Climate engineering management (CEM) as an emerging and cross-disciplinary subject gradually draws the attention to researchers. This paper aims to focus on economic and…
Climate engineering management (CEM) as an emerging and cross-disciplinary subject gradually draws the attention to researchers. This paper aims to focus on economic and social impacts on the technologies of climate engineering themselves. However, very few research concentrates on the management of climate engineering. Furthermore, scientific knowledge and a unified system of CEM are limited.
In this paper, the concept of CEM and its characteristics are proposed and elaborated. In addition, the framework of CEM is established based on management objectives, management processes and supporting theory and technology of management. Moreover, a multi-agent synergistic theory of CEM is put forward to guide efficient management of climate engineering, which is composed of time synergy, space synergy, and factor synergy. This theory is suitable for solving all problems encountered in the management of various climate engineering rather than a specific climate engineering. Specifically, the proposed CEM system aims to mitigate the impact of climate change via refining and summarizing the interrelationship of each component.
Overall, the six research frontiers and hotspots in the field of CEM are explored based on the current status of research.
In terms of the objectives listed above, this paper seeks to provide a reference for formulating the standards and norms in the management of various climate engineering, as well as contribute to policy implementation and efficient management.
Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030…
Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this paper aims to establish a cross-validation-based linear model selection system, which can consider many factors to avoid missing useful information and select the best model according to estimated out-of-sample forecast performances.
With the nine identified influencing factors of electricity demand, this system first determines the parameters in four alternative fitting procedures, where for each procedure a lot of cross-validation is performed and the most frequently selected value is determined. Then, through comparing the out-of-sample performances of the traditional multiple linear regression and the four selected alternative fitting procedures, the best model is selected in view of forecast accuracy and stability and used for forecasting under four scenarios. Besides the baseline scenario, this paper investigates lower and higher economic growth and higher consumption share.
The results show the following: China will consume 7,120.49 TWh, 9,080.38 TWh and 11,649.73 TWh of electricity in 2020, 2025 and 2030, respectively; there is hardly any possibility of decoupling between economic development level and electricity demand; and shifting China from an investment-driven economy to a consumption-driven economy is greatly beneficial to save electricity.
Following insights are obtained: reasonable infrastructure construction plans should be made for increasing electricity demand; increasing electricity demand further challenges China’s greenhouse gas reduction target; and the fact of increasing electricity demand should be taken into account for China’s prompting electrification policies.