Abstract

In this study, the impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution from 1971 to 2011 is investigated using the statistically inspired modification of partial least squares (SIMPLS) regression model. There was evidence of a linear relationship between energy, agriculture, macroeconomic and human-induced indicators and carbon dioxide emissions. Evidence from the SIMPLS regression shows that a 1% increase in crop production index will reduce carbon dioxide emissions by 0.71%. Economic growth increased by 1% will reduce carbon dioxide emissions by 0.46%, which means that an increase in Ghana's economic growth may lead to a reduction in environmental pollution. The increase in electricity production from hydroelectric sources by 1% will reduce carbon dioxide emissions by 0.30%; thus, increasing renewable energy sources in Ghana's energy portfolio will help mitigate carbon dioxide emissions. Increasing enteric emissions by 1% will increase carbon dioxide emissions by 4.22%, and a 1% increase in the nitrogen content of manure management will increase carbon dioxide emissions by 6.69%. The SIMPLS regression forecasting exhibited a 5% MAPE from the prediction of carbon dioxide emissions.

Highlights

  • Climate change as gained global prominence as a result of its long-term effect on the globe

  • Data The study examines the impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution using the statistically inspired modification of partial least squares (SIMPLS) regression model

  • gross domestic product (GDP) per capita increased by 1% will reduce carbon dioxide emissions by 0.46%, supports the environmental Kuznets curve hypothesis that an increase in a country’s economic growth leads to a reduction in environmental pollution

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Summary

Introduction

Climate change as gained global prominence as a result of its long-term effect on the globe. Climate change mitigation through a sustainable global action deems essential to limit the rising levels of greenhouse gas emissions (Asumadu-Sarkodie and Owusu 2016b; Owusu and Asumadu-Sarkodie 2016). This global effort has propelled a lot of research interest in environmental, energy and agricultural sustainability. This paradigm shift in scientific research has increased the interest in using historical data to predict and/or explain the causal-effect between response and predictor variables. Multicollinearity among study variables are problematic in the aforementioned models

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