 1.085 1.088 0.909 0.916 0.920 0.780 0.562 1.094 1.428 1.130 1.895 1.366 1.373 1.373 1.061 1.061 2.528 2.528 0.736 1.437 1.258 2.141 1.457 1.553 1.764 1.296 1.325 1.615 1.690 2.090 1.444 1.493 1.470 0.716 0.334 0.367 0.365 0.365 1.196 1.196 0.956

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2

(continued on next page)

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Table 5 (continued ) Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Clusters

Greece Greece Greece Greece Greece Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain France France France France France France Croatia Croatia Croatia Ireland Ireland Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Lithuania Lithuania Malta Malta Austria Austria Poland Poland Poland Poland Poland Portugal Portugal Portugal Portugal Portugal Romania Romania Romania Romania

Makedonia-Thraki Ipiros-Peloponissos-Nissi Ioniou Thessalia Sterea Ellas-Nissi Egaeou-Kriti Total Galicia Asturias Cantabria País Vasco Navarra La Rioja n Arago ~a Catalun Islas Baleares n Castilla y Leo Madrid Castilla-La Mancha Comunidad Valenciana Murcia Extremadura Andalucía Total Champagne-Ardenne Limousin Auvergne Languedoc-Roussillon Guadeloupe union La Re Jadranska Hrvatska Kontinentalna Hrvatska Total Ireland Total Valle d’Aosta Piemonte Lombardia Trentino Alto Adige Veneto Friuli-Venezia Giulia Liguria Emilia-Romagna Toscana Marche Umbria Lazio Abruzzo Molise Campania Calabria Puglia Basilicata Sicilia Sardegna Total Lithuania Total Malta Total Austria Total Pomorze i Mazury Wielkopolska and Slask Mazowsze i Podlasie rze Małopolska i Pogo Total Norte e Centro Ribatejo e Oeste Alentejo e Algarve Açores e Madeira Total Nord-Est Sud-Est Sud-Muntenia Sud-Vest-Oltenia

2.914 2.746 3.002 2.745 2.826 6.394 5.835 5.496 10.010 9.763 7.530 8.992 10.472 7.531 9.237 6.710 6.064 6.055 11.562 7.011 7.651 7.804 22.949 9.022 10.290 12.899 5.713 8.924 2.851 4.081 3.837 6.124 6.124 7.180 9.303 18.382 5.706 7.957 10.943 9.397 6.810 11.193 9.671 5.108 4.597 6.938 3.715 4.361 5.086 4.010 4.551 4.411 3.983 4.307 7.189 5.894 5.894 5.331 5.331 7.119 7.119 7.752 6.972 4.095 3.025 4.932 3.904 5.436 4.573 2.945 4.118 1.763 3.066 2.707 1.636

562.547 409.243 472.080 491.947 488.990 217.690 231.473 120.200 528.760 522.190 823.330 785.747 1260.573 783.073 451.917 744.553 849.150 699.957 2175.603 1192.470 1421.743 986.687 1320.410 296.590 213.927 1517.317 628.460 1035.813 442.943 425.350 428.917 158.450 158.450 766.310 354.730 446.127 326.280 814.610 431.807 397.093 571.050 902.667 1158.667 292.957 466.170 614.147 277.647 389.650 761.400 1209.613 950.483 808.920 729.867 240.727 679.303 672.263 672.263 386.150 386.150 233.000 233.000 566.837 662.703 386.180 306.747 453.000 587.220 458.763 1360.843 383.070 690.180 147.970 458.563 352.223 117.193

13.394 17.561 12.951 15.041 14.985 27.956 18.885 14.462 26.059 34.830 25.154 25.883 40.688 25.607 20.114 17.983 22.549 15.423 31.226 18.010 39.005 27.653 30.635 24.598 23.715 16.533 6.150 8.728 55.090 14.443 22.534 95.105 95.105 30.341 32.618 56.397 45.625 79.236 67.060 50.847 21.450 50.277 47.235 30.067 36.990 27.869 15.058 18.027 18.780 14.649 34.047 24.433 16.095 28.918 35.037 10.218 10.218 21.735 21.735 32.107 32.107 28.858 26.603 18.319 12.231 20.116 12.319 9.336 13.365 12.709 12.167 2.847 5.008 4.830 4.056

