Energy crop scenarios

From Opasnet
Jump to: navigation, search
The text on this page is taken from an equivalent page of the IEHIAS-project.

Scope: To obtain estimates/projections of energy crop type and distribution in the regions of interest (Thessaly and Central Macedonia) according to the business-as-usual and mitigation scenarios.

A. Mitigation scenario

This scenario, for 2020, 2030 and 2050, is based on a recent EEA study (see references below).

Extrapolation to the year 2050 was done by fitting the trend for the entire EU-27 to data for the study regions. Most of the land allocated to energy crop cultivation is assumed to be released arable land and to a lesser extent set-aside land.

Available arable land for dedicated crop cultivation by Member State (1000 ha)
AT BE CZ DK EE FI FR DE EL HU IE IT LV LT NL PL PT SK SI ES SE UK EU-14 New member states (EU-8) EU-22
2010 204 0 303 74 88 486 536 100 356 413 0 1074 83 523 0 3823 250 81 3 2706 135 824 5320 7646 12965
2020 266 0 314 0 154 299 1000 2000 298 512 0 1786 144 882 0 4321 169 140 16 2582 168 1118 6181 9686 16170
2030 290 0 301 0 159 174 2000 3000 266 547 0 2165 183 1055 0 4525 125 213 36 2459 178 1584 7019 12249 19267

The results of the EEA study are summarized in the attached file from the EEA report. The following points should be noted:

  1. The substantial increase of bio-energy production is apparently due to crop cultivation. The data for 2020 and 2030, and extrapolated to year 2050, constitute the part of the mitigation scenario referring to energy crops.
  2. It is foreseen that forestry makes a small contribution to energy production in the context of the mitigation scenario; this prediction is in accordance with other stakeholders' opinions (e.g. Europa Bio 2007).
  3. For the purpose of the case study, the EEA projections of land allocation to energy crops (Table 3.2, reproduced above) are employed. In estimating equivalent energy production under the scenarios, account has to be taken of:
    • agricultural residues related to the crops, in addition to the main product (seeds or grain); and
    • an appropriate yield for both main crop (seed and grain) and the remainder of biomass (from the plant, where applicable) - i.e. the total biomass produced per unit area cultivated.
  4. The estimates of equivalent energy (MToe or eJ per year) derived for these scenarios differ from those indicated in Table 6.1 (attached below) in that :1
    • more detailed yield values are employed; and
    • both main crop/product (grain, seed) and plant biomass is considered in the yield estimates.

B. Business-as-usual (BAU) scenario

Figure 1. Total biomass production in EU-27 years, 1996 to 2007 (Eurostat 2008)

The rate of increase of biomass production in EU-27 before year 2003 was approximately 1.25 Mtoe/yr, as shown in Figure 1, below. In 2003, a reform to the CAP established subsidies for energy crops cultivated in EU-27 up to a total area of 2.0 Mha. The above rate of increase is attributed largely to the general trend of increasing yield of the various crops and possibly to increased utilisation of agricultural residues. Only part of that increase is considered to be due to explicit land reallocation to energy crops. For the purpose of selecting a BAU scenario, an average yield of of 10t TS/ha is assumed for energy crop. This is based on the observation that approximately 50% of the increase in biomass production seen in recent years (~1.25 Mtoe/year) is due to land allocation to such crops.

C. Total land requirements for the energy crops

To estimate land requirements for energy crops in the two Greek case study areas (Thessaly and Central Macedonia), the national data are scaled down proportional to the area of total arable land. Table below shows the resulting projections for the different scenarios and years.

Area of land (km2) devoted to energy requirements in the Greek case study areas under the BAU and mitigation scenarios. Scenario
Scenario Total (all energy crops) Sunflower Sorghum Cardoon
Baseline - - - -
BAU 2020 581 194 194 194
Mitigation 2020 1104 368 368 368
BAU 2050 329 110 110 110
Mitigation 2050 413 138 138 138

References

  • EEA 2006 How much bioenergy can Europe produce without harming the environment? EEA Report No 7/2006. Copenhagen: European Environment Agency.
  • Doran, M. 2008 Contribution of energy crops in displacing fossil fuels in the EU. Paper presented at Integrating Generations, FIG Working week 2008, Stockholm, Sweden, 14-19 June 2008.
  • Nielsen, J.B.H. and Olekskowicz, P. 2008 The future of biogas in Europe: visions and targets until 2020. Paper presented at AEBIOM Workshop: Biogas - promising renewable energy source for Europe, European Parliament Brussels, December 11, 2008.
  • EuropaBio 2007 Biofuels in Europe. European Association for Bioindustries, Position paper, June 2007.
  • Eurostat 2008 Eurostat pocket-books 2008. Luxembourg: Office for Official Publications of the European Communities.

See also

  • Bioenergy potential 2010 2020 2030.jpg
Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages
Toolkit
Data

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty
Appraisal