An Economic Assessment of the Impact on Agriculture of the Proposed Changes in EU Biofuel Policy Mechanisms
Abstract
:1. Introduction
2. Background
2.1. What Are Biofuels?
- Raw materials containing significant amounts of sugar and starch (sugar beet, cereals, potatoes);
- Lignocellulosic biomass (wood and its waste, targeted wood crops, straw);
- Oilseeds and animal fats;
- Organic waste (organic fertilisers and food and municipal waste);
- Algal biomass.
- First generation (edible feedstocks);
- Second generation (non-edible biomass sources);
- Third generation (microalgae biomass);
- Fourth generation (genetic modification of the microalgae).
- Microbial conversion of lignocellulosic biomass (e.g., stalks, corn stover) into bioethanol or biobutanol;
- Transesterification of sustainably sourced FAME (i.e., biodiesel);
- Hydrotreatment of sustainably sourced vegetable oils or animal fats followed by alkane isomerisation and cracking to produce drop-in fuels (HVO/HEFA);
- Thermochemical pathways starting with pyrolysis to produce biocrude or gasification of biomass for syngas.
2.2. Legal Conditions
- Directive 2009/28/EC (RED) of the European Parliament and by the Council meeting of 23 April 2009 on the promotion of the use of energy from renewable sources and the amendment and subsequent appeal of Directives 2001/77/EC and 2003/30/EC (Text with EEA relevance) [89];
- Directive 2009/30/EC of the European Parliament and by the Council meeting of 23 April 2009 that amended Directive 98/70/EC regarding the specification of petrol, diesel, and gas-oil and introduced a mechanism to monitor and reduce greenhouse gas emissions and amended Council Directive 1999/32/EC regarding the specification of fuel used by inland waterway vessels and repealed Directive 93/12/EEC (Text with EEA relevance) [90].
- A 14% share of renewable energy in final energy consumption in the transport sector by 2030 at least;
- Renewable energy used in the transport sector should also comprise renewable liquid and gaseous transport fuels of non-biological origin (e.g., hydrogen) and recycled carbon fuels (e.g., derived from plastic waste, rubber);
- First-generation biofuels should be divided into two categories: low (certification required) and high-risk Indirect Land Use Change -ILUC (cannot be higher than 2019 consumption levels—reduction from 31 December 2023 to 0% by 31 December 2030);
- Input of advanced biofuels and biogas produced from raw materials listed in Annex IX:
- -
- Part A—min 0.2% in 2022, min 1% in 2025 and min 3.5% in 2030;
- -
- Part B—maximum 1.7%.
- New methodology for calculating GHG emissions.
2.3. Development of Biofuel Production in the UE
2.4. Biodiesel Production and Changes in the Area under Basic Crops
3. Materials and Methods
- The sown area of oilseeds;
- The area of sown crops replaced by oilseeds;
- The direct surplus for the above-mentioned crops;
- The value of oilseeds as a forecrop in relation to the crops that were replaced;
- The profits of beekeeping;
- The possibilities of using by-products for feed purposes and thereby reducing protein feed imports.
4. Results and Discussion
4.1. Land Use Change
- The model was estimated with all of the independent variables and then statistically insignificant and non-coincident variables were removed by a posteriori elimination method;
- The model was estimated using all of the independent variables as potential variables using the stepwise regression algorithm (assuming that the variable left in the model must be statistically significant at least at the level of p < 0.05) and following the rule of coincidence;
- The model with independent variables negatively correlated with the dependent variable was estimated, and then statistically insignificant and non-correlated variables were removed by a posteriori elimination method;
- The model was estimated by using only independent variables as the potential variables for winter crops, which were negatively correlated with the dependent variables, using the stepwise regression algorithm (assuming that the variable left in the model must be statistically significant at least at the level of p < 0.05) and following the rule of coincidence;
- The dependence model of the sown area of winter rape and colza (Y) and rye (X) was estimated with the use of an additional artificial zero-one variable (with value 1 for the periods when the variable Y had significantly lower values than those resulting from the linear model; and 0—in the remaining periods). Both variables in the model were statistically significant at the p < 0.0001 level.