0.284 0.151 0.283 0.203 0.223 0.278 0.242 0.290 0.415 0.431 0.298 0.530 0.507 0.489 0.432 0.492 0.395 0.120 0.449 0.316 0.353 0.362 0.920 0.780 0.869 0.588 0.335 0.421 0.164 0.274 0.252 0.300 0.300 0.292 0.491 0.872 0.216 0.207 0.530 0.441 0.316 0.491 0.449 0.411 0.351 0.488 0.255 0.333 0.302 0.184 0.308 0.304 0.263 0.284 0.387 0.520 0.520 0.485 0.485 0.450 0.450 0.576 0.532 0.286 0.240 0.364 0.161 0.297 0.238 0.125 0.189 0.104 0.231 0.237 0.138

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

7

Table 5 (continued ) Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Clusters

Romania Romania Romania Romania Slovenia Slovenia United Kingdom United Kingdom European Union Czech Republic Czech Republic Germany Germany Germany Germany Germany Slovakia Slovakia

Vest Nord-Vest Centru Total Slovenia Total Wales Northern Ireland Total Czech Republic Total Brandenburg Mecklenburg-Vorpommern Sachsen Sachsen-Anhalt Thüringen Slovakia Total

2.961 2.054 2.517 2.280 2.904 2.904 14.233 10.821 7.150 47.946 47.946 78.503 91.391 68.899 82.985 102.201 95.409 95.409

262.440 246.047 433.463 269.510 103.563 103.563 619.277 471.267 773.907 8698.073 8698.073 13619.700 10200.857 11091.593 9501.090 16339.710 22105.113 22105.113

6.236 5.524 5.482 4.592 24.540 24.540 112.991 111.998 26.577 57.982 57.982 128.874 158.172 122.330 162.134 164.931 92.929 92.929

0.278 0.155 0.158 0.174 0.279 0.279 0.953 0.686 0.484 3.640 3.640 5.871 6.302 5.087 5.548 7.317 7.653 7.653

2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3

Farm Accountancy Data Network. All variables in monetary units were corrected with Price Level Indices and deflated with the HICP. One subsection for each one of the three clusters found before will be considered through cluster analysis.

together in cluster 2.

5. Efficiency study through DEA In this section, following, for example, Martinho [1], the efficiency for EU countries and regions will be analysed through Data Envelopment Analysis (input oriented and multi-stage) and with the DEAP software. As a base the Cobb-Douglas model was considered, where the output is the total farming production and the inputs are the paid labour, total fixed assets and energy costs. The statistical information, at farm level, was obtained from the

5.1. Cluster 1 for the French regions and some of its neighbours The results presented in Table 6 shows that the most efficient regions/countries are the following (those with a percentage between the projected and the original values of 100%): Niedersachsen (with exception for the fixed assets) and Saarland

Table 6 Percentage between the original values and those projected by DEA (Cluster 1). Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Belgium Belgium Bulgaria Bulgaria Bulgaria Bulgaria Bulgaria Germany Germany Germany Germany Germany Germany Germany Germany Germany Spain Estonia Estonia France France France France France France France France France France France France France

Vlaanderen Total Severozapaden Severen tsentralen Severoiztochen Yugoiztochen Total Schleswig-Holstein Hamburg Niedersachsen Nordrhein-Westfalen Hessen Rheinland-Pfalz Baden-Württemberg Saarland Total Canarias Estonia Total ^Ile-de-France Picardie Haute-Normandie Centre Basse-Normandie Bourgogne Nord-Pas-de-Calais Lorraine Alsace  Franche-Comte Pays de la Loire Bretagne Poitou-Charentes

100 100 100 100 100 100 105 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

93 98 73 81 83 79 93 88 71 100 91 83 99 94 100 84 100 59 59 88 100 100 94 99 99 93 100 100 100 100 100 100

79 46 73 81 83 79 93 57 94 58 47 50 57 52 100 65 100 59 59 88 100 100 94 99 99 93 100 100 100 100 100 100

93 98 69 64 66 65 84 88 92 100 91 83 99 94 100 84 100 59 59 88 100 100 94 100 99 93 100 100 100 100 100 100 (continued on next page)

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V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

Table 6 (continued ) Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

France France France France France France France Hungary Hungary Hungary Hungary Luxembourg Luxembourg Latvia Latvia Netherlands Netherlands Romania Finland Finland Finland Finland Finland Sweden Sweden Sweden Sweden United Kingdom United Kingdom United Kingdom United Kingdom United Kingdom

