agriregionieuropa dynamic adjustments in dutch greenhouse sector due to environmental regulations...
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Dynamic adjustments in Dutch greenhouse sector due to environmental regulations
Daphne Verreth 1, Grigorios Emvalomatis 1, Frank Bunte 1,2, and Alfons Oude Lansink 1
1 Wageningen University, The Netherlands2 Agricultural Economics Research Institute, The Netherlands
122nd European Association of Agricultural Economists Seminar
Evidence-Based Agricultural and Rural Policy MakingMethodological and Empirical Challenges of Policy Evaluation
February 18th, 2011, Ancona
associazioneAlessandroBartola studi e ricerche di economia e di politica agraria
Centro Studi Sulle Politiche Economiche, Rurali e AmbientaliUniversità Politecnica delle Marche
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
In Dutch agriculture greenhouse horticulture sector is the most energy-intensive sector
Government stimulates sector to reduce energy use and CO2 emissions by taxes, grants incentives
Dutch firms respond by substitution of variable inputs or by investing in energy-saving technologies
Investment choices of greenhouse farmers represent long-term commitments
Dynamic optimization approach
Background
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Objective
To assess Dutch greenhouse farmers’ responses to policies that would affect the prices of different categories of energy inputs
– Emphasis on two phases: • Firms are assumed to maximize short-term profit at given
quantities of quasi-fixed factors and a given energy use level.
• Firms are assumed to minimize energy costs over an infinite horizon, producing at least given energy use level
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Theoretical Framework
Phase 1: restricted profit maximization– Static model– Profit dependent on capital and quantity of used
energy
– Variable netputs: output and ‘other inputs’– Fixed inputs: land, capital, labour, quantity of used
energy and time-trend
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Theoretical Framework
Phase 2: Cost minimization– Dynamic model– At least produce ‘energy-used’ quantity E.
– Variable inputs: electricity, gas, ‘other’ and price of capital
– Quasi-fixed inputs: Energy-related capital , electricity output, energy-used quantity and time trend
– Investments energy capital represented by adjustment costs
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Phase 2: Cost minimization
X: Vector of energy inputs (i.e. gas, electricity, other energy) at price wK: Energy-related capital at price rI: Gross rate of investment E: Energy output quantityEl: Electricity output
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Investment demand function
Investment demand function different from disinvestment/zero investment and positive investment
Switching regressions model ordered probit model
Inverse mills ratio added to investment demand eq. :
Multivariate linear accelerator mechanism
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Data
Agricultural Economics Research Institute (LEI) data
Greenhouse horticultural farms data: output, capital stock, energy-using capital, land, labour, expenditures on energy gas, heat, fuel, electricity, pesticides, fertilizers, seeds, etc.
Unbalanced panel data, Time span: 2001-2008
Profit function: 214 firms (909 obs)Cost function : 100 firms (369 obs)
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Results I
Short-Run Elasticities Profit Maximization
* Significant at 5% level** Significant at 1% level
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Results II
Short-Run Elasticities Cost Minimization
* Significant at 5% level** Significant at 1% level
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Results III
Long-Run Elasticities Cost Minimization
Adjustment rate: 25.52%
0.874
1.253
1.569
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Scenarios
Effects on quantities energy inputs, net investment
– Baseline scenario: no changes– Scenario 1: gas price increases (tax of 10%)– Scenario 2: electricity price increases (tax of 10%)
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Scenario results
Negative investment and positive shadow cost of capital
Firms are over-capitalized Optimal to decrease size capital stock
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Scenario results
Disinvestment smaller than baseline scenario (-4.8%). Shadow price of capital also smaller (-3%).
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Scenario results
Disinvestment higher (5.3%) and shadow cost of capital higher (2.9%) than baseline scenario. Quantity of electricity increases slower than other two scenarios.
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Conclusions
Energy-related capital is using mostly electricity
Increase in energy production result in an increase in the volume of gas, but a decrease in the volumes of the other two inputs.
Dutch greenhouse firms behave in the sense that they want to maximise their profit.
A small number of energy input elasticities change significantly in magnitude when analysed in the long-run
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Policy implications
If Dutch firms invest in energy-using capital, they will use more volume of electricity and the aggregate group of other energy , but the volumes of gas will decrease.
Investment incentivesLarge elasticities imply that substitution
between energy inputs is easy. Policies could be directed towards reducing
use of more polluting inputs.
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122nd EAAE Seminar, February 18th , 2011, Ancona (Italy)
Further research
To simulate ex-ante energy CO2 emission policy
Connect effects on energy inputs to the profitability of the firm, estimated in the first stage of our model
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