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Enduse Extract Efficiency

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Enduse Extract Efficiency

  1. 1. M. Khairul Bahri Review Model Which is the better end use efficiency or extraction efficiency? by : Muhamad Khairul Bahri POSTGRADUATE PROGRAM DEVELOPMENT STUDI ES SCHOOL OF ARCHI TECTURE, PLANNI NG AND POLI CY DEVELOPMENT I NSTI TUT TEKNOLOGI BANDUNG 2008
  2. 2. M. Khairul Bahri Phenomenon: Efficiency in Natural Resource Utilization. Problem : Which is the better end use efficiency or extraction efficiency. This paper is a further task of Annababette Wills’s End-use or extraction efficiency in natural resource utilization: which is better ? (System Dynamics Review vol 14, 1998, page 163-188). Wills (1998) stated that end use efficiency is socially optimal for society need. Differ from Wills’s work, this paper focus on understanding dynamic interaction between two types of efficiency. My paper conclude that from starting time, end use efficiency is a very important factor but until its time, extraction efficiency will need more notice to support society need. List Variables Endogenous Exogenous Excluded Cumulative Use, Cumulative Service Cost Increased Unit (Resource) Government Subsidy on Demand (Resource) Maximum Subsitution Fraction (dimensionless) technology development Effort Multiplier (Dimensionless) Unit, unit cost , substitute price (US$/ Actual Cost (US$/ Resource) Resource) Use Rate, Service Demand Subsititute Teechnology Adjustment, delay (Resource/year) efficiency technology (year) Price/Service Unit (US$/ Resource) Efficiency Initial (Technology) Subsitution Fraction,Potential Initial Market Size (resource/year) Subsitution Fraction (Dimensionless) Market Growth Rate, percent invested (dimensionless) Cost all efficiency type (US$/technology) Sales Revenue, R-D Investment (US$/year) All type Efficiency (Technology) Profit US$/year The first modified Model Cumulative_Use Annual_demand sector mining Profit_Loss service_demand RD_investment sales_revenue mining_margin ciu Cumulative_Use effort_multiplier mining_selling_price_to_service percent_RD unit_cost operating_cost actual_cost sector subsitution extraction_efficiency sector service Annual_demandprice_per_service_unit end_use_efficiency derivn_actual_cost price_subsitute max_subsitution price_per_service_unit market_size substitution_fraction potential_subsitution_fraction switch_1 fraction_extraction_cost_dynamic service_demand subsitution_time unit substitution_fraction fract_extraction_cost_fixed substitution_change_rate cum_service_demand service By modified Wills’s work, this model has new variables: Profit_Loss, Mining_Margin, Mining_selling_price to service, derivn_actual_cost (the first derivative of actual cost variable) and fract_extraction_cost_dynamic.
  3. 3. M. Khairul Bahri In short words, this model work on two basic principles, if profit_loss > 0, then allocate it to RD_investment and if derivn_actual_cost greater then RD allocation (research and development) for extraction also rise. This model sums that we need interact dynamically between two efficiency, indeed extraction efficiency need more allocation while extraction go to very expensive matter to support society need. 40 3 cum_service_demand 2.500 1 1 service_demand 30 2.000 3 3 1.500 2 20 2 1 1 1.000 3 3 10 12 500 12 123 3 01 2 3 12 01 2 3 0 50 100 150 200 0 50 100 150 200 Time Time 6 3 40 3 price_per_service_unit 5 actual_cost 30 4 23 1 3 20 3 2 1 2 23 1 10 11 2 3 3 123 12 0 01 2 3 123 0 50 100 150 200 0 50 100 150 200 Time Time 0,4 3 1 substitution_fraction Cumulative_Use 0,3 600 23 1 0,2 23 400 1 23 1 0,1 200 3 23 12 1 0,0 1 2 3 123 01 2 3 0 50 100 150 200 0 50 100 150 200 Time Time Line 1 and 2 allocation of 15%, 20% for extraction DERIVN(actual_cost) technology of sales revenue (switch_1=0). While GRAPH(derivn_actual_cost,0,0.1,[0,0.08,0.2,0.3,0.4,0.5, switch_1 set 1, line 3 show that extraction efficiency 0.6,0.65,0.7"Min:0;Max:1.5"])*(1- has dynamics RD funding – as the right equation. switch_1)+switch_1*fract_extraction_cost_fixed 1,0 1 1,0 1 2 3 extraction_efficiency 2 3 end_use_efficiency 3 0,8 1 0,8 2 3 0,6 1 0,6 1 2 2 3 0,4 0,4 1 23 12 0,2 1 3 0,2 123 1 0,0 0,0 0 50 100 150 200 0 50 100 150 200 Time Time The simulation shows that to fulfill society needs, we need dynamics interaction between two efficiency. The right-above graph reveals that extraction efficiency more important since year 150.
