The findings provide valuable insights for both communities and businesses in achieving the goals of net-zero and energy conservation.
Authors
Praveen Ranjan Srivastava, Indian Institute of Management (IIM), Rohtak, India.
Sachin Kumar Mangla, Full Professor and Director, Research Center for Digital Circular Economy for Sustainable Development Goals (DCE-SDG), Jindal Global Business School, O.P. Jindal Global University, Haryana, India.
Prajwal Eachempati, Senior AI/ML Technology Consultant, Ernst and Young, County Dublin D01N2T4, Ireland.
Aviral Kumar Tiwari, Indian Institute of Management Bodh Gaya, Bodh Gaya, Bihar, India; Rajagiri Business School, Rajagiri Valley Campus, Kochi, Kerala, India.
Summary
This study aims to optimize energy consumption to achieve sustainability, as there is a necessary foray into the green energy market by switching to net-zero carbon-emitting fuel alternatives. There is a need to decide when to switch to these net-zero fuels from conventional energy sources, which calls for a deeper investigation into the driving factors that lead to harmful energy emissions. This investigation will help monitor and curb such indicators to minimize harmful emissions or determine the opportune time to switch to green energy alternatives if the indicator levels cannot be controlled.
This research is motivated by recent studies that consider factors such as per capita carbon intensity and other sector-specific factors such as per dwelling carbon intensity and per value-added carbon intensity. However, these studies do not scientifically quantify the extent to which each element contributes to the final conventional energy consumption. Furthermore, sector-specific consumption indicators also need to be estimated to undertake significant design modifications for developing energy-efficient systems.
The consumption is predicted as “total consumption” for the four sectors considered: residential, industry, services, and transportation. Advanced machine learning algorithms such as random forest, gradient boosting, and deep neural network are used for this purpose. The essential factors/drivers for each sector are derived through the explainable artificial intelligence Shapley framework, which scientifically measures the contribution of each factor to energy consumption.
Consequently, the major key indicators for each of the four sectors under consideration are identified. Alternatively, the insights gained from the study may prompt a complete switch to alternative energy-efficient sources. The study findings provide valuable insights for both communities and businesses in achieving the goals of net-zero and energy conservation. Additionally, some examples are highlighted to demonstrate how practitioners can implement customized designs under each sector to reduce harmful emissions and promote energy efficiency. Suitable steps for stakeholder engagement and community participation in this regard are also suggested.
Published in: Energy Economics
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