# Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Data and Methodology

_{t}

_{−1}, y

_{t}

_{−2}, …, y

_{t}

_{−p}), the representative ANN for the case of a single hidden layer has the following mathematical representation

## 4. Results

## 5. Discussions and Policy Implications

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ETF | Exchange Traded Funds |

ANN | Artificial Neural Network |

MLP | Multi-Layer Perceptron |

LSTM | Long Short-Term Memory |

GRU | Gated Recurrent Unit |

QCLN | First Trust NASDAQ Clean Edge Green Energy Index Fund |

ICLN | iShares Global Clean Energy |

PBW | Invesco WilderHill Clean Energy ETF |

TAN | Invesco Solar ETF |

NCEI | National Centre for Environmental Information |

IPCC | Intergovernmental Panel on Climate Change |

COP | Conference of the Parties |

ADB | Asian Development Bank |

GARCH | Generalized Auto-Regressive Conditional Heteroscedasticity |

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Return_Qcln | Return_Icln | Vol_Qcln | Vol_Icln | Temp_NY | Temp_L | Temp_T | |
---|---|---|---|---|---|---|---|

N | 4376 | 4376 | 4376 | 4376 | 4376 | 4376 | 4376 |

Mean | 0.0317 | −0.0176 | 70,662.3 | 72,002.6 | 55.6 | 51.4 | 62.3 |

Median | 0.1389 | 0.0000 | 14,830 | 25,445 | 55.5 | 51.3 | 63 |

Std | 1.7893 | 1.6175 | 145,349.6 | 139,501.2 | 16.5 | 11.3 | 13.9 |

Min | −13.9 | −13.7 | 100 | 1010 | 8.5 | 11.4 | 34.1 |

Max | 13.6 | 10.8 | 999,810 | 991,680 | 90.1 | 83.4 | 90.3 |

Skew | −0.4139 | −0.5235 | 3.3958 | 3.7514 | −0.1744 | −0.1980 | −0.0133 |

Kurtosis | 4.4585 | 5.5627 | 12.6491 | 15.4445 | −0.9641 | −0.2615 | −1.1707 |

**Table 2.**p-values for each model and each tested lag obtained from the Wilcoxon signed-rank test in case where Y → X. AR describes linear Granger causality. Cases where a causal relationship was detected are shown in bold.

Lag Value | Granger Causality | MLP | AR |
---|---|---|---|

30 | $\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ return_qcln | 3.300 × 10^{−85} ** | 0.9586 |

$\mathrm{temp}\_\mathrm{L}\text{}\nrightarrow $ return_qcln | 0.1558 | 0.7259 | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ return_qcln | 0.00181 ** | 0.3639 | |

$\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ return_icln | 0.0004598 ** | 0.9693 | |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ return_icln | 3.02839 × 10^{−52} ** | 0.6994 | |

$\mathbf{temp}\_\mathbf{T}\text{}\mathbf{\nrightarrow}$ return_icln | 0.02819 * | 0.2961 | |

60 | $\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ return_qcln | 1.189715 × 10^{−28} ** | 0.8919 |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ return_qcln | 6.802304 × 10^{−13} ** | 0.5499 | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ return_qcln | 1.066786 × 10^{−13} ** | 0.5741 | |

$\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ return_icln | 3.579381 × 10^{−05} ** | 0.9281 | |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ return_icln | 2.161692 × 10^{−49} ** | 0.3977 | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ return_icln | 1.197499 × 10^{−40} ** | 0.3111 |

**Table 3.**p-values for each model and each tested lag obtained from the Wilcoxon signed-rank test in case where Y → X. AR describes linear Granger causality. Cases where a causal relationship was detected are shown in bold.

Lag Value | Granger Causality | MLP | AR |
---|---|---|---|

30 | $\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ vol_qcln | 2.073000 × 10^{−36} ** | 0.0507 * |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ vol_qcln | 4.015626 × 10^{−09} ** | 0.0383 * | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ vol_qcln | 3.122777 × 10^{−108} ** | 0.6730 | |

$\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ vol_icln | 4.050622 × 10^{−49} ** | 0.3861 | |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ vol_icln | 1.890867 × 10^{−68} ** | 0.9022 | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ vol_icln | 2.989957 × 10^{−77} ** | 0.0974 * | |

60 | $\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ vol_qcln | 6.542931 × 10^{−16} ** | 0.0452 ** |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ vol_qcln | 2.369952 × 10^{−13} ** | 0.0668 * | |

$\mathbf{temp}\mathbf{\_}\mathbf{T}\text{}\mathbf{\nrightarrow}$ vol_qcln | 1.797172 × 10^{−50} ** | 0.1938 | |

$\mathbf{temp}\mathbf{\_}\mathbf{NY}\text{}\mathbf{\nrightarrow}$ vol_icln | 6.052480 × 10^{−62} ** | 0.6370 | |

$\mathbf{temp}\mathbf{\_}\mathbf{L}\text{}\mathbf{\nrightarrow}$ vol_icln | 1.858961 × 10^{−68} ** | 0.1864 | |

$\mathrm{temp}\_\mathrm{T}\text{}\nrightarrow $ vol_icln | 0.630244 | 0.0703 * |

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**MDPI and ACS Style**

Swarup, S.; Singh Kushwaha, G.
Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis. *Sustainability* **2022**, *14*, 11163.
https://doi.org/10.3390/su141811163

**AMA Style**

Swarup S, Singh Kushwaha G.
Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis. *Sustainability*. 2022; 14(18):11163.
https://doi.org/10.3390/su141811163

**Chicago/Turabian Style**

Swarup, Shivam, and Gyaneshwar Singh Kushwaha.
2022. "Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis" *Sustainability* 14, no. 18: 11163.
https://doi.org/10.3390/su141811163