# Biomass Power Generation Industry Efficiency Evaluation in China

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Emerged Concerns

_{2}emissions [2]. Klevas and Denis used DEA to analyze the technical efficiency of some common renewable technologies, including biomass technology [3,4]. Based on their methods, Peng Zhou in China meliorated the DEA model and made it suitable for overall efficiency assessment [5]. DEA methods showed their advantage in evaluating the efficiency of the biomass power generation industry, and domestic scholars started to apply them to China’s own data. Zhao chose SWOT to analyze the industry state of the biomass power generation of China and thought that the advantage lied in the increasing electricity demand and changing electricity price, yet, the industry was sensitive to local policy and the local industrial environment [6]. Kautto also compared the regional and national biomass power plan, finding that if the regional plan can cooperate with national development better, the whole biomass industry efficiency would take a step toward a higher level [7]. In all, the regional or local situation indeed impacts the industry efficiency of biomass power generation.

## 3. Industrial Characteristics of Biomass Power Generation in China

#### 3.1. Conservative Rising Development Tendency

#### 3.2. Simple Impacts from the Industry Chain

Year | 2010 | 2020 | 2030 | 2050 | |
---|---|---|---|---|---|

Biomass resource type | Present biomass resource | 2.8 | 2.8 | 2.8 | 2.8 |

Newly-added organic waste | 0.6 | 1.7 | 2.2 | 2.7 | |

Present woodland growth | 0.05 | 0.3 | 0.7 | 1.37 | |

New ground marginal product | 0.05 | 0.3 | 1 | 2 | |

Total potential | 3.5 | 5.1 | 6.7 | 8.9 |

#### 3.3. Distinct Regional Difference

## 4. Methodology

_{ij}denotes the i-th input indicator of the j-th DMU, while y

_{rj}denotes the r-th output indicator of the j-th DMU. Finally, X

_{j}denotes the input vector of the j-th DMU, while Y

_{j}denotes the output vector of the j-th DMU.

#### 4.1. Drawbacks of Traditional DEA Models

#### 4.2. Additive-Based DEA Models

- (P1)
- The optima is between 0 and 1;
- (P2)
- The optima is 0 when DMU
_{o}is fully inefficient, while the optima is 1 when DMU_{o}is fully efficient; - (P3)
- The optima is well defined and unit invariant;
- (P4)
- The optima is strongly monotonic;
- (P5)
- The optima is translation invariant.

Model | P1 | P2 | P3 | P4 | P5 |
---|---|---|---|---|---|

CCR | Yes | Yes | Partially Units Invariance | Monotonic | NO |

Normalized weighted CCR | Yes | Yes | Units Invariance | Monotonic | NO |

BCC | Yes | Yes | Partially Units Invariance | Monotonic | Partially Translation Invariance |

Normalized weighted BCC | Yes | Yes | Units Invariance | Monotonic | Partially Translation Invariance |

Additive model | No | No | No | Monotonic | Yes |

Normalized weighted Additive Model | No | No | Units Invariance | Monotonic | Yes |

SBM | Yes | Yes | Yes | Monotonic | Yes |

RAM | Yes | Yes | Yes | Strongly Monotonic | Yes |

BAM | Yes | Yes | Yes | Monotonic | Yes |

#### 4.3. Generalized Additive-Based DEA Model-BMA Model

_{r}= σ

_{r}, Φ(Ω)

_{i}= σ

_{i}and M = 1. Here, σ

_{r}, σ

_{i}denote the sample standard deviation of the r-th output variable and the i-th input variable. It can be transformed into the SBM model when we set Ψ(Ω)

_{r}= s·y

_{ro}, Φ(Ω)

_{i}= m

**·**x

_{io}and M = 1. It can be transformed into the RAM model when we set Ψ(Ω)

_{r}= ${\text{R}}_{r}^{+}$, Φ(Ω)

_{i}= ${\text{R}}_{i}^{-}$ and M = (m + s), where ${R}_{i}^{-}={\overline{x}}_{i}-{\underset{\xaf}{x}}_{i}$ with ${\overline{x}}_{i}=\text{max}\{{x}_{ij},j=1,\dots ,n\}$, ${\underset{\xaf}{x}}_{i}=\text{min}\{{x}_{ij},j=1,\dots ,n\}$ and ${R}_{r}^{+}={\overline{y}}_{r}-{\underset{\xaf}{y}}_{r}$ with ${\overline{y}}_{r}=\text{max}\{{y}_{rj},j=1,\dots ,n\}$, ${\underset{\xaf}{y}}_{r}=\text{min}\{{y}_{rj},j=1,\dots ,n\}$. It can be transformed into the BAM model when we set Ψ(Ω)

_{r}= ${\text{U}}_{ro}^{+}$, Φ(Ω) = ${\text{U}}_{io}^{-}$ and M = (m + s), where ${L}_{io}^{-}={x}_{io}-{\underset{\xaf}{x}}_{i}$ and ${U}_{ro}^{+}={\overline{y}}_{r}-{y}_{ro}$. Moreover, it is obvious that the mappings Ψ(Ω) and Φ(Ω) control the unit invariance property, i.e., property (P2) and M control the properties of (P1), (P2) and (P4).

