agriregionieuropa 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 Netherlands 2 Agricultural Economics Research Institute, The Netherlands 122 nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 18 th, 2011, Ancona associazioneAlessandroBartola studi e ricerche di economia e di politica agraria Centro Studi Sulle Politiche Economiche, Rurali e Ambientali Università Politecnica delle Marche

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy)  In Dutch agriculture greenhouse horticulture sector is the most energy-intensive sector  Government stimulates sector to reduce energy use and CO 2 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Phase 2: Cost minimization X:Vector of energy inputs (i.e. gas, electricity, other energy) at price w K:Energy-related capital at price r I:Gross rate of investment E:Energy output quantity El: Electricity output

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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:  Profit function: 214 firms (909 obs)  Cost function : 100 firms (369 obs)

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Results I Short-Run Elasticities Profit Maximization * Significant at 5% level ** Significant at 1% level

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Results II Short-Run Elasticities Cost Minimization * Significant at 5% level ** Significant at 1% level

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Results III Long-Run Elasticities Cost Minimization Adjustment rate: 25.52%

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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%)

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Scenario results Baseline Scenario Period (yr) Q. Gas Q. other energy Q. Electricity JkJk Negative investment and positive shadow cost of capital Firms are over-capitalized Optimal to decrease size capital stock

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Scenario results Disinvestment smaller than baseline scenario (-4.8%). Shadow price of capital also smaller (-3%). Scenario 1: Gas price increases Period (yr) Q. Gas Q. other energy Q. Electricity JkJk % 19.4% 12.2% %19.7%12.2% %21.3% 11.3% % 26.1% 9.1% %34.5%7.4%0.0666

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Scenario results Scenario 2: electricity price increases Period (yr) Q. Gas Q. other energy Q. Electricity JkJk % 106%-38.3% %108%-37.9% %118%-34.4% % 133%-29.9% %170%-23.9% Disinvestment higher (5.3%) and shadow cost of capital higher (2.9%) than baseline scenario. Quantity of electricity increases slower than other two scenarios.

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 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 incentives  Large elasticities imply that substitution between energy inputs is easy.  Policies could be directed towards reducing use of more polluting inputs.

agriregionieuropa 122 nd EAAE Seminar, February 18 th, 2011, Ancona (Italy) Further research  To simulate ex-ante energy CO 2 emission policy  Connect effects on energy inputs to the profitability of the firm, estimated in the first stage of our model

agriregionieuropa Thank you for your attention!