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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

})(),(;{max)),();((1

)(),(TtEtZYPEtZtP j

M

jj

ETZY

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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

0

]''[min),,,,,( dtKrXwetKElErwJ ito

)()( tKtIK 0)0( KK 0)( tK

],),(),(),([),( ttKtKtXFtEltE

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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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Thank you for your attention!