4.2. Revenues of Operations
4.3. Pre-Crop Value
4.4. Profits from Beekeeping
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Specification | Pure Biodiesels (Ktoe) | Oilseed Areas (Kha) | |||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total | Including Rape and Turnip Rape | ||||||||||||||||||||
1993 | 2000 | 2005 | 2010 | 2015 | 2018 | 2019 | 1993 | 2000 | 2005 | 2010 | 2015 | 2018 | 2020 | 1993 | 2000 | 2005 | 2010 | 2015 | 2018 | 2020 | |
Germany | 0 | 222 | 1323 | 2736 | 2765 | 2960 | 3176 | 1088 | 1104 | 1371 | 1486 | 1304 | 1272 | 1018 | 1007 | 1078 | 1344 | 1461 | 1286 | 1228 | 957 |
France | 0 | 265 | 542 | 1788 | 2170 | 2435 | 1868 | 1443 | 1993 | 1935 | 2207 | 2239 | 2323 | 2082 | 559 | 1186 | 1232 | 1465 | 1499 | 1617 | 1122 |
Netherlands | 0 | 0 | 0 | 338 | 1440 | 1625 | 1739 | 2 | 1 | 2 | 3 | 2 | 3 | 2 | 2 | 1 | 2 | 3 | 2 | 2 | 1 |
Spain | 0 | 0 | 147 | 764 | 1011 | 1561 | 1804 | 2155 | 871 | 523 | 704 | 811 | 771 | 725 | 13 | 29 | 5 | 21 | 71 | 79 | 73 |
Poland | 0 | 0 | 59 | 348 | 695 | 784 | 849 | 349 | 438 | 555 | 949 | 955 | 856 | 876 | 349 | 437 | 550 | 946 | 947 | 845 | 864 |
Italy | 0 | 0 | 177 | 706 | 510 | 664 | 771 | 311 | 506 | 286 | 280 | 436 | 445 | 397 | 6 | 36 | 4 | 20 | 12 | 14 | 17 |
Portugal | 0 | 0 | 0 | 280 | 317 | 321 | 347 | 95 | 52 | 7 | 14 | 20 | 9 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Finland | 0 | 0 | 0 | 297 | 431 | 281 | 339 | 69 | 53 | 77 | 158 | 55 | 53 | 30 | 69 | 53 | 77 | 158 | 55 | 53 | 30 |
Sweden | 0 | 0 | 7 | 111 | 124 | 259 | 323 | 169 | 48 | 82 | 110 | 95 | 97 | 99 | 169 | 48 | 82 | 110 | 95 | 97 | 99 |
Belgium | 0 | 0 | 0 | 309 | 223 | 227 | 229 | 4 | 5 | 6 | 11 | 11 | 11 | 11 | 4 | 5 | 6 | 11 | 11 | 11 | 11 |
Austria | 0 | 17 | 37 | 237 | 303 | 206 | 255 | 148 | 90 | 87 | 114 | 113 | 130 | 124 | 59 | 52 | 35 | 54 | 38 | 41 | 32 |
Others | 0 | 60 | 155 | 763 | 889 | 1099 | 1236 | 2107 | 2927 | 3522 | 4639 | 4852 | 5329 | 5179 | 447 | 819 | 964 | 2215 | 1799 | 2331 | 2005 |
UE 27 | 0 | 564 | 2447 | 8677 | 10,878 | 12,422 | 12,936 | 7940 | 8088 | 8453 | 10,675 | 10,893 | 11,299 | 10,551 | 2684 | 3744 | 4301 | 6464 | 5815 | 6318 | 5211 |
Dependent Variable | b0 | b1 | b2 | b3 | Error Variance | R2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Estimate | Stan. Error | t-Stat. | p-Value | Estimate | Stan. Error | t-Stat. | p-Value | Estimate | Stan. Error | t-Stat. | p-Value | Estimate | Stan. Error | t-Stat. | p-Value | |||
UE | ||||||||||||||||||
Y1 | 8162.552 | 112.46 | 72.580 | 0.000 | - | - | - | - | 0.00007 | 0.000 | 9.169 | 0.000 | 0.000 | 0.000 | −6.892 | 0.000 | 168205 | 0.904 |
