Clean Technologies and Environmental Policy

, Volume 20, Issue 1, pp 113–125 | Cite as

An optimization study of a palm oil-based regional bio-energy supply chain under carbon pricing and trading policies

  • Ashkan Memari
  • Robiah Ahmad
  • Abd. Rahman Abdul Rahim
  • Mohammad Reza Akbari Jokar
Original Paper
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Abstract

Biomass residues due to their low bulk density typically require frequent transportation from biomass plantations in rural areas to conversion bio-energy power plants. This issue contrasts with environmental protection strategies, especially when power plants are facing different carbon reduction policies that enforce them to emit less than a given specific carbon amount. Although several researchers have investigated bio-energy supply chains concerning environmental policies, the majority of studies have been devoted to strategic decisions over a single planning period. This paper presents a multi-period bio-energy supply chain under carbon pricing (carbon tax) and carbon trading (cap-and-trade) policies at the tactical planning level. A mixed-integer linear programming model was adopted to optimize the proposed regional oil-palm biomass-to-bio-energy supply chain planning model. The numerical results indicate that when carbon pricing is in place when carbon tax increases linearly, carbon emissions’ reductions have a nonlinear trend, whereas both cost increase and carbon emissions’ reductions have a relatively upward trend in the carbon trading scheme. This paper also presents the sensitivity analysis of the proposed model regarding cost, emissions’ generation and supply chain performance. Finally, the paper recommends several significant practical implications and policy-making insights for managers and policymakers.

Keywords

Bio-energy supply chain Biomass Carbon tax Carbon trading Optimization Renewable energy 

List of symbols

Indices

I

Number of palm oil mills

J

Number of CHP plants

T

Number of planning periods

K

Number of transportation modes

i

Index of sources, i = 1, 2, …, I

j

Index of sinks j = 1, 2, …, J

t

Index of planning periods, t = 1, 2, …, T

k

Index of transportation modes, k = 1, 2, …, K

Parameters

Lij

Distance (in km, round-trip) between mill i and CHP plant j

Djt

Demand (in tonnage) of empty fruit bunches in CHP plant j in period t

AEFBit

Available empty fruit bounces at mill i in period t

Hjt

Holding capacity in tonnage at CHP plant j in period t

HCjt

Holding cost per ton of empty fruit bunches at CHP plant j in period t

BCj

Backlog cost per ton of empty fruit bunches at CHP j

\(B_{jt}^{\hbox{max} }\)

Maximum allowed backlog in CHP plant j during period t

LC

Lorry’s capacity

FCk

Fuel consumption of transportation mode k per km (L/km)

FPk

Fuel price of transportation mode k per litter

ck

Carbon emission of transportation mode k (kg/(t km))

chold

Carbon emission from inventory holding

p

Carbon price

Emax

Maximum allowed carbon emissions per period (carbon cap)

Decision variables

EFBijkt

Integer variable for the amount of empty fruit bunches shipped from mill i to CHP plant j through transportation mode k in period t

Ijt

Integer variable for the amount of inventory at CHP plant j in period t

Bjt

Integer variable for the amount of backlog at CHP plant j in period t

Objective functions

Z1

Total logistics costs

Z2

Total carbon emissions

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Notes

Acknowledgements

The authors would like to thank Universiti Teknologi Malaysia (UTM) and Ministry of Higher Education (MOHE) Malaysia under Fundamental Research Grant Scheme (FRGS) Vot 4F850 for financial support provided throughout this research. The first author is a researcher at UTM under the Postdoctoral Fellowship Scheme (PDRU Grant) for the project: “A Tuned NSGA-II for Optimizing JIT Distribution Networks” Vot No.Q.J130000.21A2.03E46.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Ashkan Memari
    • 1
    • 2
  • Robiah Ahmad
    • 1
  • Abd. Rahman Abdul Rahim
    • 1
  • Mohammad Reza Akbari Jokar
    • 2
  1. 1.Department of Engineering, UTM Razak School of Engineering and Advanced TechnologyUniversiti Teknologi MalaysiaKuala LumpurMalaysia
  2. 2.Department of Industrial EngineeringSharif University of TechnologyTehranIran

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