# Analysis of Micro Value Flows in the Value Chain of Eco-Innovation in Agricultural Products

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

#### 2.1. The Meaning and Application of GERT

#### 2.2. Research on Green Innovation and Innovation Ecosystems

## 3. Related Conceptual and Methodological Foundations

#### 3.1. Green Innovation

#### 3.2. Green Innovation Value Chain for Agricultural Products

#### 3.3. GERT Diagrammatic Review Method

## 4. Method of Value Flow GERT Network Model Construction

#### 4.1. Value Flows GERT Network Model Key Influencing Factors

#### 4.2. Value Transfer Function Construction for the GERT Network Model of Value Flows

#### 4.3. Determination of Equivalent Transfer Probabilities of Value Flows and the Establishment of Equivalent Moment Mother Functions

#### 4.3.1. Equivalent Transmission Probability of Value Flows

#### 4.3.2. Value Flow Equivalence Moment Function

#### 4.3.3. Solution to Main Factors

- Parameter Solution Procedure

- Based on the practical problems and basic characteristics related to the agricultural green innovation value chain, the GERT network model of the value flow of the agricultural green innovation value chain is constructed by structurally describing it, analysing the relationship between each innovation subject in it and the composition of each parameter of the value flow.
- Use the W function to combine the basic value parameters of each value flow activity in the value flow GERT network of the agricultural green innovation value chain obtained, and construct the value flow transfer relationship function of the value flow GERT network model of the agricultural green innovation value chain.
- Determine the value flow equivalence transfer function and equivalence transfer probability of the value flow GERT network model of the agricultural green innovation value chain, which can use the topological equation formula of the signal flow diagram (Mason diagram).
- According to the definition of the W function, the equivalence moment matrix of the GERT network model of the value flow of the agricultural green innovation value chain is determined.
- Using the equivalent moment matrix, derive the basic value parameters of each value flow activity in the GERT network model of the value flow of the agricultural green innovation value chain, and calculate the analytical solutions of the basic value parameters of each value flow activity.

- 2.
- Value added value flows and their variance solving

- 3.
- Value flow value added factor

## 5. Experient of Green Innovation Input Optimization Program for Agricultural Product Value Chain

#### 5.1. GERT Network Synergistic Organization Mechanism for Green Innovation Value Chain Value Flows

#### 5.2. Multi-Objective Planning Model Construction for Value Management of Green Innovation Value Chain of Agricultural Products

#### 5.3. Agricultural Value Chain Green Innovation Input Optimization Implementation Plan

#### 5.3.1. Configuration Goals

- Maximize the four goals of economic value, ecological value, innovation value and social value
- Maximize the integrated value of green innovation for agricultural products.

#### 5.3.2. Configuration Method

## 6. Analysis of Green Innovation Input Optimization Scheme for Agricultural Product Value Chain

#### 6.1. Evaluation Model Establishment

**Propositions**

**1.**

**Propositions**

**2.**

**Propositions**

**3.**

**Propositions**

**4.**

#### 6.2. Effectiveness of the Implementation of Green Innovation Input Optimization Program for Agricultural Products

## 7. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Key to the GERT network model of value flows in the agricultural green innovation value chain Schematic diagram of the combined effect of influencing factors.

**Figure 5.**Hierarchy diagram for optimal allocation of innovation resources under multi-objective decision making.

**Figure 6.**Distribution of innovative resource optimisation control measures under multi-objective decision-making.

**Figure 7.**A model for optimal allocation of green innovation resources under multi-objective decision making.

Events | Probability | Related Parameters | ||
---|---|---|---|---|

${\mathit{p}}_{\mathit{i}\mathit{j}}$ | $\mathit{C}$ | $\mathit{R}$ | $\mathit{I}$ | |