Y2 | 3151.389 | 122.87 | 25.648 | 0.000 | 0.647 | 0.062 | 10.456 | 0.000 | −0.00004 | 0.000 | −7.153 | 0.000 | - | - | - | - | 140047 | 0.909 |
Y3 | 516.030 | 33.412 | 15.444 | 0.000 | −0.034 | 0.011 | −3.158 | 0.004 | - | - | - | - | 0.000 | 0.000 | 5.861 | 0.000 | 11564 | 0.711 |
Y4 | 4404.115 | 112.99 | 38.977 | 0.000 | −0.474 | 0.123 | −3.862 | 0.001 | 0.000082 | 0.000 | 3.550 | 0.002 | 0.000 | 0.000 | −3.133 | 0.005 | 83821 | 0.359 |
Germany | ||||||||||||||||||
Y1 | 995.231 | 44.249 | 22.492 | 0.000 | 0.375 | 0.065 | 5.776 | 0.000 | - | - | - | - | −4.682 | 0.000 | 16693 | 0.587 | ||
Y2 | 995.231 | 44.249 | 22.492 | 0.000 | 0.375 | 0.065 | 5.776 | 0.000 | - | - | - | - | −4.682 | 0.000 | 16693 | 0.587 | ||
France | ||||||||||||||||||
Y1 | 1764.149 | 64.072 | 27.534 | 0.000 | 0.603 | 0.187 | 3.225 | 0,003 | −0.00019 | 0.000 | −2.403 | 0.024 | - | - | - | - | 22705 | 0.515 |
Y2 | 623.984 | 86.412 | 7.221 | 0.000 | 2.057 | 0.402 | 5.118 | 0.000 | −0.00134 | 0.000 | −3.600 | 0.001 | 0.000 | 0.000 | 2.650 | 0.014 | 20948 | 0.752 |
Poland | ||||||||||||||||||
Y1 | 461.034 | 26.846 | 17.173 | 0.000 | 1.547 | 0.242 | 6.392 | 0.000 | −0.00127 | 0.000 | −4.206 | 0.000 | - | - | - | - | 10013 | 0.803 |
Y2 | 459.682 | 26.706 | 17.213 | 0.000 | 1.549 | 0.241 | 6.435 | 0.000 | −0.00129 | 0.000 | −4.281 | 0.000 | - | - | - | - | 9909 | 0.800 |
Scheme 1 | b1 Parameter Characteristic | Error Variance | Adjusted R-Squared | |||
---|---|---|---|---|---|---|
Estimate | Stan. Error | t-Stat. | p-Value | |||
Total oilseeds | 0.144 | 0.014 | 10.358 | 0.000 | 0.354 | 0.797 |
Rape and turnip rape | 0.131 | 0.014 | 9.148 | 0.000 | 0.377 | 0.754 |
Soybean | 0.015 | 0.004 | 3.798 | 0.001 | 0.027 | 0.332 |
Sunflower | −0.002 | 0.009 | −0.194 | 0.847 | 0.136 | −0.037 |
Total cereals | −0.233 | 0.029 | −8.063 | 0.000 | 1,526 | 0.703 |
Wheat | 0.021 | 0.017 | 1.202 | 0.240 | 0.544 | 0.016 |
Barley | −0.147 | 0.011 | −13.370 | 0.000 | 0.222 | 0.868 |
Rye | −0.093 | 0.008 | −11.944 | 0.000 | 0.111 | 0.840 |
Triticale | 0.060 | 0.006 | 10.427 | 0.000 | 0.061 | 0.800 |
Oats | −0.031 | 0.003 | −11.756 | 0.000 | 0.013 | 0.836 |
Grain maize | −0.025 | 0.012 | −2.033 | 0.052 | 0.284 | 0.104 |
Other cereals | −0.017 | 0.004 | −4.389 | 0.000 | 0.027 | 0.403 |
Renewable energy sources | 6.042 | 0.276 | 21.932 | 0.000 | 111.011 | 0.950 |
Total biofuels | 0.715 | 0.043 | 16.506 | 0.000 | 2.744 | 0.916 |
Pure biodiesels | 0.570 | 0.035 | 16.082 | 0.000 | 1.834 | 0.912 |
Pure biogasoline | 0.119 | 0.008 | 14.053 | 0.000 | 0.105 | 0.887 |
Other liquid biofuels | 0.026 | 0.008 | 3.155 | 0.004 | 0.102 | 0.264 |