  4. 4. M. Khairul Bahri 1 3 5 Cumulative_Use Annual_demand 600 2 23 1 3 1 1 4 2 3 1 400 123 23 3 1 200 2 23 1 3 01 2 3 11 2 0 50 100 150 200 0 50 100 150 200 Time Time The second modified Model The second modified model based on addition of profit loss variable for two sector (mining and service). If actual cost greater then R&D for extraction cost rise. In service sector, if subsitution greater then we shall rise R&D for end use efficiency. For comprehensive view, how important combination between two efficiency, effort equation change to be ((1+(Cumulative_Use/ciu)^8) and simulation time set to 300 years- simulation. + Extraction Effort + - Extraction Cost - Cumulative Use End Use + + Efficiency Potential Subsitution + Operating cost Selling price mining to service + service + Service + - - Actual Subsitution + - + Sales Revenue RD Investment Annual Demand Selling price in society Sector Service End Use Market Size + + + + - - + Derivn Actual Subsitution Service demand - + Actual Subsitution Extraction Cost + + + + Extraction Sales Revenue RD Investment Efficiency Operating cost Sector Mining Extraction + + mining + Derivn - + Extraction Cost
  5. 5. M. Khairul Bahri By looking at causal loop we simply can conclude that RD for extraction efficiency rise while actual cost more expensively. In line with that, if subsitution rise then service sector tend to set greater RD funding for end use technology. End Use Efficiency-end value Extraction Efficiency-end value Simulation 1 0,0720 0,0787 Simulation 2 0,0342 0,0553 Simulation 3 0,0698 0,0608 Simulation 4 0,0332 0,0430 Simulation 1 : variable percen_RD_extraction dan percen_RD_end-use not increase. Simulation 2 variable percen_RD_extraction not increase while percen_RD_end-use increased twice; Simulation 3 : variable percen_RD_extraction set twice and percen_RD_end-use no incresae; Simulation 4 : variable percen_RD_extraction and percen_RD_end-use set twice together. Simulation reveals that increasing percen_RD_end-use force setting higher of extraction efficiency. This simply give us more understanding that society need two efficiency work together dynamically. Cumulative_Use Annual_demand sector mining Profit_Loss_Mining_Sector service_demand sales_revenue RD_extraction_tech ciu mining_margin Cumulative_Use effort_multiplier selling_price_mining_to_service unit_cost operating_cost_mining_sector percent_RD_extraction actual_cost extraction_efficiency end_use_efficiency Annual_demand derivn_actual_cost percent_RD_extraction sector subsitution price_per_service_unit price_subsitute max_subsitution sector service operating_cost_sektor_service selling_price_in_society potential_subsitution_fraction end_use_efficiency service_demand service_margin subsitution_time price_per_service_unit substitution_fraction Profit_Loss_Service substitution_change_rate market_size substitution_fraction selling_price_mining_to_service service_demand market_growth_rate sales_revenue_service unit sector market cum_service_demand market_size previos_ms_x_mkt_growth service percent_RD_end_use Profit_Loss_Service previous_market_size RD_end_use_tech initial_market_size substitution_fraction derivn_SF
  6. 6. M. Khairul Bahri cum_service_demand 20 service_demand 4 2 24 4 3.000 4 2 15 2 4 2 2 4 1 2 2.000 2 10 24 4 4 4 3 2 3 13 1 3 13 1 1.000 3 1 5 13 2 4 13 1 3 13 23 1 1 01 2 3 4 01 2 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Time Time 10 price_per_service_unit 34123412341 200 12 2 3 4 12 150 2 4 actual_cost 34 2 5 4 4 100 2 2 1 13 1 4 13 50 2 13 123 3 4 1 0 4 01 2 3 4 1 2 3 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Time Time 1,0 1 1,0 1 2 2 3 3 4 0,8 0,8 41 extraction_efficiency end_use_efficiency 1 0,6 3 0,6 2 23 4 4 0,4 0,4 1 0,2 1 0,2 23 2 4 3 1 1 3 1 4 23 1 3 1 4 234123 1 2 4 2 4 2 3 1 4 0,0 4 0,0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Time Time 1,0 1234 1234 12 4 4 24 2 substitution_fraction 0,8 500 23 3 13 Cumulative_Use 34 4 1 1 400 23 0,6 12 1 300 4 3 3 0,4 2 4 200 1 0,2 100 2 4 1 0,0 1 2 3 4 01 2 3 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Time Time This simulation has wider conclusion that extraction, end use dan subsitution (technology) has dependent and important rules to fulfill society need. While non renewable resource tend to scarce , subsitution technology will has very important concern.
  7. 7. M. Khairul Bahri sektor technology end_use_efficiency_delayed cost_end_use_efficiency delay_time end_use_efficiency_expected efficiency_initial RD_end_use_tech best_end_use_efficiency end_use_efficiency best_end_in_use average_end_use_efficiency technology_residence_time old_end_use_out extraction_tech_advanced best_extraction_tech_in best_extraction_technology RD_extraction_tech best_extraction_technology extraction_technology_initial cost_extract_tech_advance extraction_tech_change_delayed extraction_tech_res_time avg_extract_efficiency delay_time extraction_efficiency avg_extract_efficiency old_extraction_tech_out

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