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Table 1 Qualitative evaluation of ‘bottom-up’ and ‘top-down’ modelling approaches referred in this paper

From: Approaches for modelling the energy flow in food chains

Models Brief description Common benefits Common limitations Common assumptions
Bottom-up approaches
LCA models A process or product based evaluation method of the energy use and GHG emissions, typically using a ‘cradle to grave’ approach - Measure high quality of energy and GHG emissions data Level of detail required increases the complexity of data collection - Secondary to primary energy conversion factors
   - Capture the intricacies and complex nature of the food-energy chain - Analysis is usually very specific to country and product/process - Transportation distances and fuels
   - Allow the identification of energy and GHG emission hot-spots - Information from different sources cannot be combined, unless uncertainties and assumptions are clearly specified - Imported energies and GHG emissions
    - Choice of functional units and demarcations between system add complexity to the method - Agricultural sector energies and GHG emissions
    - Ignores the holistic industry impact on the product/process, i.e. static model - Waste disposal and storage emissions
    - LCA needs constant update  
MARKAL and sub-models - Demand-driven multi-period linear programming and cost optimisation tools - Technologies and processes can be explicitly modelled in detail - Results depend on the accuracy of demand-inputs and description of technological processes - Description of technological processes
  - Simultaneously assess the impact of several technologies through the partial equilibrium of demand and supply of energy - Currently being employed in various energy research studies, and various extensions/innovations of the model are being developed - Discount rates of technologies impact the partial equilibrium when forecasting energy demands - Discount rates and future energy demands
   - Allows the explicit modelling of the evolution of demand and energy prices - Does not have information feedback to the wider economy  
    - MARKAL databases have to be regularly updated  
Regression models Find the causal relationship between the dependent and independent variables in the food-energy chain - Relatively easy to use and construct - Require large amount of data to ensure proper correlations - Determination of errors, and validity of models are subjective to modeller
   - Provide a simplistic description of problem, and allows quick approximations of different policies - Provide only a quantitative evaluations, and does not show the factors causing the inefficiencies (depends on the level of disaggregation of the model) - General assumptions relate to the energy consumption and GHG emissions in the data collection phase of the study
    - Assumptions are implicit in the model, hence constant updates required - Data points outside three standard deviations require further investigation
Top-down approaches
Economic Input-output (IO) models Provide the aggregated monetary/energy flow through an economy - Analyse the impact of the entire economy on each industry, and inter-industry relationships - Stop at the point of purchase, and ignore waste and imports - Employ the principle of embodied energy to convert monetary to energetic values
   - Data are usually obtained from same sources, which provides consistency in analyses - Aggregated data analysis prevents detection of specific energy/environmental hot-spots - Economic IO analysis requires energy/GHG assumptions for wastes and imports
   - Quantify the impact/weight of each sector, and therefore allows identification of low-performing sector Frequency of national IO tables is low - Linear production technologies [2]
    - No capacity constraints [111]
     - Sector homogeneity [111]
     Usually assume a constant level of technologies for future analyses [2,111]
     - Import emissions usually based on domestic production technologies [2]
Index decomposition analysis models (IDA) Decompose aggregate energy and GHG emissions data into pre-defined factors to measure the relative impacts over specific time periods has been used for: (i) energy demand and supply, (ii) energy-related gas emissions, (iii) material flows and dematerialisation, (iv) national energy efficiency trend monitoring and (v) cross-country comparisons [70] - Method is relatively quick and simple to implement - Require an adequate level of disaggregation, else actual effects are not clearly identified Energy and GHG intensities are usually based on monetary outputs, as opposed to physical outputs
  - LMDI approach has no residuals in decomposition process - Laspeyres index is simple to implement, but calculates with residuals  
  Provide quick access to assessing the overall impact of policy measures on the economy   
Dynamic models Aim at predicting future energy and GHG expectations of the food-energy chain Can provide indication of future energy and policy expectations - Technological effects are often implicitly accounted in models - Economic growth rates
    - Can require significant level of assumptions, which questions the validity of such models - Time preferences
    - Studies related to the food chain are scarce - Population growth rates
    Inflation and depreciation rates