Specification | b1 Parameter Characteristic | b2 Parameter Characteristic | Error Variance | Adjusted R-Squared | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Estimate | Stan. Error | Estimate | Stan. Error | t-Stat. | p-Value | |||||
Total oilseeds | 0.507 | 0.054 | 9.467 | 0.000 | −0.016 | 0.002 | −6.296 | 0.000 | 0.108 | 0.926 |
Rape and turnip rape | 0.490 | 0.065 | 7.518 | 0.000 | −0.018 | 0.003 | −5.995 | 0.000 | 0.159 | 0.817 |
Specification | Average | Difference | |
---|---|---|---|
2004–2006 | 2017–2019 | ||
Wheat (winter) | 1848.2 | 1972.8 | 124.6 |
Wheat (spring) | 386.7 | 467.3 | 80.6 |
Barley(winter) | 143.0 | 205.7 | 62.7 |
Triticale (winter) | 1032.7 | 1137.2 | 104.5 |
Triticale (spring) | 114.9 | 181.0 | 66.1 |
Maize for grain | 353.5 | 624.1 | 270.6 |
Maize for feed | 317.4 | 599.2 | 281.8 |
Rape and turnip rape (winter) | 534.6 | 846.6 | 312.0 |
Total | 4731.0 | 6033.9 | 1302.9 |
Rye | 1427.6 | 890.3 | −537.3 |
Barley (spring) | 972.9 | 762.4 | −210.5 |
Cereal mixed for grain (spring) | 1412.6 | 861.8 | −550.8 |
Potatoes | 632.9 | 311.7 | −321.2 |
Sugar beets | 286.9 | 237.1 | −49.8 |
Total | 4732.9 | 3063.3 | −1669.6 |
Specification | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Average 2013–2019 |
---|---|---|---|---|---|---|---|---|
Rape and turnip rape (winter) | 2147 | 2225 | 2077 | 1508 | 1793 | 1918 | 2050 | 1960 |
Wheat (winter) | 2177 | 2247 | 1982 | 1409 | 1945 | 1975 | 1739 | 1925 |
Rye (winter) | 1273 | 1480 | 1252 | 1186 | 1446 | 1166 | 1137 | 1227 |
Triticale (winter) | 1624 | 1724 | 1387 | 1411 | 1568 | 1452 | 1427 | 1513 |
Specification | Yield Mg/ha | Prices | Pre-Crop Value | ||
---|---|---|---|---|---|
PLN/Mg | EUR/Mg | PLN | EUR | ||
Wheat (winter) | 6.3 | 654.2 | 149.7 | 453.4 | 103.7 |
Rye (winter) | 3.5 | 530.3 | 121.4 | 204.2 | 46.7 |
Triticale (winter) | 4.8 | 534.4 | 122.3 | 282.2 | 64.6 |
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Gradziuk, P.; Jończyk, K.; Gradziuk, B.; Wojciechowska, A.; Trocewicz, A.; Wysokiński, M. An Economic Assessment of the Impact on Agriculture of the Proposed Changes in EU Biofuel Policy Mechanisms. Energies 2021, 14, 6982. https://doi.org/10.3390/en14216982
Gradziuk P, Jończyk K, Gradziuk B, Wojciechowska A, Trocewicz A, Wysokiński M. An Economic Assessment of the Impact on Agriculture of the Proposed Changes in EU Biofuel Policy Mechanisms. Energies. 2021; 14(21):6982. https://doi.org/10.3390/en14216982
Chicago/Turabian StyleGradziuk, Piotr, Krzysztof Jończyk, Barbara Gradziuk, Adrianna Wojciechowska, Anna Trocewicz, and Marcin Wysokiński. 2021. "An Economic Assessment of the Impact on Agriculture of the Proposed Changes in EU Biofuel Policy Mechanisms" Energies 14, no. 21: 6982. https://doi.org/10.3390/en14216982