Li Shuyu, Wang Qiang, Jiang Xue-Ting, Li Rongrong
School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China.
Institute for Energy Economics and Policy, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China.
J Clean Prod. 2022 Sep 20;367:132996. doi: 10.1016/j.jclepro.2022.132996. Epub 2022 Jul 8.
According to the United Nations Environment Programme, the COVID-19 pandemic has created challenges for the economy and the energy sector, as well as uncertainty for the renewable energy industry. However, the impact on renewable energy during the pandemic has not been consistently determined. Instead of relying on data from year-to-year comparisons, this study redesigned the analytical framework for assessing the impact of a pandemic on renewable energy. First, this research designed an "initial prediction-parameter training-error correction-assignment combination" forecasting approach to simulate renewable energy consumption in a "no pandemic" scenario. Second, this study calculates the difference between the "pandemic" and "no pandemic" scenarios for renewable energy consumption. This difference represents the change in renewable energy due to the COVID-19 pandemic. Various techniques such as nonlinear grey, artificial neural network and IOWGA operator were incorporated. The MAPEs were controlled to within 5% in 80% of the country samples. The conclusions indicated that renewable energy in China and India declined by 8.57 mtoe and 3.19 mtoe during COVID-19 period. In contrast, the rise in renewable energy in the US is overestimated by 8.01 mtoe. Overall, previous statistics based on year-to-year comparisons have led to optimistic estimates of renewable energy development during the pandemic. This study sheds light on the need for proactive policy measures in the future to counter the global low tide of renewable energy amid COVID-19.
根据联合国环境规划署的说法，新冠疫情给经济和能源部门带来了挑战，也给可再生能源行业带来了不确定性。然而，疫情期间对可再生能源的影响尚未得到一致确定。本研究并未依赖逐年比较的数据，而是重新设计了评估疫情对可再生能源影响的分析框架。首先，本研究设计了一种“初始预测-参数训练-误差校正-赋值组合”预测方法，以模拟“无疫情”情景下的可再生能源消费。其次，本研究计算了可再生能源消费的“疫情”情景和“无疫情”情景之间的差异。这一差异代表了新冠疫情导致的可再生能源变化。研究纳入了非线性灰色、人工神经网络和IOWGA算子等多种技术。在80%的国家样本中，平均绝对百分比误差（MAPEs）被控制在5%以内。结论表明，在新冠疫情期间，中国和印度的可再生能源分别下降了857万吨油当量和319万吨油当量。相比之下，美国可再生能源的增长被高估了801万吨油当量。总体而言，以往基于逐年比较的统计数据导致了对疫情期间可再生能源发展的乐观估计。本研究揭示了未来需要采取积极的政策措施，以应对新冠疫情期间可再生能源的全球低潮。