Aquitaine ne es Midi-Pyre ^ne-Alpes Rho ^te d’Azur Provence-Alpes-Co Corse Martinique Total  g Eszak-Magyarorsz a €ld Alfo Dun antúl Total Luxembourg Total Latvia Total The Netherlands Total Bucuresti-Ilfov €-Suomi Etela Sis€ a-Suomi Pohjanmaa Pohjois-Suomi Total €n Sl€ attbyggdsla €n Skogs- och mellanbygdsla €n i norra Sverige La Total England - North Region England - East Region England - West Region Scotland Total

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 119 100 101 100 106 100 100 100 100 100 100 100 100 100

95 98 99 100 95 100 97 64 75 70 72 77 77 74 74 100 100 100 90 99 89 80 90 64 85 83 67 76 70 83 73 78

95 98 99 100 95 100 97 64 75 70 72 26 26 74 74 100 100 100 90 99 89 80 90 38 73 83 41 32 50 27 17 22

95 98 99 100 95 100 97 56 72 59 66 77 77 71 71 100 100 100 66 69 45 64 59 64 85 71 67 76 82 83 73 78

(Germany); Canarias (Spain); Picardie, Haute-Normandie, Lorraine, , Pays de la Loire, Bretagne, PoitouAlsace, Franche-Comte ^ te d’Azur and Martinique (France); Charentes, Provence-Alpes-Co the Netherlands; and Bucuresti-Ilfov (Romania). The less efficient for the overall inputs (lower percentages) seem to be the following regions/countries, for example: Severozapaden and Yugoiztochen (Bulgaria); Hungary; Luxembourg; Latvia; Sweden; and the United Kingdom. Specifically regarding energy costs which can be saved, it is worth stressing regions/countries where it is possible to save

 almost 50%, such as, for example, in the following: Estonia; EszakMagyarorsz ag and Dun antúl (Hungary); and Pohjanmaa (Finland). 5.2. Cluster 2 for Southern European and neighbouring regions The more efficient regions in this cluster are those presented in the following (Table 7): Yuzhen tsentralen (Bulgaria); Denmark; Galicia, Asturias, Cantabria and Comunidad Valenciana (Spain); union Champagne-Ardenne, Auvergne, Guadeloupe and La Re (France); Ireland; Lombardia (Italy); Nord-Est and Sud-Vest-Oltenia

Table 7 Percentage between the original values and those projected by DEA (Cluster 2). Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Belgium Bulgaria Bulgaria Cyprus Cyprus Denmark Denmark Germany Greece Greece Greece Greece Greece Spain Spain Spain Spain Spain Spain Spain Spain Spain

Wallonie Yugozapaden Yuzhen tsentralen Cyprus Total Denmark Total Bayern Makedonia-Thraki Ipiros-Peloponissos-Nissi Ioniou Thessalia Sterea Ellas-Nissi Egaeou-Kriti Total Galicia Asturias Cantabria País Vasco Navarra La Rioja n Arago ~a Catalun Islas Baleares

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

99 88 100 47 47 100 100 80 44 67 48 54 52 100 100 100 96 90 76 67 66 60

87 90 100 47 47 100 100 63 44 33 48 39 42 100 100 100 96 84 76 67 66 60

100 90 100 47 47 100 100 79 44 72 48 54 52 100 100 100 96 91 76 67 66 60

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

9

Table 7 (continued ) Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Spain Spain Spain Spain Spain Spain Spain Spain France France France France France France Croatia Croatia Croatia Ireland Ireland Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Lithuania Lithuania Malta Malta Austria Austria Poland Poland Poland Poland Poland Portugal Portugal Portugal Portugal Portugal Romania Romania Romania Romania Romania Romania Romania Romania Slovenia Slovenia United Kingdom United Kingdom European Union

n Castilla y Leo Madrid Castilla-La Mancha Comunidad Valenciana Murcia Extremadura Andalucía Total Champagne-Ardenne Limousin Auvergne Languedoc-Roussillon Guadeloupe union La Re Jadranska Hrvatska Kontinentalna Hrvatska Total Ireland Total Valle d’Aosta Piemonte Lombardia Trentino Alto Adige Veneto Friuli-Venezia Giulia Liguria Emilia-Romagna Toscana Marche Umbria Lazio Abruzzo Molise Campania Calabria Puglia Basilicata Sicilia Sardegna Total Lithuania Total Malta Total Austria Total Pomorze i Mazury Wielkopolska and Slask Mazowsze i Podlasie rze Małopolska i Pogo Total Norte e Centro Ribatejo e Oeste Alentejo e Algarve Açores e Madeira Total Nord-Est Sud-Est Sud-Muntenia Sud-Vest-Oltenia Vest Nord-Vest Centru Total Slovenia Total Wales Northern Ireland Total

