Vol. 6 No. 1 (2024)

Open Access

Article

Article ID: 3491

Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network

by Bingzi Jin, Xiaojie Xu

Global Finance Review, Vol.6, No.1, 2024; 328 Views, 138 PDF Downloads

Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.

Open Access

Article

Article ID: 3534

On the empirical nexus of agricultural credit facility scheme and agricultural output dynamics in Uganda

by Namuyomba Monica Nsamba, Joel Ede Owuru

Global Finance Review, Vol.6, No.1, 2024; 58 Views, 38 PDF Downloads

The role of the agricultural sector in economic growth cannot be overemphasized. Agriculture is basically one of the key sub-sectors that enhance economic growth in all economies of the world. Since no sector of the economy can grow without enough capital, agricultural credit is considered as imperative for improved agricultural output. To contribute to knowledge in this regard, this study examined the impact of agricultural credit fund (ACF) on agricultural output in Uganda using quarterly data from 2009 to 2021. By employing the Autoregressive Distributed Lag (ARDL) framework, the results revealed that ACF has no significant effect on agricultural output in Uganda in the short run, but it significantly has positive effect on the sector in the long run. We control for economic growth, proxied by gross domestic product (GDP), interest, inflation, and exchange rates and find that exiting level of GDP, spurred agricultural output, while the rate of interest and inflation retard agricultural productivity (output), especially in the long run in Uganda. We recommend that the government of Uganda needs to increase agricultural financing through the credit facility scheme for further productivity of the sector.