(1, 1) | 0.1 | 0.28 | 0.48 | 0.28 |

(1, 2) | 0.9 | 0.31 | 0.40 | 0.25 |

(2, 3) | 1 | 0.16 | 0.55 | 0.31 |

(3, 3) | 0.2 | 0.18 | 0.53 | 0.33 |

(3, 4) | 0.8 | 0.57 | 0.38 | 0.35 |

(4, 1) | 0.1 | 0.44 | 0.46 | 0.34 |

(4, 5) | 0.45 | 0.35 | 0.42 | 0.32 |

(4, 6) | 0.45 | 0.39 | 0.39 | 0.34 |

(5, 7) | 1 | 0.32 | 0.44 | 0.38 |

(6, 7) | 1 | 0.37 | 0.40 | 0.33 |

(7, 7) | 0.3 | 0.35 | 0.38 | 0.36 |

(7, 8) | 0.7 | 0.72 | 0.57 | 0.67 |

(8, 8) | 0.2 | 0.48 | 0.41 | 0.49 |

(8, 9) | 0.8 | 0.52 | 0.44 | 0.52 |

(9, 9) | 0.3 | 0.38 | 0.46 | 0.35 |

(9, 10) | 0.7 | 0.31 | 0.39 | 0.45 |

Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ | Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ |
---|---|---|---|

(1, 1) | 0.1 | (5, 7) | 1 |

(1, 2) | 0.9 | (6, 7) | 1 |

(2, 3) | 1 | (7, 7) | 0.23 |

(3, 3) | 0.2 | (7, 8) | 0.77 |

(3, 4) | 0.8 | (8, 8) | 0.12 |

(4, 1) | 0.495 | (8, 9) | 0.88 |

(4, 5) | 0.495 | (9, 9) | 0.23 |

(4, 6) | 0.01 | (9, 10) | 0.77 |

**Table 3.**GERT network activity parameters after implementing moderation measures under single target planning.

Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ | Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ |
---|---|---|---|

(1, 1) | 0.1 | (5, 7) | 1 |

(1, 2) | 0.9 | (6, 7) | 1 |

(2, 3) | 1 | (7, 7) | 0.265 |

(3, 3) | 0.2 | (7, 8) | 0.735 |

(3, 4) | 0.8 | (8, 8) | 0.16 |

(4, 1) | 0.473 | (8, 9) | 0.84 |

(4, 5) | 0.473 | (9, 9) | 0.16 |

(4, 6) | 0.054 | (9, 10) | 0.84 |

Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ | Events $(\mathit{i},\mathit{j})$ | Probability ${\mathit{p}}_{\mathit{i}\mathit{j}}$ |
---|---|---|---|

(1, 1) | 0.1 | (5, 7) | 1 |

(1, 2) | 0.9 | (6, 7) | 1 |

(2, 3) | 1 | (7, 7) | 0.23 |

(3, 3) | 0.2 | (7, 8) | 0.77 |

(3, 4) | 0.8 | (8, 8) | 0.104 |

(4, 1) | 0.492 | (8, 9) | 0.896 |

(4, 5) | 0.492 | (9, 9) | 0.233 |

(4, 6) | 0.016 | (9, 10) | 0.767 |

**Table 5.**Comparison table of comprehensive benefits of green agricultural technology innovation under different target planning.

$\mathit{C}{\mathit{I}}_{1}$ | $\mathit{C}{\mathit{I}}_{2}$ | $\mathit{C}{\mathit{I}}_{3}$ | |
---|---|---|---|

Comprehensive income | $5.69x-C$ | $6.92x-C$ | $9.88x-C$ |

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**MDPI and ACS Style**

Wang, Y.; Wang, Y.; Fan, P.
Analysis of Micro Value Flows in the Value Chain of Eco-Innovation in Agricultural Products. *Sustainability* **2022**, *14*, 7971.
https://doi.org/10.3390/su14137971

**AMA Style**

Wang Y, Wang Y, Fan P.
Analysis of Micro Value Flows in the Value Chain of Eco-Innovation in Agricultural Products. *Sustainability*. 2022; 14(13):7971.
https://doi.org/10.3390/su14137971

**Chicago/Turabian Style**

Wang, Yang, Yifeng Wang, and Peng Fan.
2022. "Analysis of Micro Value Flows in the Value Chain of Eco-Innovation in Agricultural Products" *Sustainability* 14, no. 13: 7971.
https://doi.org/10.3390/su14137971