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

93 58 55 100 42 68 54 65 100 82 100 73 100 100 65 64 61 100 100 75 85 100 98 95 90 91 78 81 67 59 54 59 68 59 58 36 45 47 52 72 66 73 73 54 54 78 78 60 56 61 61 59 72 82 37 78 65 100 78 75 100 76 70 56 74 100 100 68 71 58

93 58 55 100 84 72 55 65 100 82 100 94 100 100 11 64 46 100 100 66 85 100 38 34 50 57 78 77 67 49 37 59 68 59 58 63 35 47 52 42 61 73 73 54 54 78 78 60 56 61 61 59 72 82 66 50 71 100 78 75 100 76 69 77 73 100 100 39 30 57

93 58 55 100 84 72 55 66 100 82 100 94 100 100 66 64 61 100 100 75 85 100 98 98 90 91 78 81 67 59 54 59 68 59 58 63 44 47 52 72 66 57 57 54 54 78 78 60 56 61 60 59 72 82 66 86 71 100 74 63 100 60 70 77 73 100 100 68 71 57

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V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

(Romania); and Slovenia. In general, the less efficient regions seem to be the following: Cyprus; Greece; a significant part of Spanish regions (for example n, Catalun ~ a, Islas Baleares, Madrid, Castilla-La ManLa Rioja, Arago cha, Extremadura and Andalucía); Croatia; a significant part of Italian regions; Lithuania; Malta; Austria; Poland; Portugal; a substantial part of Romanian regions; Wales and Northern Ireland; and the overall EU. For example, across the overall European Union it is possible to save about 50% of the paid labour, fixed assets and energy costs. With regards to energy costs, it is possible to save more than 50% in regions such as Cyprus, Makedonia-Thraki and Thessalia (Greece) and Puglia and Basilicata (Italy). 5.3. Cluster 3 for Slovakia, the Czech Republic and its neighbouring German regions In this cluster the regions are almost all efficient (with projected efficient values equal to the originals for the three inputs), with the exception of Brandenburg and Sachsen (two German regions) (Table 8). When the overall total output average between the regions and countries is observed within each country (with data in average at farm level, obtained from the FADN), it is possible to confirm, in fact, that these regions from this cluster are the most competitive, followed by cluster 1 and lastly the regions/countries from cluster 2. Considering that the efficiency analysis is an optimized benchmark between variables from different DMUs, the efficiency found depends on the characteristics of the group which is considered to be benchmarked. In this way, the results found are valid for comparison only between regions/countries of each cluster. 6. Discussions and main findings The study presented here aimed to analyse, namely, the energy costs incurred by farms from the European Union regions and countries. Statistical information was considered, at farm level, from the Farm Accountancy Data Network, in average, for the period 2014e2016. This is one the main contributions for the several stakeholders, including the scientific community, because, in fact, it is not so usual the consideration of data at farm level from the FADN [1,37]. To take the heterogeneity between the EU countries into account, this statistical information was corrected with the Price Level Indices and deflated with the HICP (from the Eurostat). These adjustments in the prices are other relevant contributions of this study, namely, to mitigate the heterogeneity between the EU realities and to find homogeneous DMUs. This is a determinant question when it is intended to perform efficiency analyses [2]. After this statistical adjustment, the several regions were clustered through factor and cluster analysis. This is another important approach to obtain homogeneous DMUs. Finally,