_{rj}, j = 1, …, n} and Φ(Ω) = max{x

_{ij}, j = 1, …, n} when p,q = ∞.

_{o}is fully efficient, while the optimum of Model (3) is not 0 when DMU

_{o}is fully inefficient.

_{o}is fully efficient; thus, the optimum is one. However, when DMU

_{o}is fully inefficient, the optimum varies when different M are chosen.

_{i}.), and it satisfies the translation invariance property.

#### 4.4. BMAS Model

## 5. Empirical Analysis

#### 5.1. Efficiency Analysis

Inputs/Outputs | Unit | Average | Std | Max | Min |
---|---|---|---|---|---|

Greenhouse Gas Emissions (I) | tCO_{2}/GWh | −203.961 | 542.0662 | 600 | −1223 |

O&M + CC Costs (I) | RMB/MWh | 14.69 | 25.4917 | 62.53 | −26.52 |

Investment Costs (I) | RMB/MWh | 38.15727 | 18.70269 | 76 | 14.96 |

Potential Job Creation (O) | Job/TWh | 7811.348 | 11,466.02 | 35,347.69 | 1.88 |

Potential Distributed Power Generation (O) | GWH/year | 54,870.36 | 35,121.65 | 133,296 | 6833 |

_{r}= σ

_{r}, Φ(Ω)

_{i}= σ

_{i}and M equal to 1, and the BMA model was transformed as the normalized weighted additive-based model, which is proposed by Lovell and Pastor [20]. In order to compare the results of the BMA model with those of the traditional DEA models, we use the BCC model, the BMA model and the BMAS model to evaluate the performance of the biomass power plants in China.

Biomass Power Plants | BCC Efficiency | BMA Efficiency | BMAS Efficiency | BCC Rank | BMA Rank | Location |
---|---|---|---|---|---|---|

Shangdong Pingyuan | 1.00 | 0.882389 | 7 | 9 | South | |

Hebei Wuqiao | 1.00 | 0.984928 | 9 | 8 | North | |

Hebei Yuanshi | 1.00 | 0.981673 | 10 | 10 | North | |

Anhui Shouxian | 1.00 | 0.859524 | 11 | 11 | South | |

Jilin Changling | 1.00 | 1.000000 | 1.010540 | 6 | 3 | North |

Neimeng Zhaoxin | 1.00 | 1.000000 | 1.012733 | 3 | 2 | North |

Hengshui Taida | 1.00 | 0.988927 | 2 | 5 | North | |

Jilin Gongzhuling | 1.00 | 1.000000 | 1.033233 | 1 | 1 | North |

Dongping Guangyuan | 1.00 | 0.887661 | 5 | 6 | South | |

Shandong Pingquan | 1.00 | 0.886453 | 8 | 7 | South | |

Jiangxi Ganxian | 0.331 | 0.888768 | 4 | 4 | South |

#### 5.2. Group Analysis

Group | Mean | Std | Max | Min |
---|---|---|---|---|

North | 0.9923 | 0.0079 | 1.0000 | 0.9814 |

South | 0.8009 | 0.0421 | 0.8888 | 0.7595 |

Variable | Mann–Whitney U | Prob | K-S | Prob |
---|---|---|---|---|

efficiency | 15 | 0.006 | 1.651 | 0.009 |

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Yan, Q.; Tao, J.
Biomass Power Generation Industry Efficiency Evaluation in China. *Sustainability* **2014**, *6*, 8720-8735.
https://doi.org/10.3390/su6128720

**AMA Style**

Yan Q, Tao J.
Biomass Power Generation Industry Efficiency Evaluation in China. *Sustainability*. 2014; 6(12):8720-8735.
https://doi.org/10.3390/su6128720

**Chicago/Turabian Style**

Yan, Qingyou, and Jie Tao.
2014. "Biomass Power Generation Industry Efficiency Evaluation in China" *Sustainability* 6, no. 12: 8720-8735.
https://doi.org/10.3390/su6128720