efficiency analysis was performed through DEA methodologies, with the DEAP software, and considering the Cobb-Douglas model as a base. Output was considered as being the total farming production and as inputs the paid labour, total fixed assets (proxy for the capital) and energy costs. The literature survey stressed the importance of concepts such as efficiency and renewable sources of energy when the questions related with energy use in the agricultural sector are addressed, as well as, the importance of EU countries such as Spain, France and Italy. In fact, the farming sector may significantly contribute to the decarbonisation, in the energy use, upstream and downstream of the energy sector. Upstream with the production of renewable energy through, for example, the biomass and downstream improving, for example, the efficiency in the energy consumption. The relevant European agricultural countries may play here an interesting contribution. In turn, one of the biggest problems related with farming energy consumption is the greenhouse gas emissions, namely from fossil fuel use. To mitigate these impacts the agricultural policies should play a determinant role here. However the farmers’ perceptions about the possibilities of increased costs are, sometimes, a constraint. The data analysis reveals that countries such as Slovakia, the Czech Republic and the Netherlands are the most competitive with more total farming output, but, also, with more paid labour and energy consumption. In turn, with less total output and with inferior environmental impacts, seem to be the countries such as Romania, or, for example, Poland and Greece. It seems that there is a direct relationship between level the output and the energy consumption, that may be environmentally enhanced with improvements in the efficiency. The factor (to avoid problems of collinearity) and the cluster analysis show that it is possible to group the several countries and regions, as well as the overall EU, into three clusters. These findings are important for this research, but also for the several stakeholders, namely the policymakers. In short, one cluster (cluster 1) was found for the French regions and the neighbouring regions, another (cluster 2) for the Southern European and neighbouring regions (the largest) and another (cluster 3 - the smallest) for Slovakia, the Czech Republic and the neighbouring German regions. The Data Envelopment Analysis reveals that the regions from cluster 3 are the most efficient and that a relevant part of regions from Spain, Italy and Romania (cluster 2) have the worst performance in terms of efficiency. Relative to the energy costs savings, it is possible to save about 55% in Pohjanmaa (Finland), 53% in Cyprus, 56% in Makedonia-Thraki (Greece), 52% in Thessalia (Greece), 56% in Puglia (Italy) and 53% in Basilicata (Italy). Of course, these findings need adjustments with the specific characteristics of each specific local, but are relevant bases and supports for the several stakeholders in the design of plans and strategies to improve the EU agricultural efficiency in the energy use.

Table 8 Percentage between the original values and those projected by DEA (Cluster 3). Country

Region

Total output

Paid labour Input

Total fixed assets

Energy costs

Czech Republic Czech Republic Germany Germany Germany Germany Germany Slovakia Slovakia

Czech Republic Total Brandenburg Mecklenburg-Vorpommern Sachsen Sachsen-Anhalt Thüringen Slovakia Total

100 100 100 100 100 100 100 100 100

100 100 78 100 83 100 100 100 100

100 100 95 100 95 100 100 100 100

100 100 95 100 95 100 100 100 100

V.J. Pereira Domingues Martinho / Energy 195 (2020) 116953

7. Conclusions In this conclusions section, the main contributions of the manuscript will be listed point wise, as following: 1. It is important to promote the sustainable energy use in the agricultural sector (upstream and downstream), namely considering renewable sources of energy (some from inside the sector) and improving the efficiency to mitigate the environmental impacts. In these contexts, the agricultural realities from some EU countries, as Spain, France and Italy, for example, should be taken into account, considering its relevance in the European framework. 2. In these perspectives, the agricultural and environmental policies play a determinant role, as well as, the awareness of farmers to the problem. In fact, it is relevant to make farmers aware that, in some circumstances, sacrifice the environment today to reduce the production costs may increase them significantly at medium and long term. In other contexts, it is improve the relationships of the agriculture with the environment without any impact in the level of output. 3. The design of more adjusted agricultural policies inside the European Union (in the Common Agricultural Policy framework, for example) may address more these trade-offs related with the energy use in the agriculture. 4. In this context of agricultural policies design, it will be important to be aware about the heterogeneity of farming contexts between European regions and countries. The factor and cluster analysis here performed may be an interesting support for the policymakers and may bring relevant insights. 5. In turn, with adjusted plans and useful benchmarking, the results show that it is possible to reduce about 50% of the energy costs in the European farms. Indeed, considering the most efficient regions inside each cluster, as a benchmark, may be an interesting approach in order to support the proposal of adjusted plans and strategies. 6. In any case, it could be important to consider more specific policy instruments in the first (income financial supports) and second (investment financial supports) Pillars of Common Agricultural Policy to deal directly, specifically, with the problems of inefficiency in European farms. It could be adjusted to create an instrument, inside the first pillar, similar to the “Greening”, but specific for the efficient energy use. Inside the second pillar could be designed an instrument similar to the agro-environment measures, specific to promote a sustainable framework for the energy within the farming sector.

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~o This work is financed by national funds through FCT - Fundaça ^ncia e Tecnologia, I.P., under the project UID/Multi/04016/ para a Cie cnico 2019. Furthermore we would like to thank the Instituto Polite de Viseu and CI&DETS for their support. This work is supported by national funds, through the FCT e Portuguese Foundation for Science and Technology under the project UID/SOC/04011/2019.

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